WO2001026026A2 - Diagnostic local et reseaux de neurones de formation a distance pour diagnostic medical - Google Patents

Diagnostic local et reseaux de neurones de formation a distance pour diagnostic medical Download PDF

Info

Publication number
WO2001026026A2
WO2001026026A2 PCT/US2000/027220 US0027220W WO0126026A2 WO 2001026026 A2 WO2001026026 A2 WO 2001026026A2 US 0027220 W US0027220 W US 0027220W WO 0126026 A2 WO0126026 A2 WO 0126026A2
Authority
WO
WIPO (PCT)
Prior art keywords
neural network
data
medical condition
diagnosis
patient
Prior art date
Application number
PCT/US2000/027220
Other languages
English (en)
Other versions
WO2001026026A3 (fr
Inventor
James K. Walker
Stephen A. Tuchman
Won Young Choi
Original Assignee
University Of Florida
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by University Of Florida filed Critical University Of Florida
Priority to AU77491/00A priority Critical patent/AU7749100A/en
Publication of WO2001026026A2 publication Critical patent/WO2001026026A2/fr
Publication of WO2001026026A3 publication Critical patent/WO2001026026A3/fr

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS

Definitions

  • the present invention relates to medical diagnosis and more specifically to a system and method using neural networks for the diagnosis and interpretation of medical conditions.
  • the medical diagnosis task can be decomposed into three basic steps as follows: 1. Detection; 2. Classification; and 3. Recommendation. Detection refers to the step in which symptoms associated with one or more specific illnesses or conditions are first recognized. Classification is the process of designating or naming the condition, for instance, categorizing the condition into a known diagnostic group. Finally, recommendation is the step in which the physician prescribes a course of treatment for the condition.
  • Consistency On any given day, a physician may be fatigued or under stress. She or he may be inexpe ⁇ enced in a particular medical specialty. Identical clinical data and parameter values monitored for one patient may be interpreted differently by two physicians due to their different medical training, expe ⁇ ence level, stress level, or other factors.
  • Transference/interpretation One physician's mental rules in the diagnosis of a medical condition may be hard to desc ⁇ be and hence, difficult to transfer from one physician to another. These mental rules may also be difficult to explain to a patient if he asks how the physician arrived at the diagnosis or even to document reasoning for use by other physicians.
  • Nonlmeanty When the relationships between the monitored values and the patient's condition are complex and not well understood, conventional, e.g., linear, statistical, models are often inaccurate and thus not sufficient or reliable. Therefore, diagnostic technology using more complex nonlinear models is clearly preferable and often necessary.
  • Expert systems represent a different Al approach in which complex systems are modeled using a set of Production Rules, i.e., IF/THEN rules. Expert systems are popular because of their design simplicity and their capability to recommend actions by inference or search. They have been shown to be beneficial m diagnosis problems under certain circumstances.
  • the rule-based approach used in these systems requires a complete understanding of the task to be automated before an expert system can be implemented.
  • the large number of Production Rules required for increased robustness in the modeling of complex systems often slows down the decision-making process and aggravates maintenance due to the sheer number of rules to be kept track of
  • Fuzzy logic is typically used in situations where data and functional relationships cannot be expressed in clear mathematical terms. Instead, "fuzzy" relational equations are applied m which quantifiers such as "for many” or “for a few” are used to relate elements of different sets. Fuzzy logic systems provide conceptual advantages but require both intuition and expe ⁇ ence m the proper design of working medical diagnosis systems.
  • neural networks are networks of neuron-like units that can modify themselves by adapting to changing conditions. Unlike traditional Al systems which are rule-based, neural networks are very flexible and provide the capability of simulating complex nonlinear systems, the behavior of which is not well understood. This makes them uniquely suitable for medical diagnosis applications.
  • neural networks mimic the ability of the human bram to recognize recur ⁇ ng patterns on the basis of an inventory of previously learned patterns. In particular, they can predict the value of an output va ⁇ able based on input from several other input va ⁇ ables that can impact it. The prediction is made by selecting from a set of known patterns the one that appears most relevant in a particular situation. Because of their flexibility m modeling complex systems, neural nets have been widely used in the medical practice.
  • p ⁇ or neural networks have been limited in the extent of their training which requires an adequate amount of input data and corresponding reliable diagnoses. In particular, this is especially true and difficult to achieve for diagnoses of relatively rare diseases.
  • the input parameters of such diseases may also be correlated with additional factors such as patient age, ethnic background, etc., which may be c ⁇ tical to the correct diagnosis of the patient's condition. Accordingly, such systems have provided limited ability for reliable diagnosis under these conditions.
  • p ⁇ or art diagnostic tools based on classical statistical methods, expert system methods, and simple neural network methods have significant limitations when applied to medical diagnosis problems in general and especially to relatively rare medical diagnosis problems particularly where a disease or a medical condition is affected by a va ⁇ ety of patient-related additional parameters.
  • a novel medical diagnosis system including a neural network for diagnosis, an Internet connection, and a server on which neural net training occurs.
  • the neural network can be trained by being provided with the diagnosis made by a physician and with the measurement and interview data that was available to the physician.
  • the neural network system can use measurements and interview data to produce a score, or graded classification of the patient's medical condition to assist the physician in the diagnosis.
