WO2023034046A1

WO2023034046A1 - Antimicrobic susceptibility testing using machine learning and feature classes

Info

Publication number: WO2023034046A1
Application number: PCT/US2022/040986
Authority: WO
Inventors: Arman Garakani; Katherine SEI; Qi Chen; Michael Urban; Yemi ODEYEMI; Peng LAI
Original assignee: Beckman Coulter, Inc.
Priority date: 2021-08-31
Filing date: 2022-08-22
Publication date: 2023-03-09

Abstract

An optimized testing method is used to determine minimum inhibitory concentration (MIC) of a particular antimicrobic for use with a particular microbe. The testing method includes iteratively imaging the microbes mixed with various concentrations of the antimicrobic. The images are thereafter processed to identify curvilinear structures, which are bucketed and provided as input to a machine learning model such as a decision tree classifier. The output of the model would then make a prediction regarding the MIC, such as a prediction of growth or no growth for each well (in which case the lowest concentration in a well with a prediction of no growth may be treated as the MIC), or a prediction of the MIC directly.

Description

ANTIMICROBIC SUSCEPTIBILITY TESTING USING MACHINE LEARNING AND FEATURE CLASSES

BACKGROUND

[0001] Various types of tests related to patient diagnosis and therapy can be performed by analysis of the patient’s microorganisms, or “microbes.” Microbes are microscopic living organisms such as bacteria, fungi, or viruses, which may be single-celled or multicellular. Biological samples containing the patient's microorganisms may be taken from a patient's infections, bodily fluids or abscesses and may be placed in test panels or arrays, combined with various reagents, incubated, and analyzed to aid in treatment of the patient. Automated biochemical analyzers have been developed to meet the needs of health care facilities and other institutions to facilitate analysis of patient samples and to improve the accuracy and reliability of assay results when compared to analysis using manual operations and aid in determining effectiveness of various antimicrobials. An antimicrobial is an agent that kills microorganisms or inhibits their growth, such as antibiotics which are used against bacteria and antifungals which are used against fungi. However, with ever changing bacterial genera and newly discovered antimicrobials, the demand for biochemical testing has increased in both complexity and in volume.

[0002] An important family of automated microbiological analyzers function as a diagnostic tool for determining both the identity of an infecting microorganism and of an antimicrobic effective in controlling growth of the microorganism. Automated microbiological analyzers function as a diagnostic tool for determining both the identity of an infecting microorganism and of an antimicrobic effective in controlling growth of the microorganism. In performing the diagnostic tests, identification and in vitro antimicrobic susceptibility patterns of microorganisms isolated from biological samples are ascertained. Conventional versions of such analyzers may place a small sample to be tested into a plurality of small sample test wells in panels or arrays that contain different enzyme substrates or antimicrobics in serial dilutions. Identification (ID) testing of microorganisms, and antimicrobic susceptibility testing (AST) for determining Minimum Inhibitory Concentrations (MIC) of an antimicrobic effective against the microorganism may utilize color changes, fluorescence changes, the degree of cloudiness (turbidity) in the sample test wells created in the arrays, or other information derived from the testing. Both AST and ID measurements and subsequent analysis may be performed by computer controlled microbiological analyzers to provide advantages in reproducibility, reduction in processing time, avoidance of transcription errors and standardization for all tests run in the laboratory.

[0003] In ID testing of a microorganism, a standardized dilution of the patient's microorganism sample, known as an inoculum, is first prepared in order to provide a bacterial or cellular suspension having a predetermined known concentration. This inoculum is placed in a plurality of test wells that may contain or thereafter be supplied with predetermined test media. Depending on the species of microorganism present, this media will facilitate changes in color, turbidity, fluorescence, or other characteristics after incubation. These changes are used to identify the microorganism in ID testing.

[0004] In AST testing, a plurality of test wells are filled with inoculum and increasing concentrations of one or more antimicrobial agents, for example antibiotics. The antimicrobial agents may be diluted in a growth medium or liquid medium to concentrations that include those of clinical interest. After incubation, the turbidity will be increased or unchanged in test wells where growth has not been inhibited by the antimicrobics in those test wells. The MIC of each antimicrobial agent is measured by lack of growth with respect to each concentration of antimicrobial agent. It follows that the lowest concentration of antimicrobial agent displaying a lack of growth is the MIC.

SUMMARY

[0005] In accordance with a first aspect of the invention, there is provided a method comprising:

(a) creating a set of test mixtures in a set of test wells, wherein each test mixture from the set of test mixtures is inoculated using an antimicrobial solution comprising: (i) an antimicrobial agent; and

(ii) a biological sample; wherein, for each test well from the set of test wells, the antimicrobial solution used to inoculate that test well has a different concentration of the antimicrobial solution than any other test well from the set of test wells;

(b) incubating the set of test mixtures;

(c) capture a plurality of images by, at a plurality of imaging times, wherein each of the imaging times takes place after incubation has begun, for each test well from the set of test wells, capturing an image of that test well;

(d) determining a plurality of model inputs by, for each of the plurality of imaging times, for each of the set of test wells:

(i) identifying a plurality of microorganisms in that test well at that imaging time;

(ii) determining a set of characteristics for each microorganism identified in that test well at that imaging time;

(iii) organizing the identified microorganisms in that test well at that imaging time into a plurality of classes based on the set of characteristics determined for the identified microorganisms; and

(iv) for each class from the plurality of classes, treating a number of microorganisms in that class in that test well at that imaging time as a model input;

(e) providing the plurality of model inputs to a machine learning model; and

(f) determining a minimum inhibitory concentration of the antimicrobial agent based on an output of the machine learning model.

[0006] The biological sample may be a microorganism sample, referred to as an inoculum. The microorganisms may be microbes.

[0007] Optionally, for each of the plurality of imaging times and each of the set of test wells, the set of characteristics determined for each microorganism identified in that test well at that imaging time: (a) consists of a single characteristic; and

(b) the single characteristic may be length.

[0008] Optionally, the machine learning model may be an ensemble decision tree classifier.

[0009] Optionally:

(a) the machine learning model is adapted to provide, for each test well from the set of test wells, a prediction of growth or inhibition; and

(b) determining the minimum inhibitory concentration of the antimicrobial agent based on the output of the machine learning model comprises determining the minimum inhibitory concentration based on the concentration of the antimicrobial agent in a test well with a lowest concentration of the antimicrobial agent and a prediction of inhibition.

[00010] Optionally:

(a) the machine learning model is a regression model;

(b) the machine learning model is adapted to provide a concentration of the antimicrobial agent; and

(c) determining the minimum inhibitory concentration of the antimicrobial agent based on the output of the machine learning model comprises treating the concentration of the antimicrobial agent as the minimum inhibitory concentration.

[00011] Optionally, for each of the plurality of imaging times, for each of the set of test wells, identifying the plurality of microorganisms in that test well at that imaging time comprises:

(a) identifying a set of edges; and

(b) a set of circular segments; in the image of that test well captured at that imaging time.

