CN113088555A - Microbial colony inspection method and system based on machine learning model - Google Patents

Microbial colony inspection method and system based on machine learning model Download PDF

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CN113088555A
CN113088555A CN202110456021.4A CN202110456021A CN113088555A CN 113088555 A CN113088555 A CN 113088555A CN 202110456021 A CN202110456021 A CN 202110456021A CN 113088555 A CN113088555 A CN 113088555A
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黄学东
谢轶
杨骐瑞
聂鼎宜
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Sichuan University
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Abstract

The invention discloses a microbial colony inspection method and an inspection system based on a machine learning model. The inspection method comprises the following steps: acquiring a sample photo of a sample to be detected; inputting the sample picture of the sample to be detected into a machine training model for processing; outputting the colony growth condition of the sample to be detected; the machine training model is obtained by training a machine learning model by adopting a training sample set at least composed of specimen photos; the specimen photographs include at least a first specimen photograph in which a colony grows and a second specimen photograph in which a colony does not grow. The invention utilizes the machine training model to realize fast and accurate interpretation of the bacterial colony in the cultured sample photo, and particularly when the invention is combined with a microorganism inspection assembly line, the full automation of inspection can be realized, thereby not only reducing the subjectivity of diagnosis of medical workers and improving the accuracy of microorganism colony identification, but also effectively reducing the labor intensity of medical workers.