  • the central server can then receive data from one or more of the plurality of clinical sites, including, for example, images, patient information, and physician diagnoses.
  • a neural network on the server can then be trained at a faster rate than an individual neutral network at one of the clinical sites could be trained utilizing only the data from medical cases at that clinical site.
  • the server's neural network can learn at a fast rate, even for a relatively rare medical condition where there is not a sufficient volume of cases at an individual clinical site to effectively tram a neural network.
  • the central server receives data from many, if not all, of the clinical sites, such that there is an adequate rate for effective training at the central server.
  • the parameters of the central server's trained neural network are transmitted to each of the neural networks at the plurality of clinical sites.
  • the system can also provide an estimate of the likelihood of a medical condition being present which is characte ⁇ stic of the patient subsequently developing the disease.
  • the neural network of the server can be trained for this task by receiving patient data, such as images and patient information, from the p ⁇ or records of patients who have been reliably diagnosed to subsequently have the disease.
  • Figure 1 shows a schematic diagram of a system in accordance with the subject invention mco ⁇ oratmg a plurality of neural networks at a plurality of clinical sites, each neural network connected via the Internet to a central server which houses a neural network.
  • Figure 2 shows a schematic diagram of a neural network having an input layer of processing elements, a middle layer of processing elements, and an output layer composed of a single processing element.
  • Figure 3 shows a block diagram of an embodiment of a data processing system for use m one of the plurality of clinical sites in accordance with the subject invention.
  • Figure 4 shows a schematic diagram of an embodiment of a central server in accordance with the subject invention, mco ⁇ oratmg dual servers where each contain framing neural networks with data storage facilities.
  • Figure 5 shows data and speculative curves for the performance of a neural network m detecting breast cancer m accordance with the subject invention.
  • Figure 6 shows a schematic diagram of a specific embodiment of the subject invention for the detection of breast cancer utilizing composite digital images.
  • the subject invention pertains to a method and apparatus for medical diagnosis.
  • the subject invention can tram a centralized neural network to estimate the likelihood that a patient has a certain medical condition.
  • This centralized neural network can be trained by data collected at a plurality of clinical sites, which communicate this data to a centralized server via, for example, the Internet.
  • the centralized neural network is able to train much more quickly than if the training were based on data collected at an individual clinical site.
  • the centralized neural network's parameters can then be transmitted to each clinical site to update the neural network at each site.
  • the data acquired at each site can then be analyzed by the neural network on that site.
  • the method and apparatus of the subject invention are particularly advantageous for medical diagnosis of relatively rare medical conditions, wherein it is difficult to have an adequate number of occurrences at an individual clinical site to effectively train a neural network.
  • the subject invention can be applied to medical imaging for the detection of certain medical conditions.
  • medical imaging can include, for example, magnetic resonance imaging (MRI), CT scans, PET scans, ultrasound, x-ray, and other imaging techniques.
  • the subject method and apparatus can be utilized for the detection of breast cancer. For example, upon a reliable diagnosis of breast cancer in a patient, a radiographic image of a patient's breast tissue and other relevant data acquired at the individual clinical site can be communicated to a central server such that the image can be used to tram a centralized neural network. Other parameters can accompany the image, if desired.
  • the centralized neural network By receiving images from a plurality of sites, the centralized neural network can be trained more rapidly and effectively than a neural network at an individual site by images collected at that site The centralized neural network can then transmit updated parameters to each clinical site to update one or more neural networks at each site.
  • This system can also be used for chest and other imaging for the detection of, for example, cancer.
  • FIG. 3 illustrates a processing system 10 for use at one of the plurality of clinical sites m accordance with the subject invention.
  • Processing system 10 generally comp ⁇ ses a computer
  • Computer 12 which can be adapted to received input data from one or more sources, examples of which include an operator by means of a keyboard 14, a digital image file 24, and a patient database stored in memory 16. Memory 16 can also be used to store output data from the computer 12.
  • Computer 12 can be coupled to display module 18 which may be a computer monitor or similar device. System 10 can further comp ⁇ se a p ⁇ nter 19 for providing a hard copy of the diagnostic results.
  • An interface 22, such as a modem, can be used for connection to a network of computers and/or the Internet.
  • Computer 12 can execute a simulation of a neural network 20 and an inte ⁇ reter unit 25.
  • Computer 12 can be, for example, a PC, a mainframe computer, a server, or a workstation.
  • Computer 12 can be connected via interface 22 to a Local Area Network (LAN), Wide
  • processing system 10 can be made accessible to other computers via, for example, management software.
  • processing system 10 can include va ⁇ ous other input/output (I/O) and pe ⁇ pheral modules, as known m the art.
  • Central Server The subject invention can inco ⁇ orate a central server with a resident
  • the master neural network can be trained by the input of approp ⁇ ate data.
  • a specific embodiment of the central server is shown schematically m Figure 4.
  • the server shown in Figure 4 is connected to each of a plurality of neural networks located at a corresponding plurality of the clinical sites.
  • the function of the neural networks located at the clinical sites is to assist the physicians in performing medical diagnoses.