[00012] Optionally, for each of the plurality of imaging times, for each of the set of test wells, identifying the set of edges comprises:

(a) applying a Gaussian kernel having values based on:

(i) an expected thickness of microorganisms identified in that test well at that imaging time; and

(ii) resolution of the image captured of that test well at that imaging time; and

(b) searching in an area surrounding pixels identified based on application of the Gaussian kernel, wherein the area is defined by the expected thickness of microorganisms identified in that test well at that imaging time.

[00013] Optionally, for each of the plurality of imaging times, for each of the set of test wells, identifying the set of circular segments comprises:

(a) identifying, for each point comprised by the set of edges, a center of motion for that point; and

(b) for each point comprised by the set of edges, comparing a threshold with a result obtained by dividing the center of motion for that point by an average of all points comprised by the set of edges.

[00014] Optionally the antimicrobial agent is selected from a group consisting of:

(a) cefepime;

(b) ceftazidime; and

(c) ceftriaxone.

[00015] Optionally, the biological sample comprises microorganisms selected from a group consisting of:

(a) E. cloacae,'

(b) E. co!i,

(c) K. pneumoniae,' and

(d) P. mirabilis. [00016] In accordance with a second aspect of the invention, there is provided a biological testing system comprising a processor configured with a set of computer instructions operable, when executed, to cause the system to perform a method comprising:

(a) creating a set of test mixtures in a set of test wells, wherein each test mixture from the set of test mixtures is inoculated using an antimicrobial solution comprising:

(i) an antimicrobial agent; and

(b) incubating the set of test mixtures;

(e) providing the plurality of model inputs to a machine learning model; and (f) determining a minimum inhibitory concentration of the antimicrobial agent based on an output of the machine learning model.

[00017] Optionally, for each of the plurality of imaging times and each of the set of test wells, the set of characteristics determined for each microorganism identified in that test well at that imaging time:

(a) consists of a single characteristic; and

(b) the single characteristic is length.

[00018] Optionally, the machine learning model is an ensemble decision tree classifier.

[00019] Optionally:

[00020] Optionally:

(a) the machine learning model is a regression model;

[00021] Optionally, for each of the plurality of imaging times, for each of the set of test wells, identifying the plurality of microorganisms in that test well at that imaging time comprises:

(a) identifying a set of edges; and

[00022] Optionally, for each of the plurality of imaging times, for each of the set of test wells, identifying the set of edges comprises:

(a) applying a Gaussian kernel having values based on: (i) an expected thickness of microorganisms identified in that test well at that imaging time; and (ii) resolution of the image captured of that test well at that imaging time; and

[00023] Optionally, for each of the plurality of imaging times, for each of the set of test wells, identifying the set of circular segments comprises:

[00024] Optionally, the antimicrobial agent is selected from a group consisting of:

(a) cefepime;

(b) ceftazidime; and

(c) ceftriaxone.

[00025] In accordance with a third aspect of the invention, there is provided a biological testing system comprising:

(a) a digital camera;

(b) a plurality of wells; and

(c) a means adapted for determining minimum inhibitory concentration of an antimicrobial based on numbers of items in classes populated using data extracted from images of the plurality of wells captured by the digital camera.

Optionally, the means for determining minimum inhibitory concentration of an antimicrobial based on numbers of items in classes populated using data extracted from images of the plurality of wells captured by the digital camera uses the method of the first aspect of the invention described above. The means for determining minimum inhibitory concentration of an antimicrobial based on numbers of items in classes populated using data extracted from images of the plurality of wells captured by the digital camera may be the processor of the second aspect described above. The processor may configured with a set of computer instructions operable, when executed, to cause the system to perform the method of the first aspect described above.

BRIEF DESCRIPTION OF THE DRAWINGS

[00026] While the specification concludes with claims which particularly point out and distinctly claim the invention, it is believed the present invention will be better understood from the following description of certain examples taken in conjunction with the accompanying drawings, in which like reference numerals identify the same elements and in which:

[00027] FIG. 1A depicts a portion of a diagrammatic view of an exemplary biological testing system;

[00028] FIG. IB depicts another portion of the diagrammatic view of the biological testing system of FIG. 1A; [00029] FIG. 2 depicts a perspective view of an exemplary incubator system and an exemplary optics system of the biological testing system of FIG. IB;

[00030] FIG. 3 depicts a perspective view of the optics system of FIG. 2;

[00031] FIG. 4 depicts another perspective view of the optics system of FIG. 2 showing an XY stage of the optics system;

[00032] FIG. 5 depicts a diagrammatic view of portions of the optics system of FIG. 2;

[00033] FIG. 6 depicts a diagrammatic view of an exemplary computer system;

[00034] FIG. 7 depicts a chart of theoretical growth curves for a microbe;

[00035] FIG. 8 depicts a schematic view of an exemplary growth test well for use in the biological testing system of FIGS. 1A and IB;

[00036] FIG. 9 depicts an exemplary image analysis cycle for use in the biological testing system of FIGS. 1A and IB;

[00037] FIG. 10 depicts an exemplary raw image captured by the optics system of FIG. 2;

[00038] FIG. 11 depicts an exemplary method which may be used to identify curvilinear structures.

[00039] FIG. 12 depicts an exemplary method for linking pixels; and

[00040] FIG. 13 depicts an exemplary optimized antimicrobic susceptibility testing method of the present invention.

[00041] The drawings are not intended to be limiting in any way, and it is contemplated that various embodiments of the invention may be carried out in a variety of other ways, including those not necessarily depicted in the drawings. The accompanying drawings incorporated in and forming a part of the specification illustrate several aspects of the present invention, and together with the description serve to explain the principles of the invention; it being understood, however, that this invention is not limited to the precise arrangements shown. DETAILED DESCRIPTION

[00042] The following description of certain examples of the invention should not be used to limit the scope of the present invention. Other examples, features, aspects, embodiments, and advantages of the invention will become apparent to those skilled in the art from the following description, which is by way of illustration, one of the best modes contemplated for carrying out the invention. As will be realized, the invention is capable of other different and obvious aspects, all without departing from the invention. Accordingly, the drawings and descriptions should be regarded as illustrative in nature and not restrictive.

[00043] It will be appreciated that any one or more of the teachings, expressions, versions, examples, etc. described herein may be combined with any one or more of the other teachings, expressions, versions, examples, etc. that are described herein. The following- described teachings, expressions, versions, examples, etc. should therefore not be viewed in isolation relative to each other. Various suitable ways in which the teachings herein may be combined will be readily apparent to those of ordinary skill in the art in view of the teachings herein. Such modifications and variations are intended to be included within the scope of the claims.

[00044] I. Biological Testing System Hardware

[00045] FIGS. 1A and IB depict a diagrammatical example of various hardware components available in a biological testing system 1. Biological testing system 1 facilitates an optimized antimicrobial susceptibility testing (AST) method 101 (FIG. 13). Biological testing system 1 broadly includes a consumable preparation system 3, an inoculating system 5, an incubator system 7, and an optics system 9. The various systems within biological testing system 1 coordinate with each other and work automatically once loaded with adequate material by a user.