Description

Microbial colony inspection method and system based on machine learning model
Technical Field
The invention belongs to the technical field of microbial colony detection, and particularly relates to a microbial colony detection method and a microbial colony detection system based on a machine learning model.
Background
Clinical microbiological examination technology provides diagnosis and treatment information for infectious diseases. With the development of automation in clinical microbiological laboratories, the current microbiological test can be used for the streamlined test integrating the operation flows of automatic inoculation, incubation, detection, drug sensitivity test and the like, namely a microbiological test streamline.
However, the current microbial testing assembly line can only accurately perform photographic processing and storage functions on cultured colony images, cannot automatically interpret results, and instead, stored photos are manually interpreted by laboratory operators the next day to give interpretation results. Because the number of the photos read and interpreted every day is large, the image background is complex, the inspection process is easy to have recognition errors, the result is easy to have deviation, and the subsequent inspection process is easy to be complicated and has long time; the requirement for an operator to interpret the photo result is extremely high (experience is rich), and the operator must be familiar with the operation process to improve the working efficiency and shorten the TAT time. Therefore, there is a high necessity for a function of automatically processing daily photographs.
Disclosure of Invention
The invention mainly aims to provide a microbial colony inspection method, an inspection system, an electronic device and a computer readable storage medium based on a machine learning model, so as to solve the technical problems of complicated inspection process and long time existing in the prior art of manually judging the growth condition of the microbial colony.
In order to solve the technical problems, the invention firstly provides a microbial colony inspection method based on a machine learning model. The technical scheme is as follows:
the microbial colony inspection method based on the machine learning model comprises the following steps:
acquiring a sample photo of a sample to be detected;
inputting the sample picture of the sample to be detected into a machine training model for processing;
outputting the colony growth condition of the sample to be detected;
the machine training model is obtained by training a machine learning model by adopting a training sample set at least composed of specimen photos; the specimen photographs include at least a first specimen photograph in which a colony grows and a second specimen photograph in which a colony does not grow.
Further, the first type of specimen photo comprises specimen photos of colonies with at least two colors; and/or the first type of specimen photos comprise specimen photos with the colony number of 1, specimen photos with the colony number of 2-30 and specimen photos with the colony number of more than or equal to 31; and/or the first type of specimen photo comprises a non-catheter specimen photo and a catheter specimen photo.
Further, the second type of specimen photo includes a non-catheter specimen photo and a catheter specimen photo; and/or the second type of specimen photo comprises a specimen photo with magnetic bead pits.
Furthermore, the number of the first type specimen photo and/or the second type specimen photo is larger than or equal to 1200.
Further, a deep learning framework adopted by the machine learning model is Tensorflow; the model architecture training classification model adopted by the machine learning model is Big Transfer; the computer programming language adopted by the machine learning model is Python; the machine learning model adopts a Pylnstaller as a manufacturing interface; the graphics card training model adopted by the machine learning model is RTX 20070.
And further, inputting the patient information corresponding to the sample to be detected into a machine training model, and outputting the patient information and the bacterial colony growth condition corresponding to the patient information after the patient information is processed by the machine training model.
Further, the sample photo of the sample to be detected is obtained by a camera device and a processing system which are arranged on the microorganism detection production line.
In order to solve the technical problem, the invention secondly provides a microbial colony inspection system based on a machine learning model. The technical scheme is as follows:
a microbial colony inspection system based on a machine learning model comprises
The camera module is used for collecting a sample photo of a sample to be detected;
the training module is used for processing the sample photo of the sample to be detected by using a machine training model;
and the transmission module is used for transmitting the sample picture of the sample to be detected and the processing result of the training module.
In order to solve the above technical problem, the present invention provides an electronic device. The technical scheme is as follows:
an electronic device, comprising: a processor; a memory for storing processor-executable instructions; the processor is configured to perform the above-described inspection method.
In order to solve the above technical problem, the present invention further provides a computer-readable storage medium. The technical scheme is as follows:
a computer-readable storage medium comprising a stored program which when executed performs the verification method described above.
In conclusion, the microbial colony inspection method based on the machine learning model, the inspection system, the electronic device and the computer readable storage medium are simple and easy to implement, the colony in the cultured sample photo is quickly and accurately interpreted by utilizing the machine training model, and particularly when the machine training model is combined with a microbial inspection assembly line, the full automation of inspection can be realized, so that the diagnosis subjectivity of medical workers is reduced, the identification accuracy of the microbial colony is improved, the labor intensity of medical workers is effectively reduced, and the machine training model has great significance for the work development of clinical microbial laboratories.
Obviously, many modifications, substitutions, and variations are possible in light of the above teachings of the invention, without departing from the basic technical spirit of the invention, as defined by the following claims.
The present invention will be described in further detail with reference to the following examples. This should not be understood as limiting the scope of the above-described subject matter of the present invention to the following examples. All the technologies realized based on the above contents of the present invention belong to the scope of the present invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to assist in understanding the invention, and are included to explain the invention and their equivalents and not limit it unduly. In the drawings:
FIG. 1 is a schematic flow chart of an embodiment of a method for testing microbial colonies based on a machine learning model according to the present invention.
FIG. 2 is a schematic structural view of an embodiment of a microbiological test line.
FIG. 3 is a schematic structural diagram of an embodiment of a machine learning model-based microbial colony assay system according to the present invention.
FIG. 4 is a schematic structural diagram of an embodiment of the electronic device for testing microbial colonies of the present invention.
The relevant references in the above figures are:
110: camera module, 120: training module, 130: transmission module, 210: processor, 220: memory, 230: communication interface, 240: bus, 300: track system, 301: plate auto-sort module, 302: flat bar code label module, 303: inoculation and streaking module, 304: intelligent incubator, 305: high definition screen workstation, 306: flat plate stacking module, 307: MS target plate preparation module, 308: MS, 309: a drug sensitivity preparation module, 310: an automatic drug sensitivity identification instrument.
Detailed Description
The invention will be described more fully hereinafter with reference to the accompanying drawings. Those skilled in the art will be able to implement the invention based on these teachings. Before the present invention is described in detail with reference to the accompanying drawings, it is to be noted that:
the technical solutions and features provided in the present invention in the respective sections including the following description may be combined with each other without conflict.
Moreover, the embodiments of the present invention described in the following description are generally only some embodiments of the present invention, and not all embodiments. Therefore, all other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without any creative effort shall fall within the protection scope of the present invention.
With respect to terms and units in the present invention. The terms "comprising," "having," and any variations thereof in the description and claims of this invention and the related sections are intended to cover non-exclusive inclusions.
FIG. 1 is a schematic flow chart of an embodiment of a method for testing microbial colonies based on a machine learning model according to the present invention.
As shown in fig. 1, the microbial colony inspection method based on the machine learning model includes the following steps:
s100, obtaining a sample photo of a sample to be detected;
s200, inputting the sample picture of the sample to be detected into a machine training model for processing;
s300, outputting the colony growth condition of the sample to be detected;