  • data corresponding to the patient can be sent to the central server.
  • data can include, for example, relevant patient image data files, and relevant patient information.
  • the master neural network can be trained on the transmitted data sets to optimize the ability of the software to detect the presence of a given medical condition such as a particular disease.
  • the optimized parameters of the master NN can be downloaded to the NN at each of the plurality of clinical sites.
  • the optimized, or updated, parameters received by the NN at each of the plurality of clinical sites updates the NN at each of the plurality of clinical sites to take advantage of the training of the master NN by data sets sent by one or more of the plurality of clinical sites.
  • the master NN can also receive and be trained by data sets corresponding to a confirmed diagnosis which was not conducted at one of the plurality of clinical sites.
  • master NN can be enhanced or optimized by other framing if desired.
  • patient image data and information from the p ⁇ or year, or several p ⁇ or years, from an annual screening or checkup, from patients subsequently positively diagnosed can be transmitted to the cenfral server.
  • the data set can include the pe ⁇ od of time between collection of the patient image and other data and the diagnosis of a certain stage of the medical condition.
  • the cenfral NN can then be trained with respect to this data m order to optimize the ability of the software to detect the presence of symptoms characte ⁇ stic of the later development of a particular medical condition such as a disease.
  • patient characte ⁇ stics which are correlated to, or predictive of, future development of a disease can be flagged, allowing preventive treatment and/or approp ⁇ ate preparation.
  • the master neural network algorithm can be optimized to detect the presence of a given medical condition and the detection of patient characte ⁇ stics which may indicate the likelihood of future development of the medical condition.
  • the present invention is particularly advantageous with respect to a disease which occurs relatively rarely. By utilizing the confirmed diagnosed cases at a plurality of the clinical sites, an adequate number of cases can be obtained to effectively train the central NN. In this way, the present invention can achieve effective diagnostic neural networks at each clinical site even for relatively rare diseases.
  • a person skilled in the art will also be able to use a central neural network for framing and improving the diagnostic functioning of neural networks at a plurality of sites.
  • neural networks To fully appreciate the va ⁇ ous aspects and benefits produced by the present invention, a basic understanding of neural network technology can be useful. Following is a bnef discussion of neural network technology as applicable to the medical diagnosis system and method of the present invention.
  • neural networks loosely model the functioning of a biological neural network, such as the human bram. Accordingly, neural networks are typically implemented as computer simulations of a system of interconnected neurons. In particular, neural networks are hierarchical collections of interconnected processing elements configured, for example, as shown in Figure
  • Figure 2 is a schematic diagram of a standard neural network having an input layer of processing elements, a middle layer of processing elements, and an output layer composed of a single processing element.
  • the example shown m Figure 2 is merely an illustrative embodiment of a neural network 20 that can be used in accordance with the present invention. Other embodiments of a neural network can also be used
  • each of its processing elements can receive multiple input signals, or data values, that are processed to compute a single output.
  • the output value is calculated using a mathematical equation, known in the art as an activation function or a transfer function that specifies the relationship between input data values and the output value. Vanous parameters can be used to control, and therefore update, the relationship between input data values and the output value.
  • the activation function may include a threshold, or a bias element.
  • the outputs of elements at lower network levels are provided as inputs to elements at higher levels.
  • the highest level element produces a final system output.
  • neural network 20 can be a computer simulation that produces a score, or graded classification, of a patient's medical condition, based on available measurements including, for example, image data file(s), responses, and other input factors.
  • the scores produced by the network might range continuously from zero to one, with scores near zero indicating a low likelihood of disease and scores near one indicating a high likelihood of disease.
  • the neural networks in the plurality of clinical sites can be updated penodically such that they can be identical to the neural network in the central server.
  • the neural network in the central server can continue to be trained, followed by additional updates of the clinical site neural networks.
  • the neural networks in the plurality of clinical sites can then be used for providing scores for indicating likelihood of disease or other medical condition.
  • the central server can act as the learning neural network which is trained to diagnose a given medical condition.
  • the neural network can be framed by being provided with confirmed diagnoses made by physicians accompanied by input data such as image data, measurement data, and interview data, that was available to the physician.
  • a given diagnosis along with the corresponding input data can be referred to as a data record.
  • All available data records taken from a plurality of clinical sites compnse a data set.
  • a data set corresponding to a particular medical condition can be stored m memory and made available for use by the processing system for training of the neural network m the central server.
  • a typical framing mechanism which can be used with respect to a preferred embodiment of the subject invention is bnefly desc ⁇ bed.
  • a my ⁇ ad of techniques has been proposed in the past for framing feedforward neural networks. Most currently used techniques are va ⁇ ations of the well-known error backpropagation method.