[00046] To operate biological testing system 1, the user first acquires an appropriate microbe sample. As shown in FIG. 1 A, a microbe sample may be obtained from an agar plate 11 or, under certain circumstances, from a blood sample. Next, the user prepares an inoculum suspension by transferring the microbes into a tube containing a suitable liquid medium or broth. One such tube is shown in FIG. 1A as an inoculum 13. In some versions of biological testing system 1, the liquid medium or broth may be an approximately 0.5 mM phosphate buffered solution with small amounts of sodium and potassium chloride to aid in maintaining the viability of the microbes introduced into solution without adversely interfering with the MIC determination or other associated testing. Other types of liquid medium, such as nutrient broths containing amino acids, salts, and a buffer may also be used. Such a liquid medium may be used in both ID testing and AST testing to minimize the need for different inoculum across the two systems and leverage the efficiencies in having a single broth. Each inoculum 13 is placed into an inoculum rack 15 and the entire inoculum rack 15 is placed into inoculating system 5. Once in inoculating system 5, the inoculum in each inoculum 13 is adjusted if necessary to a standard turbidity value of 0.5 McFarland to create an inoculum 17. In some versions of biological testing system 1, a 1 microliter plastic loop or swab may be provided to the user to easily pick colonies from the agar plate and to minimize the amount of adjustment needed to bring the inoculum to the desired turbidity value. Once adjusted to the desired turbidity value, the inoculum is finalized. The finalized inoculum will be referred to hereinafter as inoculum 17, as depicted in FIG. IB. Inoculum 17 may be further diluted into a 1 :250 dilution and converted into an inoculum 18. As will be discussed in more detail below, the inoculum contained in each inoculum 17 is applied to an identification (ID) array holder 21, while the inoculum contained in each inoculum 18 is applied to an AST array holder 23. Both ID array holder 21 and AST array holder 23 are assembled by consumable preparation system 3 and provided to inoculating system 5 for use with inoculum 17 and inoculum 18.

[00047] Consumable preparation system 3 is loaded with magazines of test arrays 19, which may contain various antimicrobials or other agents required by biological testing system 1 disposed in a series of test wells 20. For example, test array 19 may comprise an antimicrobic dilution array or an identification array. Consumable preparation system 3 may also be loaded with bulk diluents (not shown) and/or various other elements for preparing and finalizing ID array holder 21 and AST array holder 23 and the inoculate therein. Primarily, consumable preparation system 3 operates to retrieve test arrays 19 as required and combine each retrieved test array 19 into either ID array holder 21 or AST array holder 23. Test arrays 19 may be selected and assembled by a robotic gripper (not shown) or other mechanical features as dictated by the prescribed testing. For example, a physician may order biological testing using the antibiotic amoxicillin. Test arrays 19 relating to amoxicillin testing are therefore retrieved and assembled into the appropriate ID array holder 21 and AST array holder 23. All or some portions of test array 19 may be formed of a styrene material to aid in reducing fluorescent crosstalk, fallout, and/or bubbles when digitally examining each test well 20.

[00048] Once inoculum 17, inoculum 18, ID array holder 21, and AST array holder 23 are assembled, inoculating system 5 dispenses the generally undiluted inoculum from inoculum 17 into test wells 20 of ID array holder 21 and the diluted inoculum from inoculum 18 into test wells 20 of AST array holder 23. The time between applying inoculum 17 to ID array holder 21 or inoculum 18 to AST array holder 23 and the start of logarithmic growth of the microbes disposed therein is known as “lag time.” Lag time may be decreased by using enhanced broth such as a broth with yeast extract, vitamins, and/or minerals. Lag time may also be decreased by increasing the inoculum. In some versions of biological testing system 1, the amount of inoculum may be doubled to decrease the lag time by approximately 30 minutes without affecting the accuracy of the MIC determination. The dispensing may be accomplished via an elevator assembly 26 having an XY robot or XYZ robot (not shown) with a gripper (not shown) and pipettor (not shown), along with various circuitry, channels, and tubing as necessary. The XYZ robot is tasked with retrieving inoculum from inoculum racks 15 and dispensing the inoculum into test wells 20 of ID array holder 21 and AST array holder 23. Once ID array holder 21 and AST array holder 23 are sufficiently loaded with inoculum, each ID array holder 21 and AST array holder 23 are moved into incubator system 7 by way of an elevator assembly

26. [00049] As shown in FIG. 2, incubator system 7 includes slots 27 for holding a large number of ID array holders 21 and AST array holders 23. Each array holder is placed into a corresponding slot 27 by an XYZ robot 29 using a gripper 31. XYZ robot 29 operates to move in any portion of the XYZ plane and position gripper 31 proximate the desired ID array holder 21 or AST array holder 23. While in incubator system 7, each array holder incubates in specific desired environmental conditions. For example, incubator system 7 may be set to incubate array holders at thirty-five degrees Celsius. At certain time intervals during the incubation, XYZ robot 29 retrieves a particular ID array holder 21 or AST array holder 23 and move the selected array holder into the optics system 9.

[00050] As shown in FIG. 2-4, optics system 9 includes features that are configured to observe, monitor, review, and/or capture images for each test well 20 of an ID array holder 21 or AST array holder 23. Specifically, each ID array holder 21 is monitored by an ID fluorimeter 33, which each AST array holder 23 is monitored by an AST camera 35. To accomplish the monitoring, XYZ robot 29 retrieves the particular array holder with gripper 31 and places the selected array holder onto an XY-stage 37. The XY-stage 37 moves in the XY plane to position the array holder under the associated monitoring element, namely, the ID array holders 21 are disposed under the ID fluorimeter 33 and the AST array holders 23 are disposed under the AST camera 35 for monitoring and observation in optics system 9. XY-stage 37 includes finely tuned motor control to allow each test well 20 of the associated array holder to be positioned accurately within the observation frame of either ID fluorimeter 33 or AST camera 35.