the machine training model is obtained by training a machine learning model by adopting a training sample set at least composed of specimen photos; the specimen photographs include at least a first specimen photograph in which a colony grows and a second specimen photograph in which a colony does not grow. Wherein the content of the first and second substances,
the first type of specimen photo not only comprises specimen photos of colonies with at least two colors, such as specimen photos of colonies with various colors, such as white, gray, red, light yellow and the like, but also comprises specimen photos with 1 colony number, specimen photos with 2-30 colony numbers, specimen photos with colony numbers larger than or equal to 31, specimen photos without a guide pipe and specimen photos with a guide pipe.
The second type of specimen photo includes a non-conduit specimen photo, a conduit specimen photo, and a specimen photo with magnetic bead printing pits.
The first type specimen photo and the second type specimen photo are both more than or equal to 1200.
The deep learning framework adopted by the machine learning model is Tensorflow, such as Tensorflow 2.3;
the model architecture training classification model adopted by the machine learning model is Big Transfer (BiT for short) issued by Google;
the computer programming language adopted by the machine learning model is Python, such as Python 3.6;
the machine learning model adopts a pylnstar as a manufacturing interface, such as pylnstar 4.2;
the graphics card training model adopted by the machine learning model is RTX 20070.
And further comprising the steps of inputting the patient information corresponding to the sample to be detected into a machine training model, and outputting the bacterial colony growth condition corresponding to the patient information after the treatment of the machine training model.
The colony growth conditions include producing colonies and non-producing colonies.
And the sample photo of the sample to be detected is acquired by the camera equipment and the processing system which are arranged on the microorganism inspection production line.
FIG. 2 is a schematic structural view of an embodiment of a microbiological test line. As shown in FIG. 2, a first embodiment of a microbiological inspection line is a system that includes a processing system and an automated plate sorting module 301, a plate bar code label module 302, an inoculation and streaking module 303, an intelligent incubator 304, a high definition screen workstation 305, and a plate stacking module 306, all mounted on a track system 300. The second embodiment of the microorganism testing line may further include an MS target plate preparation module 307, an MS308, a drug sensitivity preparation module 309, and an automatic identification drug sensitivity analyzer 310 on the basis of the first embodiment. Wherein the content of the first and second substances,
the plate auto-sort module 301 is configured to: various nutrition plates (such as blood plates, chocolate plates and the like) cultured by microorganisms are placed in a storage box, and after clinical specimens are swept into the system, the culture plates are transferred to a plate bar code label module 302 through a rail system 300.
The flat-panel barcode label module 302 is configured to: the barcode of each clinical specimen identification is aerodynamically affixed to the culture plate, which is then transported to the inoculation and streaking module 303 via the rail system 300.
The inoculation and streaking module 303 is used to: the microorganism is pretreated by a robot, magnetic beads or other means, and then the culture plate is transferred to the intelligent incubator 304 through the rail system 300.
Intelligent incubator 304 is used for: in a specific incubation condition, the image pickup device in the intelligent incubator 304 takes pictures regularly and transmits the pictures to the high-definition screen workstation 305 during the incubation process.
High definition screen workstation 305 is used to: summarizing and displaying the photos in different time periods; in the prior art, all photos transmitted by a camera device are manually interpreted, about 2-3 workers are consumed in the process, and 3-4 hours of working time is consumed for processing every day, so that clinically significant strains can be screened out for strain identification and drug sensitivity approval.
The flat plate stacking module 306 is configured to: and stacking the culture plates processed by the high-definition screen workstation 305, and performing subsequent high-pressure treatment.
The MS target plate preparation module 307 is used to: and (4) carrying out MS target plate preparation on the microorganisms growing on the culture flat plate corresponding to the photo screened by the high-definition screen workstation 305.
The MS308 is configured to: and performing strain identification on the culture plate output by the MS target plate preparation module 307.
The drug sensitivity preparation module 309 is used to: and (4) carrying out drug sensitivity preparation on the microorganisms growing on the culture flat plate corresponding to the photo screened by the high-definition screen workstation 305.
The automatic identification drug susceptibility meter 310 is used for: and identifying the bacteria drug sensitivity identification card output by the drug sensitivity preparation module 309.
The processing system is to: receiving photos of camera equipment, displaying the photos on a high-definition screen workstation, controlling the gradual movement of a culture flat plate along a rail system, and controlling the heading of the culture flat plate corresponding to the manually screened photos; the processing system may be, but is not limited to, a fully automated microbial specimen processing system BD Kiestra using BD KiestraTMAnd InoquIA +TM。
When the microbial colony inspection method based on the machine learning model is combined with the microbial inspection assembly line, the machine training model is directly arranged on the high-definition screen workstation 305, and the machine training model can directly capture pictures stored by a processing system and perform interpretation; the processing result of the machine training model can be directly transmitted to a processing system and other terminal devices, and auditors can perform rechecking on the high-definition screen workstation 305 and can also perform remote rechecking on other terminal devices.
In order to ensure the accuracy of the processing result, the processing result is the culture plate with the negative result, the processing system conveys the corresponding culture plate to the plate stacking module 306 for stacking, and the staff wait for the next processing; and the culture plate with the positive treatment result is not treated temporarily, and is treated after manual rechecking, so that the office working time is obviously optimized, and the TAT time can be better shortened.
FIG. 3 is a schematic structural diagram of an embodiment of a machine learning model-based microbial colony assay system according to the present invention.
As shown in fig. 3, the microbial colony inspection system based on the machine learning model includes:
the camera module 110 is used for collecting a sample photo of a sample to be detected; the camera module 110 is arranged on the microorganism inspection production line;
a training module 120, configured to process a sample photograph of the sample to be detected using a machine training model;
the transmission module 130 is configured to transmit the sample photo collected by the camera module 110 to the training module 120 for processing, and transmit the processing result of the training module 120 to the terminal device, where the processing result is a conclusion whether the sample corresponding to the sample photo generates a bacterial colony or not; the terminal device may be, but is not limited to, the high definition screen workstation 305 described above.
FIG. 4 is a schematic structural diagram of an embodiment of the electronic device for testing microbial colonies of the present invention.
As shown in fig. 4, the electronic device includes: a processor 210; a memory 220 for storing processor-executable instructions; the processor 210 is configured to perform the microbial colony assay methods described above.
In particular, the processor 210 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement the above-described method for monitoring adverse drug reactions.
Memory 220 may include mass storage for data, which may include for data or instructions. By way of example, and not limitation, memory 220 may include a Hard Disk Drive (HDD), a floppy Disk Drive, flash memory, an optical Disk, a magneto-optical Disk, tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 220 may include removable or non-removable (or fixed) media, where appropriate. The memory 220 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 220 is a non-volatile solid-state memory. In a particular embodiment, the memory 220 includes Read Only Memory (ROM). Where appropriate, the ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory or a combination of two or more of these.
Processor 210 implements the above-described ADM monitoring method by reading and executing computer program instructions stored in memory 220.
In one embodiment of the microbial colony verifying electronic device, the microbial colony verifying electronic device may also include a communication interface 230 and a bus 240. As shown in fig. 4, the processor 210, the memory 220, and the communication interface 230 are connected via a bus 240 to complete communication therebetween.
The communication interface 230 is mainly used for communication among modules, devices, units and/or equipment required for microbial colony inspection. Bus 240 comprises hardware, software, or both that couple the components of the microbial colony verifying electronic device to one another. By way of example, and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hypertransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus or a combination of two or more of these. Bus 240 may include one or more buses, where appropriate. Although this disclosure describes and illustrates a particular bus, this disclosure contemplates any suitable bus or interconnect.