  • Rumelhardt, et al. m "Parallel Dist ⁇ ubbed Processing: Explorations m the Microstructure of Cognition," Vol. 1 and 2, Camb ⁇ dge: MIT Press (1986), and "Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercise," both of which are mco ⁇ orated herein by reference.
  • backpropagation learning is performed in three steps: forward pass; error backpropagation; and weight adjustment.
  • forward pass step m accordance with a specific embodiment of the present invention, a single data record can be provided to the input layer of the network. This input data propagates forward along the links to the middle layer elements which compute the weighted sums and transfer functions as descnbed above. Likewise, the outputs from the middle layer elements can propagated along the links to the output layer element. The output layer element can then compute the weighted sum and transfer function equation to produce the patient score.
  • the physician diagnosis associated with the data record can be made available.
  • the score produced by the neural network can then be compared with the physician's diagnosis, which is expressed in mathematically comparable terms as a nume ⁇ cal score.
  • an error signal can be computed as the difference between the score corresponding to the physician's diagnosis and the neural network score. This error can then be propagated from the output element back to the processing elements at the middle layer through a series of mathematical equations, as known in the art.
  • any error in the neural network score can be partially assigned to the processing elements that combined to produce it.
  • the outputs produced by the processing elements at the middle layer and the output layer can be mathematical functions of their connection weights. Errors in the outputs of these processing elements can anse from errors m the current values of the connection weights. Using the errors assigned at the previous step, weight adjustments can be made in the last step of the backpropagation learning method according to mathematical equations to reduce or eliminate the error in the neural network score.
  • the steps of the forward pass, error backpropagation, and weight adjustment can thus be performed repeatedly over the records in the data set. Through such repetition, the training of the neural network can be completed when the connection weights stabilize to certain values that minimize, at least locally, the diagnosis errors over the entire data set.
  • weight adjustments can be made in alternate embodiments of the present invention using different training mechanisms. It should be emphasized, that the present invention does not rely on a particular training mechanism. Rather, the only requirement is that the resulting network produce acceptable error rates m its sconng of patient conditions. Naturally, what is an acceptable error rate may in turn depend on the medical condition and other factors.
  • approp ⁇ ate information can be transmitted to each of the plurality of neural networks.
  • the values of the optimized connection weights can be downloaded from the server to each of the plurality of diagnostic neural networks at the plurality of clinical sites.
  • the diagnostic neural network at each site can then proceed to perform its diagnostic function on new patients' input data.
  • the framing process can be repeated with respect to the central neural network. This framing can be done with the combined data sets to achieve a better set of optimized connection weights.
  • this further optimized set of connection weights can then be used by the diagnostic neural networks at each site for a period of time.
  • the framing process can then be repeated with new data sets as they become available.
  • the training can be terminated once a desired standard of accuracy is reached.
  • the central server can have two servers, each with their own data storage as shown schematically in Figure 4.
  • the neural network on server A can be trained on the data set resident in storage A, similarly for server B and storage B.
  • the optimized neural network in server A can then be tested on the data set in storage B, and likewise for the other combination.
  • the effectiveness of the framed neural networks can then be measured as known m the art by any one of a number of ways. An example is given in Example 1.
  • training expenments can be performed to investigate correlation effects m the data.
  • two framing processes can be run for patient groups of two races.
  • an optimized set of connection weights can be obtained for each race.
  • Similar optimized connection weights can be obtained for patients in different age groups, lifetime estrogen exposure level, and a vanety of other parameters, some of which will be found to be important correlative factors for a given disease or medical condition.
  • These optimized data sets which are now a function of certain patient-related parameters can be downloaded to the diagnostic neural networks.
  • the neural net diagnosis can now be performed in an optimized manner tailored to the specific patient.
  • a data set can be assembled for patient conditions corresponding to one or more specific time intervals p ⁇ or to the patient's diagnosis with a given disease.
  • data can be assembled corresponding to one year prior to a patients diagnosis with cancer.
  • Neural network training can then be conducted on these "p ⁇ or year" data sets.
  • Optimized connection weights can then be determined and downloaded to the diagnostic neural networks.
  • Diagnosis can then be made for a patient condition symptomatic of disease developing in the subsequent year or other pe ⁇ od of time. For such a diagnosis, the patient may be advised to return in six months for further tests.
  • the neural net framing can be performed for different patient- related parameters as before, thereby providing supe ⁇ or diagnostic quality.
  • a training neural network m a central server which is connected to a plurality of diagnostic neural networks located m clinical settings.
  • Output of Diagnostic Neural Network System Upon completion of the neural network diagnostic process, the results can be displayed, for example on display 18, for use by the physician. The physician can review the results to aid m his or her diagnosis of the patient condition.
  • the displayed results can also be p ⁇ nted, for example on p ⁇ nter 19, to create a record of the results of the neural network process.
  • the p ⁇ nted output can be in the form of a reproduced image with supe ⁇ mposed indicator marks for the physician's consideration which are indicative of possible specific disease or different diseases at each of these locations.