[00051] FIG. 5 illustrates an exemplary architecture for an AST optics portion 39 of optics system 9. AST optics portion 39 includes an illumination source 41, an objective lens 43, a tube lens 45, and a fold mirror 47. Illumination source 41 may comprise a condenser LED system for providing monochromatic illumination of each test well 20 of AST array holder 23. Objective lens 43 may comprise a Nikon 20x 0.45NA ELWD objective lens, an Olympus 10X objective lens or any other suitable kind of lens. Objective lens 43 may comprise a 20x objective lens with each pixel covering about 0.33 microns. A 20x objective lens provides both the ability to detect a reasonable number of cells at the beginning of cell growth (around 100-200 cells) and the ability to detect cell morphology. Objective lens 43 may comprise a lOx objective lens and/or a 5MP camera for a larger dynamic range and/or a bigger sample of each test well 20) while maintaining enough resolution to count the microbes therein. In some exemplary embodiments of optics system 9, only one picture or image per test well 20 per pass is acquired by objective lens 43. Objective lens 43 may focus slightly off the bottom of test well 20 to eliminate background noise from the bottom of test well 20. In some versions of optics system 9, objective lens 43 is configured to focus approximately 5-10 microns from the bottom of test well 20. In some versions of optics system 9, objective lens 43 is configured to focus 8 microns from the bottom of test well 20. Objective lens 43 may also include a Z-stage 44 for allowing objective lens 43 to move in the Z-axis, relative to XY-stage 37. Thus, between XY-stage 37 moving AST array holder 23 in the XY plane and Z-stage 44 moving objective lens 43 in the Z-axis, each test well 20 of array holder 23 may be moved in any three-dimensional space to precisely align test wells 20 with the frame of AST camera 35. Tube lens 45 may be embodied in an achromatic tube lens. AST camera 35 may comprise a Sony IMX253 camera, various types of 5 megapixel cameras provided by other manufacturers such as Canon, Thorlabs, or Sentech, or any other suitable kind of camera. In some versions of optics system 9, XY-stage 37 and Z-stage 44 are replaced with an XYZ-stage to provide all three axes of three-dimensional movement of test wells 20.

[00052] Referring now to FIG. 6, the various components of biological testing system 1 may incorporate one or more computing devices or systems, such as exemplary computer system 49. For example, any one of consumable preparation system 3, inoculating system 5, incubator system 7, and/or optics system 9 may incorporate one or more computing systems such as exemplary computer system 49. Alternatively, each of these subsystems of biological testing system 1 may function via commands from one overall computing system such as exemplary computer system 49.

[00053] Computer system 49 may include a processor 51, a memory 53, a mass storage memory device 55, an input/output (I/O) interface 57, and a Human Machine Interface (HMI) 59. Computer system 49 may also be operatively coupled to one or more external resources 61 via a network 63 or I/O interface 57. External resources may include, but are not limited to, servers, databases, mass storage devices, peripheral devices, cloud-based network services, or any other suitable computer resource that may used by computer system 49.

[00054] Processor 51 may include one or more devices selected from microprocessors, micro-controllers, digital signal processors, microcomputers, central processing units, field programmable gate arrays, programmable logic devices, state machines, logic circuits, analog circuits, digital circuits, or any other devices that manipulate signals (analog or digital) based on operational instructions that are stored in memory 53. Memory 53 may include a single memory device or a plurality of memory devices including, but not limited, to read-only memory (ROM), random access memory (RAM), volatile memory, nonvolatile memory, static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, cache memory, or any other device capable of storing information. Mass storage memory device 55 may include data storage devices such as a hard drive, optical drive, tape drive, non-volatile solid state device, or any other device capable of storing information.

[00055] Processor 51 may operate under the control of an operating system 65 that resides in memory 53. Operating system 65 may manage computer resources so that computer program code embodied as one or more computer software applications, such as an application 67 residing in memory 53, may have instructions executed by the processor 51. In an alternative embodiment, processor 51 may execute application 67 directly, in which case the operating system 65 may be omitted. One or more data structures 69 may also reside in memory 53, and may be used by processor 51, operating system 65, or application 67 to store or manipulate data.

[00056] The VO interface 57 may provide a machine interface that operatively couples processor 51 to other devices and systems, such as network 63 or external resource 61. Application 67 may thereby work cooperatively with network 63 or external resource 61 by communicating via I/O interface 57 to provide the various features, functions, applications, processes, or modules comprising embodiments of the invention. Application 67 may also have program code that is executed by one or more external resources 61, or otherwise rely on functions or signals provided by other system or network components external to computer system 49. Indeed, given the nearly endless hardware and software configurations possible, persons having ordinary skill in the art will understand that different versions of the invention may include applications that are located externally to computer system 49, distributed among multiple computers or other external resources 61, or provided by computing resources (hardware and software) that are provided as a service over network 63, such as a cloud computing service.

[00057] HMI 59 may be operatively coupled to processor 51 of computer system 49 in a known manner to allow a user to interact directly with the computer system 49. HMI 59 may include video or alphanumeric displays, a touch screen, a speaker, and any other suitable audio and visual indicators capable of providing data to the user. HMI 59 may also include input devices and controls such as an alphanumeric keyboard, a pointing device, keypads, pushbuttons, control knobs, microphones, etc., capable of accepting commands or input from the user and transmitting the entered input to the processor 51.

[00058] A database 71 may reside on mass storage memory device 55, and may be used to collect and organize data used by the various systems and modules described herein. Database 71 may include data and supporting data structures that store and organize the data. In particular, database 71 may be arranged with any database organization or structure including, but not limited to, a relational database, a hierarchical database, a network database, or combinations thereof. A database management system in the form of a computer software application executing as instructions on processor 51 may be used to access the information or data stored in records of the database 71 in response to a query, where a query may be dynamically determined and executed by operating system 65, other applications 67, or one or more modules. [00059] II. Optimized AST System and Method

[00060] In some versions, system 1 as discussed above may be used to facilitate some or all of the features provided in optimized AST method 101 such as shown in FIG. 13.

[00061] In some conventional processes, MIC is determined through manual visual inspection of test wells after waiting a period to allow the microbes to grow. However, as shown in FIG. 7, microbial sample growth within the test wells is not observable with the naked eye until between four to ten hours after the inoculum is disposed in the test wells. Traditional methods of determining MIC are limited by what a human can visually perceive relative to the growth of the microbes within the test wells. Further, the presence of antimicrobic dilution concentrations that are below the MIC concentration may slow the growth rate, and take even longer to perceive. As shown in FIG. 7, the first six to seven doublings of the microbial sample cannot be observed visually by the human eye. However, the information provided through these initial doublings is often indicative of the MIC. As shown in FIGS. 7-13 optimized AST method 101 utilizes digital microscopy to monitor growth rate from the point of inoculation in the test well and thus allows for more rapid and accurate detection of the MIC. In addition, specimen morphology information, such as the size and shape of the microbe, can be used to enhance the accuracy of the MIC determination. For example, specimen morphology information such as elongation may be used to enhance the accuracy of the MIC determination.

[00062] FIG. 8 provides an illustration of how data that can be used in an optimized AST method 101 can be captured from a test well. Test well 20 is embodied by a “384” style test well, having a clear viewing bottom. The volume of inoculum in test well 20 may be set to 20 microliters to reduce the amount of materials such as bulk diluents required by AST method 101 and/or minimize light artifacts and provide sampling of a consistent number of microbes from the 1 :250 dilution of the 0.5 MacFarland inoculum. Decreasing the volume of inoculum in test well 20 generally increases the light artifacts. In some instances, capturing a single vertical plane 103 at 20x objective whereby AST camera 35 is set at 0.33 microns/pixel may be sufficient for sampling the inoculum and ensuring that each individual microbe is recognizable. However, these parameters are configurable and may change as desired by the user or the underlying needs of the system. Capturing three focal sites spaced about 5 microns apart within single vertical plane 103 may also provide a sufficient number of microbes in the sample to count for use in optimized AST method 101. These three focal sites are labeled site 105, site 107, and site 109 in FIG. 8. In an exemplary version of optimized AST method 101, each focal site is approximately 700 x 700 microns within single vertical plane 103, rather than capturing three sites in three different vertical planes. AST camera 35, optics system 9, and computer 49 are configured to capture an image of each focal site in consecutive time periods, manipulate each of these images, and thereafter use the data derived from these manipulated images to make a MIC determination.