Claims (10)

1. The microbial colony inspection method based on the machine learning model comprises the following steps:
acquiring a sample photo of a sample to be detected;
inputting the sample picture of the sample to be detected into a machine training model for processing;
outputting the colony growth condition of the sample to be detected;
the machine training model is obtained by training a machine learning model by adopting a training sample set at least composed of specimen photos;
the specimen photographs include at least a first specimen photograph in which a colony grows and a second specimen photograph in which a colony does not grow.
2. The machine learning model-based microbial colony assay method of claim 1, wherein: the first type of specimen photo comprises specimen photos of colonies with at least two colors; and/or the first type of specimen photos comprise specimen photos with the colony number of 1, specimen photos with the colony number of 2-30 and specimen photos with the colony number of more than or equal to 31; and/or the first type of specimen photo comprises a non-catheter specimen photo and a catheter specimen photo.
3. The machine learning model-based microbial colony assay method of claim 1, wherein: the second type of specimen photo comprises a non-catheter specimen photo and a catheter specimen photo; and/or the second type of specimen photo comprises a specimen photo with magnetic bead pits.
4. The machine learning model-based microbial colony assay method of claim 1, wherein: the first type specimen photo and/or the second type specimen photo are more than or equal to 1200.
5. The machine learning model-based microbial colony assay method of claim 1, wherein: the deep learning framework adopted by the machine learning model is Tensorflow; the model architecture training classification model adopted by the machine learning model is Big Transfer; the computer programming language adopted by the machine learning model is Python; the machine learning model adopts a Pylnstaller as a manufacturing interface; the graphics card training model adopted by the machine learning model is RTX 20070.
6. The machine learning model-based microbial colony assay method of claim 1, wherein: the method also comprises the steps of inputting the patient information corresponding to the sample to be detected into the machine training model, and outputting the patient information and the bacterial colony growth condition corresponding to the patient information after the patient information is processed by the machine training model.
7. The machine learning model-based microbial colony assay method of claim 1, wherein: and the sample photo of the sample to be detected is acquired by the camera equipment and the processing system which are arranged on the microorganism inspection production line.
8. Microbial community inspection system based on machine learning model, its characterized in that: comprises that
The camera module (110) is used for collecting a sample photo of a sample to be detected;
a training module (120) for processing the sample picture of the sample to be tested using a machine training model;
and the transmission module (130) is used for transmitting the sample picture of the sample to be detected and the processing result of the training module (120).
9. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
the processor is configured to perform the inspection method of any one of claims 1-7.
10. A computer-readable storage medium, characterized in that: comprising a stored program which when executed performs the verification method of any one of claims 1 to 7.
CN202110456021.4A 2021-04-26 2021-04-26 Microbial colony inspection method and system based on machine learning model Pending CN113088555A (en)

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