  • interface 22 can permit communication of the results to other physicians.
  • interface 22 can permit Internet, or other equivalent, communication with the central server in the manner descnbed in the present application.
  • the diagnostic system and method of the present invention were descnbed with reference to a specific application which is the use of the invention for the diagnosis of medical conditions. It should be clear, however, that the pnnciples of this invention that provide for an enhanced inte ⁇ retive facility due to increased training even for highly specific and even relatively rare circumstances.
  • the present invention can readily be applied in areas as diverse as financial analysis, electronics design, oil exploration, fatigue determinations, and others.
  • the inte ⁇ retation of diagnostic scores provided by the present invention can be used in vanous complex systems for the pu ⁇ oses of prediction, planning, monitonng, debugging, repair, and instruction.
  • results from the inte ⁇ retation of systems scores obtained using this type of super- framed neural network can be used to develop production rules as part of an expert system, or to provide further insight into fuzzy relationships used m other artificial intelligence systems.
  • the following examples illustrates the use of the system and method of the present invention for the diagnosis of a particular medical condition.
  • Example 1 Application to Breast Lesion Diagnosis
  • CAD Computer-aided diagnosis
  • a CAD workstation hosting the neural network is located at a given mammographic facility.
  • the digitized film or digitally acquired image is processed by the neural network and a CAD output is made available for consideration by the radiologist.
  • the connection weights used by the neural network have been determined by the manufacturer of the CAD system based on a sample of one hundred to at most a few hundred cases of breast cancer.
  • pathologies such as spiculated masses, micro-calcifications, masses, etc.
  • due to the limited number of cases for NN framing there is little or no opportunity to correlate patient parameters such as age, race, lifetime estrogen exposure level, etc., which are known to correlate with the presence of breast cancer.
  • Figure 5 shows data for the performance of a typical neural network in detecting breast masses.
  • the true positive (TP) detection rate is plotted versus the number of diagnosed cases used for framing the neural network.
  • the two data points correspond to a false positive (FP) rate per image of 4.2 and 2.0 respectively. Needless to say, it is desirable to keep the FP rate as low as possible.
  • Two curves with arbitrary but reasonable shape are drawn through the two data points. These curves indicate the general anticipated performance of the neural network versus the number of diagnosed cases used for training the neural network. There is no theoretical or expenmental information on the detailed shape of these curves.
  • the present invention can utilize a central server whose resident NN is trained by diagnosed cases from a multiplicity of clinical sites. Due to the large and ever-increasing number of diagnosed cases, the NN diagnosis can also be trained for subsets of patients grouped according to disease-related parameters as discussed earlier. In addition, NN training can be performed for pnor year mammographic images. Pathology of breast disease prior to cancer detection can then be identified as descnbed earlier. According to the present invention, major and novel benefits can result from having a very large number of disease-diagnosed patient data available for neural network training. Furthermore, according to the present invention, a means is disclosed to effectively and efficiently achieve this objective.
  • Example 1 The traditional annual mammographic screening m Example 1 is performed with a cassette containing a single screen and single film. Screening can also be performed with a cassette containing a single screen and two films located on the opposite faces of the screen as disclosed in U.S. Pat. Nos. 5,751,787, PCT/US97/15589 and pending U.S. Serial No. 09/075,670, both of which are mco ⁇ orated herein by reference.
  • the first film is exposed m the usual way as in Example 1.
  • the second film may be exposed to a fraction of the light intensity compared to the light intensity exposing the first film.
  • the second film is essentially identical to the first film, and is exposed to about half the light intensity compared to the light intensity exposing the first film.
  • the effective latitude of the dual film system is about twice that of the conventional single film technique.
  • This increased latitude overcomes the major limitation of conventional film screen mammography.
  • the two films may be digitized m a commercial film digitizer and the two digital image files manipulated and combined, if desired, in a computer to produce a single digital image file with wide latitude as disclosed in U.S. Pat. Nos. 5,751,787, PCT/US97/15589 and pending U.S. Se ⁇ al No. 09/075,670.
  • Figure 6 shows a schematic of a specific embodiment of the subject invention where two images are used to form a composite digital image which is then analyzed by one of a plurality of neural networks. Each of the plurality of neural networks received periodic updates from a central neural network.
  • the neural network system may be applied to analyze each digital image separately and/or the combined image to detect pathologies as descnbed m Example 1. All other considerations discussed m Example 1 are applicable in this example.
  • Example 3 Lung Lesion Diagnosis Using a Digital X-Ray Image Acquisition System
  • the method and apparatus of the subject invention can also be utilized for in x-ray imaging of the chest to diagnose the presence of lung lesions or other abnormalities , the process of diagnosis by the radiologist can be difficult and time consuming, even with an advanced digital image acquisition system.
  • the signal of the presence of a lesion may be very subtle and easily "missed” especially when "hidden” behind the portions of the image occupied by bones In these cases, the subject NN system can be efficiently framed to identify these lesions.
  • the subtlety of these diagnoses is such that great benefit can be obtained from the large number of cases utilized to tram the central neural network.