[00063] Image analysis cycle 102 is generally depicted in FIG. 9 and comprises an image capture step 111 and a data extraction step 117. Optimized AST method 101 may include performing image analysis cycle 102 repetitively until a MIC is determined. Image analysis cycle 102 utilizes one or more instances of computer 49 and the various elements thereof to perform image capture step 111 and data extraction step 117, as well as any sub steps provided therein.

[00064] Image analysis cycle 102 begins with image capture step 111 captures a raw image 119 (FIG. 10) of the inoculum via AST camera 35 of optics system 9 and stores raw image 119 in memory 53. While raw image 119 is depicted as a single image, raw image 119 may be a composite of several separate images taken in vertical plane 103, for example, a composite of site 105, site 107, and site 109. Raw image 119 may also be a composite of several images taken in different planes within the inoculum sample or may be a single image. As illustrated in FIG. 10, raw image 119 may include deficiencies such as uneven illumination. Uneven illumination may be a result of the meniscus created by the inoculum in combination with the walls of the associated test well 20, which may affect the path of illumination source 41. Uneven background intensity may also be caused by environmental issues such as plastic deformation or from a variety of other sources. After raw image 119 is captured in image capture step 111, the process proceeds to the data extraction step 117.

[00065] In data extraction step 117, a captured image is analyzed to identify curvilinear structures showing the number and length of microbes in a sample. An exemplary method which may be used for this purpose is shown in FIG. 11. Initially, in the method of FIG. 11, the lines present in an image would be identified 1701. This may be done, for example, by applying the second derivative of a Gaussian smoothing kernel to the image and treating pixels whose second derivatives are local maxima as lines running through the imaged microorganisms. In some cases, this second derivative Gaussian may be defined using a standard deviation based on the microscopy magnification and expected thickness of elongated cells, through the formula shown in equation 1 :

> ((width/resolution)/2)

° “ 3^0,5

Equation 1: Exemplary standard deviation calculation

For example, in a case where an elongating microorganism is expected to be about 2 pm or more in width (e.g., as would be true for E. colt) and the optical resolution of the image being analyzed is about 0.349 pm/pixel, the standard deviation for the Gaussian could be calculated as o = (2 / 0.349 / 2)/3^{0 5} = 2.86/3° ⁵ = 1.65. Once these lines have been identified, the edges of the microorganisms may be identified 1702 by, for each pixel in each line, searching for a distance equal to the expected thickness of the microorganism along a line tangent to that pixel for a gradient indicative of an edge (e.g., a gradient above a threshold).

[00066] Variations on this approach are also possible. For example, in some cases, a 2D edge operator (e.g., a Sobel filter) may be run to generate magnitude and direction of the first derivative at every pixel point, and the pixels whose first derivatives are local maxima may be treated as making up lines running through microorganisms. Additionally, in some cases, line position may be calculated with sub-pixel accuracy, for example, by taking every pixel identified as part of a line and then calculating a center of mass of the first derivative magnitude values of a 3x3 square centered on that pixel. Accordingly, the preceding description of how lines may be identified 1701 should be understood as being illustrative only, and should not be treated as limiting.

[00067] In the method of FIG. 11, after the edges of the various depicted microorganisms have been identified 1702, similarly directed edge pixels may be linked 1703. This may be done, for example, using a method such as shown in FIG. 12. As shown in FIG. 12, this type of method may start with selecting 1801 a starting point for linking. This may be done, for example, by identifying the edge pixel with the maximum second derivative value that has not already been linked with another edge pixel. After this staring point has been selected 1801, the method of FIG. 12 may continue by identifying 1802 a set of candidate pixels that it may be linked to. This may be done, for example, by choosing a direction of travel along the edge, and identifying the neighboring pixels in that direction of travel whose second derivatives are above some configurable threshold. For example, if the pixel currently being linked is (c_x, c_y), and the direction of travel along the edge is between positive and negative 22.5°, then the pixels (c_x+i, Cy-i), (c_x+i, c_y) and (c_x+i, c_y+i) may be identified 1802 as linking candidates if their second derivatives were sufficient.

[00068] If there are no link candidates (e.g., if no neighbors in the direction of travel along the edge had sufficient second derivatives) then, if the linking had only been performed along one direction of travel, the process may return to the starting point and identify link candidates 1802 in the other direction of travel. Alternatively, if there were linking candidates, the method of FIG. 12 may determine 1803 which of the link candidates is the best. This may be done, for example, by identifying the link candidate whose normal is closest to parallel to the normal of the pixel it would be linked to. If this link candidate had already been linked to another edge pixel, then it may be treated as a junction 1804, and the method may proceed as if no link candidates had been identified. Otherwise, the method of FIG. 12 may continue by linking 1805 the best candidate, and then iterating to determine if any of that pixel’s neighbors should be linked to it. This may be continued until each pixel having a sufficient second derivative (which may be determined using a higher threshold than determining if a pixel should be linked) had either been linked to another pixel, at which point the linking 1703 could be deemed complete and the process could end 1806.

[00069] Once the edge pixels have been linked 1703, any missing pixels can be filled in 1704. This may include identifying the missing pixels, such as by identifying if an edge created based on a line has missing points at a certain location in the line, but there are edge points for that line on either side of the missing points. Once these missing pixels have been detected, they may be filled in by interpolation from the neighboring points on the edge which are available.

[00070] The filling in 1704 of missing pixels may be followed by detection 1705 of short circular segments. This may be done, for example, by checking the distance between the arithmetic center of edge points from the point that has the total minimal distance to every edge point along the normal to the edge point gradient angle. To illustrate, let Px and Py represent the x and y coordinates of an edge point, and u and v be the unit vector components of the unit vector representing the normal to the gradient direction at the edge point. Using these values, a center of motion (CoM) can be calculated for that point using equations 2-12 below. r = Px * u + Py * v

Equation 2

Suu = £u*u

Equation 3

Svv = £v*v

Equation 4

Suv = £u*v

Equation 5

Svu = £v*u

Equation 6

Sur = u*r Equation 7

Svr = v*r

Equation 8

D = Suu*Svy - Suv*Svu

Equation 9

CoMx = (Sur * Svv - Suv * Svr) / D

Equation 10

CoMy = (Svr * Suu - Suv * Sur) / D

Equation 11

CoM = (CoMx,CoMy)

Equation 12

This center of motion may be used to identify short, circular segments, since the distance between the CoM for a point on such a segment and the average of x and y values for all edge points (i.e., (£Px / Number of Edge Points, Py / Number of Edge Points) for all edge points) will be minimal, and a threshold value (e.g., 3) can be used to segregate these points from points not on a short circular segment.

[00071] Next, the information determined in the preceding steps may be used to identify 1706 features in the image. For example, in some cases, each previously identified 1701 line segment greater than a threshold length (e.g., five pixels) could be treated as a microorganism. Each of these microorganisms may then be assigned an ID and a length value, and a data structure may be created that stores the edge points associated with it. Other approaches to detecting curvilinear features during data extraction 117 may also be used. For example, in some cases, data extraction 117 may utilize curvilinear feature detection as described in Steger, C., 1998. An unbiased detector of curvilinear structures. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(2), pp.113-125, which document is incorporated by reference herein in its entirety. Accordingly, the description of feature detection in the context of FIG. 11 should be understood as being illustrative only, and should not be treated as limiting. [00072] Once data extraction step 117 derives the desired information from the enhanced image, image analysis cycle 102 terminates. Optimized AST method 101 iteratively performs image analysis cycle 102 at set time intervals to determine how the microbes in each test well 20 are changing and reacting to the particular antimicrobic dilution pairing. Further, optimized AST method 101 iteratively performs image analysis cycle 102 on each test well 20 associated with the microbes to determine how the microbes are reacting to each concentration of the antimicrobic dilution. For example, presume the microbes being tested are E. coli bacteria and three test wells 20 are being tested, with each test well 20 having a 20-microliter solution therein. The first test well 20 may contain an antimicrobic dilution of 1 microgram per milliliter (mcg/ml), the second test well 20 may contain an antimicrobic dilution of 2 mcg/ml, and the third test well 20 may contain an antimicrobic dilution of 4 mcg/ml. Optimized AST method 101 performs image analysis cycle 102 on each of the three test wells at each set time interval to determine (a) how each antimicrobic dilution is affecting the microbes; and (b) how each antimicrobic dilution is performing relative to the other antimicrobic dilutions. If the data indicates the 1 mcg/ml antimicrobic dilution is as effective as the 2 and 4 mcg/ml antimicrobic dilutions at neutralizing the microbes, the 1 mcg/ml antimicrobic dilution is the MIC.

[00073] An exemplary version of optimized AST method 101 is illustrated in FIG. 13 and begins with a step 155. In step 155, the system waits for a set time period threshold to allow the microbes within the selected test well 20 enough time to provide new information regarding the growth rate or reaction to the particular antimicrobic dilution. Optimized AST method 101 may preferably be configured to utilize a time period of 30 minutes for this threshold, though different thresholds may be used in different embodiments and/or with different microorganisms. For example, some bacteria or yeast or other microbes may react very quickly and provide information relevant to making an MIC determination within an hour. In this scenario, optimized AST method 101 may be configured to perform image analysis cycle 102 every five minutes to capture data regarding the rapidly changing environment within test wells 20. Other microbes may react relatively slowly to antimicrobic dilutions, and therefore a time period threshold of one hour may be more appropriate. Once step 155 waits the specified time period threshold, step 155 moves to a step 157.

[00074] In step 157, one iteration of image analysis cycle 102 is performed on a particular microbe with a selected test well 20. As discussed above, an iteration of image analysis cycle 102 derives data regarding the growth rate of the microbes within the selected test well 20. After an iteration of image analysis cycle 102 is performed, step 157 moves to a step 159. In step 159, the data collected in step 157 is stored and/or updated in memory, which may be in the form of a database, a flat file, or any other similar memory or storage device. In some embodiments of optimized AST method 101, step 159 stores the data collected in step 157 in database 71 (FIG. 6). Once step 159 stores/updates the collected data, step 159 moves to a step 161.

[00075] In step 161, optimized AST method 101 determines whether enough data has been collected to determine a MIC. This could be done, for example, by determining whether the data collected regarding the test well matched the data used to train a machine learning classifier used in determining a MIC. If more data is needed to accurately determine a MIC, step 161 returns to step 155 and waits to perform another image analysis cycle 102 to collect more data at a future time interval. If step 161 determines a sufficient amount of data has been collected, step 161 proceeds to a step 163.

[00076] In step 163, the MIC is determined. The determination is based on the data collected during each iteration of image analysis cycle 102 for all of the antimicrobic dilutions for a microbial sample as well as for a control well where no antimicrobial agent was present. As described in the following section, this determination may involve applying the collected data to a machine learning classifier that had previously been trained to make growth predictions and then determining the MIC based on which of the test wells was predicted to exhibit growth or to be inhibited.

[00077] III. Machine Learning for Determining Minimum Inhibitory Concentration (MIC) [00078] Information such as may be extracted from a series of images using processes such as described in the context of FIGS. 9 and 13 may be converted into parameters that can be used to train machine learning algorithms for making MIC determinations. For example, in some cases, elongation data from each of the captured images may be used to group bacteria identified in an image into classes based on length. This may be done by applying a normalizing function (e.g., a hyperbolic function or a logarithmic function) to the lengths of the identified microorganisms. The microorganisms with very small normalized lengths (e.g., circular microorganisms) or very large normalized lengths (e.g., having a length more than 3 times the length of a normal microorganism, or some other length indicating an abnormal growth pattern) may be placed in boundary classes, and the remaining lengths may be split linearly into a set of classes between the very small and very large normalized lengths. The counts of bacteria in each of these classes in each imaging cycle may then be used as parameters for training a machine learning model, such as a decision tree classifier to make a prediction of whether a particular well would or would not show growth after 16 hours (e.g., by classifying the well the images were taken on as (G) or (I)), or for predicting MIC based on the information gathered from a particular well (e.g., by classifying the data into a class taken from a set of classes representing potential MICs, or by utilizing regression analysis).

[00079] To illustrate how this may be performed, consider the case of an ensemble decision tree. During testing, training data for such a model was derived by extracting curvilinear objects for each of a set of test wells from nine imaging cycles, the first of which took place 60 minutes after inoculation, and in which each subsequent cycle was separated from the preceding cycle by 30 minutes. These objects were then organized into 8 classes based on length, and the numbers of objects in each class for each well at each imaging cycle were used as parameters for the classifier. By using the XG Boost regression algorithm with this training data, ensembles of decision trees were created which made predictions of MIC after 4-6 hours based on data collected from wells inoculated with various microorganisms and dilutions of cefepime from 0.25 to 16 (pg/ml). The results of this testing are shown in below in table 1.

Further testing showed that this approach can also be applied to other microorganism and antimicrobial combinations as well, as shown below in table 2.