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Medical Informatics (AREA)
  • Primary Health Care (AREA)
  • Epidemiology (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Computational Linguistics (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Molecular Biology (AREA)
  • Evolutionary Computation (AREA)
  • Biophysics (AREA)
  • Computing Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Business, Economics & Management (AREA)
  • General Business, Economics & Management (AREA)
  • Databases & Information Systems (AREA)
  • Pathology (AREA)
  • Medical Treatment And Welfare Office Work (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

La présente invention concerne un procédé et un système de diagnostic d'un état pathologique. Un mode de réalisation spécifique de l'invention met en oeuvre une pluralité de réseaux de neurones en une pluralité correspondante de sites cliniques afin d'assister des physiciens dans le diagnostic d'un état pathologique d'un patient. Chacun des réseaux de neurones peut communiquer avec un serveur associé au réseau central via, par exemple, l'Internet. Le serveur peut recevoir des données concernant le patient pouvant inclure, par exemple, des images, des cordonnées du malade, de paramètres, des renseignements sur la biopsie, et des diagnostics médicaux. Le réseau neuronal central peut être soumis à une formation sur un volume considérable de cas médicaux, provenant d'une pluralité de sites cliniques. Même pour des états pathologiques relativement rares, on peut fournir au réseau neuronal central des cas ayant fait l'objet d'un diagnostic à un taux suffisant permettant une formation efficace du réseau neuronal central. En temps et lieu, on peut transmettre les paramètre optimisés du réseau central ayant été soumis à une formation à chacun des réseaux de neurones dans les sites cliniques. Le réseau neuronal en un site peut ainsi aider un physicien à déterminer de manière fiable la nature et la probabilité d'un état pathologique même lorsque cela dépend d'un grand nombre de coordonnées du malade et même s'il s'agit d'un état pathologique rare.
PCT/US2000/027220 1999-10-04 2000-10-03 Diagnostic local et reseaux de neurones de formation a distance pour diagnostic medical WO2001026026A2 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU77491/00A AU7749100A (en) 1999-10-04 2000-10-03 Local diagnostic and remote learning neural networks for medical diagnosis