Table 2: Test results with additional microorganisms and antimicrobials

It will be apparent that other types of ensembles, such as ensembles that would predict whether a particular well would exhibit growth (G) or inhibition (I) after 16 hours, rather than ensembles which would make specific MIC predictions are also possible, and could be implemented using the same types of approaches described above. [00080] Other types of decision tree classifiers, such as decision trees created using the QUEST (Quick, Unbiased and Efficient Statistical Tree) or CHAID (Chi-squared Automatic Interaction Detector) algorithms, as well as non-decision tree classifiers, such as neural networks or Bayesian classifiers, may be created in a similar manner. Similarly, classifiers trained using extracted and classified elongation information may be used for making MIC predictions for other types of microorganisms and antimicrobials, since excessive elongation is a common precursor to cell death and is therefore broadly indicative of effectiveness. Additionally, in some cases parameters other than elongation may be used, either in addition to, or in combination with, elongation as described above. For example, in some cases, rather than classifying microorganisms in images on only one dimensions (e.g., elongation), microorganisms may be classified on multiple dimensions (e.g., elongation and direction; elongation and straightness; straightness and direction; elongation, straightness and direction, etc.). In some cases, this type of classification on multiple parameters may be used to create a density heatmap for the microorganisms in each well at each imaging cycle, and the values for the classes making up the density heatmap may be provided as input to a classifier. Accordingly, the particular classes, microbes, parameters and models used in testing should be understood as being illustrative only, and should not be treated as implying limitations on the protection provided by this, or any related, document.

[00081] IV. Exemplary Combinations

[00082] The following examples relate to various non-exhaustive ways in which the teachings herein may be combined or applied. It should be understood that the following examples are not intended to restrict the coverage of any claims that may be presented at any time in this application or in subsequent filings of this application. No disclaimer is intended. The following examples are being provided for nothing more than merely illustrative purposes. It is contemplated that the various teachings herein may be arranged and applied in numerous other ways. It is also contemplated that some variations may omit certain features referred to in the below examples. Therefore, none of the aspects or features referred to below should be deemed critical unless otherwise explicitly indicated as such at a later date by the inventors or by a successor in interest to the inventors. If any claims are presented in this application or in subsequent filings related to this application that include additional features beyond those referred to below, those additional features shall not be presumed to have been added for any reason relating to patentability.

[00083] Example 1

[00084] A method comprising: (a) creating a set of test mixtures in a set of test wells, wherein each test mixture from the set of test mixtures is inoculated using an antimicrobial solution comprising: (i) an antimicrobial agent; and (ii) a biological sample; wherein, for each test well from the set of test wells, the antimicrobial solution used to inoculate that test well has a different concentration of the antimicrobial solution than any other test well from the set of test wells; (b) incubating the set of test mixtures; (c) capture a plurality of images by, at a plurality of imaging times, wherein each of the imaging times takes place after incubation has begun, for each test well from the set of test wells, capturing an image of that test well; (d) determining a plurality of model inputs by, for each of the plurality of imaging times, for each of the set of test wells: (i) identifying a plurality of microorganisms in that test well at that imaging time; (ii) determining a set of characteristics for each microorganism identified in that test well at that imaging time; (iii) organizing the identified microorganisms in that test well at that imaging time into a plurality of classes based on the set of characteristics determined for the identified microorganisms; and (iv) for each class from the plurality of classes, treating a number of microorganisms in that class in that test well at that imaging time as a model input; (e) providing the plurality of model inputs to a machine learning model; and (f) determining a minimum inhibitory concentration of the antimicrobial agent based on an output of the machine learning model.

[00085] Example 2

[00086] The method of example 1, wherein, for each of the plurality of imaging times and each of the set of test wells, the set of characteristics determined for each microorganism identified in that test well at that imaging time: (a) consists of a single characteristic; and (b) the single characteristic is length.

[00087] Example 3

[00088] The method of example 1, wherein the machine learning model is an ensemble decision tree classifier.

[00089] Example 4

[00090] The method of example 1, wherein: (a) the machine learning model is adapted to provide, for each test well from the set of test wells, a prediction of growth or inhibition; and (b) determining the minimum inhibitory concentration of the antimicrobial agent based on the output of the machine learning model comprises determining the minimum inhibitory concentration based on the concentration of the antimicrobial agent in a test well with a lowest concentration of the antimicrobial agent and a prediction of inhibition.

[00091] Example 5

[00092] The method of example 1, wherein: (a) the machine learning model is a regression model; (b) the machine learning model is adapted to provide a concentration of the antimicrobial agent; and (c) determining the minimum inhibitory concentration of the antimicrobial agent based on the output of the machine learning model comprises treating the concentration of the antimicrobial agent as the minimum inhibitory concentration.

[00093] Example 6

[00094] The method of example 1, wherein for each of the plurality of imaging times, for each of the set of test wells, identifying the plurality of microorganisms in that test well at that imaging time comprises: (a) identifying a set of edges; and (b) a set of circular segments; in the image of that test well captured at that imaging time.

[00095] Example 7

[00096] The method of example 6, wherein for each of the plurality of imaging times, for each of the set of test wells, identifying the set of edges comprises: (a) applying a Gaussian kernel having values based on: (i) an expected thickness of microorganisms identified in that test well at that imaging time; and (ii) resolution of the image captured of that test well at that imaging time; and (b) searching in an area surrounding pixels identified based on application of the Gaussian kernel, wherein the area is defined by the expected thickness of microorganisms identified in that test well at that imaging time.

[00097] Example 8

[00098] The method of example 6, wherein for each of the plurality of imaging times, for each of the set of test wells, identifying the set of circular segments comprises: (a) identifying, for each point comprised by the set of edges, a center of motion for that point; and (b) for each point comprised by the set of edges, comparing a threshold with a result obtained by dividing the center of motion for that point by an average of all points comprised by the set of edges.

[00099] Example 9

[000100] The method of example 1, wherein the antimicrobial agent is selected from a group consisting of: (a) cefepime; (b) ceftazidime; and (c) ceftriaxone.

[000101] Example 10

[000102] The method of example 1, wherein the biological sample comprises microorganisms selected from a group consisting of: (a) E. cloacae,' (b) E. coir, (c) K. pneumoniae,' and (d) P. mirabilis.

[000103] Example 11

[000104] A biological testing system comprising a processor configured with a set of computer instructions operable, when executed, to cause the system to perform a method of any of examples 1-9.

[000105] Example 12

[000106] A biological testing system comprising: (a) a digital camera; (b) a plurality of wells; and (c) a means for determining minimum inhibitory concentration of an antimicrobial based on numbers of items in classes populated using data extracted from images of the plurality of wells captured by the digital camera.

[000107] V. Miscellaneous

[000108] It should be understood that, in the above examples and the claims, a statement that something is “based on” something else should be understood to mean that it is determined at least in part by the thing that it is indicated as being based on. To indicate that something must be completely determined based on something else, it is described as being “based EXCLUSIVELY on” whatever it must be completely determined by.

[000109] It should be understood that, in the above examples and the claims, the phrase “means for determining minimum inhibitory concentration of an antimicrobial based on numbers of items in classes populated using data extracted from images of the plurality of wells captured by the digital camera” should be understood as a means plus function limitation as provided for in 35 U.S.C. § 112(f), in which the function is “determining minimum inhibitory concentration of an antimicrobial based on numbers of items in classes populated using data extracted from images of the plurality of wells captured by the digital camera” and the corresponding structure is a computer configured to perform algorithms as described in the context of step 117 of FIG. 9, FIGs. 11-13, and section III of this disclosure.

[000110] It should be understood that any of the examples described herein may include various other features in addition to or in lieu of those described above. By way of example only, any of the examples described herein may also include one or more of the various features disclosed in any of the various references that are incorporated by reference herein.

[000111] It should be understood that any one or more of the teachings, expressions, embodiments, examples, etc. described herein may be combined with any one or more of the other teachings, expressions, embodiments, examples, etc. that are described herein. The above-described teachings, expressions, embodiments, examples, etc. should therefore not be viewed in isolation relative to each other. Various suitable ways in which the teachings herein may be combined will be readily apparent to those of ordinary skill in the art in view of the teachings herein. Such modifications and variations are intended to be included within the scope of the claims.

[000112] It should be appreciated that any patent, publication, or other disclosure material, in whole or in part, that is said to be incorporated by reference herein is incorporated herein only to the extent that the incorporated material does not conflict with existing definitions, statements, or other disclosure material set forth in this disclosure. As such, and to the extent necessary, the disclosure as explicitly set forth herein supersedes any conflicting material incorporated herein by reference. Any material, or portion thereof, that is said to be incorporated by reference herein, but which conflicts with existing definitions, statements, or other disclosure material set forth herein will only be incorporated to the extent that no conflict arises between that incorporated material and the existing disclosure material.

[000113] Having shown and described various versions of the present invention, further adaptations of the methods and systems described herein may be accomplished by appropriate modifications by one of ordinary skill in the art without departing from the scope of the present invention. Several of such potential modifications have been mentioned, and others will be apparent to those skilled in the art. For instance, the examples, versions, geometries, materials, dimensions, ratios, steps, and the like discussed above are illustrative and are not required. Accordingly, the scope of the present invention should be considered in terms of the following claims and is understood not to be limited to the details of structure and operation shown and described in the specification and drawings.

Claims

CLAIMS A method comprising:

(i) an antimicrobial agent; and

(b) incubating the set of test mixtures;

(e) providing the plurality of model inputs to a machine learning model; and

34 The method of claim 1, wherein, for each of the plurality of imaging times and each of the set of test wells, the set of characteristics determined for each microorganism identified in that test well at that imaging time:

(a) consists of a single characteristic; and

(b) the single characteristic is length. The method of claim 1 or claim 2, wherein the machine learning model is an ensemble decision tree classifier. The method of any one of claims 1 to 3, wherein:

(b) determining the minimum inhibitory concentration of the antimicrobial agent based on the output of the machine learning model comprises determining the minimum inhibitory concentration based on the concentration of the antimicrobial agent in a test well with a lowest concentration of the antimicrobial agent and a prediction of inhibition. The method of any one of claims 1 to 3, wherein:

(a) the machine learning model is a regression model;

(c) determining the minimum inhibitory concentration of the antimicrobial agent based on the output of the machine learning model comprises treating the concentration of the antimicrobial agent as the minimum inhibitory concentration. The method of any one of claims 1 to 5, wherein for each of the plurality of imaging times, for each of the set of test wells, identifying the plurality of microorganisms in that test well at that imaging time comprises:

35 (a) identifying a set of edges; and

(b) a set of circular segments; in the image of that test well captured at that imaging time. The method of claim 6, wherein for each of the plurality of imaging times, for each of the set of test wells, identifying the set of edges comprises:

(a) applying a Gaussian kernel having values based on:

(b) searching in an area surrounding pixels identified based on application of the Gaussian kernel, wherein the area is defined by the expected thickness of microorganisms identified in that test well at that imaging time. The method of claim 6 or claim 7, wherein for each of the plurality of imaging times, for each of the set of test wells, identifying the set of circular segments comprises:

(b) for each point comprised by the set of edges, comparing a threshold with a result obtained by dividing the center of motion for that point by an average of all points comprised by the set of edges. The method of any one of claims 1 to 8, wherein the antimicrobial agent is selected from a group consisting of:

(a) cefepime;

(b) ceftazidime; and

(c) ceftriaxone. The method of any one of claims 1 to 9, wherein the biological sample comprises microorganisms selected from a group consisting of:

(a) E. cloacae,'

(b) E. co!i,

(c) K. pneumoniae,' and

(d) P. mirabilis. A biological testing system comprising a processor configured with a set of computer instructions operable, when executed, to cause the system to perform a method comprising:

(i) an antimicrobial agent; and

(b) incubating the set of test mixtures;

(iii) organizing the identified microorganisms in that test well at that imaging time into a plurality of classes based on the set of characteristics determined for the identified microorganisms; and (iv) for each class from the plurality of classes, treating a number of microorganisms in that class in that test well at that imaging time as a model input;

(e) providing the plurality of model inputs to a machine learning model; and

(f) determining a minimum inhibitory concentration of the antimicrobial agent based on an output of the machine learning model. The system of claim 11, wherein, for each of the plurality of imaging times and each of the set of test wells, the set of characteristics determined for each microorganism identified in that test well at that imaging time:

(a) consists of a single characteristic; and

(b) the single characteristic is length. The system of claim 11 or claim 12, wherein the machine learning model is an ensemble decision tree classifier. The system of any one of claims 11 to 13, wherein:

(b) determining the minimum inhibitory concentration of the antimicrobial agent based on the output of the machine learning model comprises determining the minimum inhibitory concentration based on the concentration of the antimicrobial agent in a test well with a lowest concentration of the antimicrobial agent and a prediction of inhibition. The system of any one of claims 11 to 14, wherein:

(a) the machine learning model is a regression model;

38 (c) determining the minimum inhibitory concentration of the antimicrobial agent based on the output of the machine learning model comprises treating the concentration of the antimicrobial agent as the minimum inhibitory concentration. The system of any one of claims 11 to 15, wherein for each of the plurality of imaging times, for each of the set of test wells, identifying the plurality of microorganisms in that test well at that imaging time comprises:

(a) identifying a set of edges; and

(b) a set of circular segments; in the image of that test well captured at that imaging time. The system of claim 16, wherein for each of the plurality of imaging times, for each of the set of test wells, identifying the set of edges comprises:

(a) applying a Gaussian kernel having values based on:

(b) searching in an area surrounding pixels identified based on application of the Gaussian kernel, wherein the area is defined by the expected thickness of microorganisms identified in that test well at that imaging time. The system of claim 16 or claim 17, wherein for each of the plurality of imaging times, for each of the set of test wells, identifying the set of circular segments comprises:

39 The system of any one of claims 11 to 18, wherein the antimicrobial agent is selected from a group consisting of:

(a) cefepime;

(b) ceftazidime; and

(c) ceftriaxone. A biological testing system comprising:

(a) a digital camera;

(b) a plurality of wells; and

(c) a means for determining minimum inhibitory concentration of an antimicrobial based on numbers of items in classes populated using data extracted from images of the plurality of wells captured by the digital camera optionally using the method of any one of claims 1 to 10.

40