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US41150299A 1999-10-04 1999-10-04
US09/411,502 1999-10-04

Publications (2)

Publication Number Publication Date
WO2001026026A2 true WO2001026026A2 (fr) 2001-04-12
WO2001026026A3 WO2001026026A3 (fr) 2002-05-16

Family

ID=23629200

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2000/027220 WO2001026026A2 (fr) 1999-10-04 2000-10-03 Diagnostic local et reseaux de neurones de formation a distance pour diagnostic medical

Country Status (2)

Country Link
AU (1) AU7749100A (fr)
WO (1) WO2001026026A2 (fr)

Cited By (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1378853A1 (fr) * 2002-07-04 2004-01-07 GE Medical Systems Global Technology Company LLC Système numérique d'assistance médicale
WO2004047624A2 (fr) * 2002-11-26 2004-06-10 Cardiac Pacemakers, Inc. Systeme et methode de diagnostic automatique de l'etat de sante d'un patient
EP1488355A1 (fr) * 2002-03-26 2004-12-22 Ibex Healthdata Systems Inc. Systeme et methode pour le depistage d'evenements lies a la sante
WO2005081168A2 (fr) * 2004-02-03 2005-09-01 Siemens Medical Solutions Usa, Inc. Systemes et procedes de diagnostic automatise et de support de decision utilises dans les maladies et les etats cardiaques
WO2007120904A2 (fr) * 2006-04-14 2007-10-25 Fuzzmed, Inc. Systeme, procede et dispositif soins medicaux personnels, d'analyse intelligente et de diagnostic
US7840421B2 (en) 2002-07-31 2010-11-23 Otto Carl Gerntholtz Infectious disease surveillance system
RU2449281C1 (ru) * 2011-04-01 2012-04-27 Маргарита Александровна Сидорова Способ диагностики патологий системы гемостаза с помощью нейронных сетей
WO2016119429A1 (fr) * 2015-01-26 2016-08-04 华为技术有限公司 Système et procédé pour un ensemble de paramètres d'apprentissage dans un réseau neuronal
CN107729911A (zh) * 2017-07-26 2018-02-23 江西中科九峰智慧医疗科技有限公司 一种基于dr的肺结核智能识别方法及系统
CN107730484A (zh) * 2017-07-26 2018-02-23 江西中科九峰智慧医疗科技有限公司 一种基于深度学习的异常胸片智能识别方法及系统
CN108596868A (zh) * 2017-07-26 2018-09-28 江西中科九峰智慧医疗科技有限公司 一种基于深度学习的胸部dr中肺结节识别方法及系统
CN108596198A (zh) * 2017-07-26 2018-09-28 江西中科九峰智慧医疗科技有限公司 一种基于深度学习的气胸x光图像识别方法及系统
CN108846840A (zh) * 2018-06-26 2018-11-20 张茂 肺部超声图像分析方法、装置、电子设备及可读存储介质
CN110114834A (zh) * 2016-11-23 2019-08-09 通用电气公司 用于医疗程序的深度学习医疗系统和方法
US10593431B1 (en) 2019-06-03 2020-03-17 Kpn Innovations, Llc Methods and systems for causative chaining of prognostic label classifications
EP3520038A4 (fr) * 2016-09-28 2020-06-03 D5A1 Llc Entraîneur d'apprentissage pour système d'apprentissage automatique
CN111582434A (zh) * 2019-02-18 2020-08-25 韩国宝之铂株式会社 深度学习系统
CN113039578A (zh) * 2018-11-16 2021-06-25 医科达有限公司 利用深度神经网络的实时运动监测
US11321612B2 (en) 2018-01-30 2022-05-03 D5Ai Llc Self-organizing partially ordered networks and soft-tying learned parameters, such as connection weights
US11361434B2 (en) 2019-01-25 2022-06-14 Otonexus Medical Technologies, Inc. Machine learning for otitis media diagnosis
US11468363B2 (en) 2019-07-03 2022-10-11 Kpn Innovations, Llc. Methods and systems for classification to prognostic labels using expert inputs
US11621081B1 (en) * 2018-11-13 2023-04-04 Iqvia Inc. System for predicting patient health conditions
WO2023081183A1 (fr) * 2021-11-03 2023-05-11 Liveramp, Inc. Apprentissage vertical fractionné à confidentialité différentielle (dp)
US11915152B2 (en) 2017-03-24 2024-02-27 D5Ai Llc Learning coach for machine learning system

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4912647A (en) * 1988-12-14 1990-03-27 Gte Laboratories Incorporated Neural network training tool
US5463548A (en) * 1990-08-28 1995-10-31 Arch Development Corporation Method and system for differential diagnosis based on clinical and radiological information using artificial neural networks
US5839438A (en) * 1996-09-10 1998-11-24 Neuralmed, Inc. Computer-based neural network system and method for medical diagnosis and interpretation
WO1999027841A1 (fr) * 1997-12-04 1999-06-10 Virtual-Eye. Com Test automatise de la fonction visuelle via la telemedecine

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4912647A (en) * 1988-12-14 1990-03-27 Gte Laboratories Incorporated Neural network training tool
US5463548A (en) * 1990-08-28 1995-10-31 Arch Development Corporation Method and system for differential diagnosis based on clinical and radiological information using artificial neural networks
US5839438A (en) * 1996-09-10 1998-11-24 Neuralmed, Inc. Computer-based neural network system and method for medical diagnosis and interpretation
WO1999027841A1 (fr) * 1997-12-04 1999-06-10 Virtual-Eye. Com Test automatise de la fonction visuelle via la telemedecine

Cited By (45)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1488355A1 (fr) * 2002-03-26 2004-12-22 Ibex Healthdata Systems Inc. Systeme et methode pour le depistage d'evenements lies a la sante
EP1488355A4 (fr) * 2002-03-26 2007-10-10 Ibex Healthdata Systems Inc Systeme et methode pour le depistage d'evenements lies a la sante
EP1378853A1 (fr) * 2002-07-04 2004-01-07 GE Medical Systems Global Technology Company LLC Système numérique d'assistance médicale
US7840421B2 (en) 2002-07-31 2010-11-23 Otto Carl Gerntholtz Infectious disease surveillance system
WO2004047624A2 (fr) * 2002-11-26 2004-06-10 Cardiac Pacemakers, Inc. Systeme et methode de diagnostic automatique de l'etat de sante d'un patient
WO2004047624A3 (fr) * 2002-11-26 2005-03-10 Cardiac Pacemakers Inc Systeme et methode de diagnostic automatique de l'etat de sante d'un patient
JP2006507875A (ja) * 2002-11-26 2006-03-09 カーディアック・ペースメーカーズ・インコーポレーテッド 患者の健康を自動診断するシステムおよび方法
US7912528B2 (en) 2003-06-25 2011-03-22 Siemens Medical Solutions Usa, Inc. Systems and methods for automated diagnosis and decision support for heart related diseases and conditions
WO2005081168A2 (fr) * 2004-02-03 2005-09-01 Siemens Medical Solutions Usa, Inc. Systemes et procedes de diagnostic automatise et de support de decision utilises dans les maladies et les etats cardiaques
WO2005081168A3 (fr) * 2004-02-03 2005-12-01 Siemens Medical Solutions Systemes et procedes de diagnostic automatise et de support de decision utilises dans les maladies et les etats cardiaques
JP2007527743A (ja) * 2004-02-03 2007-10-04 シーメンス　メディカル　ソリューションズ　ユーエスエー　インコーポレイテッド 心臓関連の病気及び状態のための自動診断及び意思決定支援用システム及び方法
AU2004316027B2 (en) * 2004-02-03 2009-07-30 Siemens Corporation Systems and methods for automated diagnosis and decision support for heart related diseases and conditions
US10410308B2 (en) 2006-04-14 2019-09-10 Fuzzmed, Inc. System, method, and device for personal medical care, intelligent analysis, and diagnosis
WO2007120904A3 (fr) * 2006-04-14 2008-12-04 Fuzzmed Inc Systeme, procede et dispositif soins medicaux personnels, d'analyse intelligente et de diagnostic
WO2007120904A2 (fr) * 2006-04-14 2007-10-25 Fuzzmed, Inc. Systeme, procede et dispositif soins medicaux personnels, d'analyse intelligente et de diagnostic
RU2449281C1 (ru) * 2011-04-01 2012-04-27 Маргарита Александровна Сидорова Способ диагностики патологий системы гемостаза с помощью нейронных сетей
WO2016119429A1 (fr) * 2015-01-26 2016-08-04 华为技术有限公司 Système et procédé pour un ensemble de paramètres d'apprentissage dans un réseau neuronal
EP3520038A4 (fr) * 2016-09-28 2020-06-03 D5A1 Llc Entraîneur d'apprentissage pour système d'apprentissage automatique
US11755912B2 (en) 2016-09-28 2023-09-12 D5Ai Llc Controlling distribution of training data to members of an ensemble
US11615315B2 (en) 2016-09-28 2023-03-28 D5Ai Llc Controlling distribution of training data to members of an ensemble
US11610130B2 (en) 2016-09-28 2023-03-21 D5Ai Llc Knowledge sharing for machine learning systems
US11386330B2 (en) 2016-09-28 2022-07-12 D5Ai Llc Learning coach for machine learning system
US11210589B2 (en) 2016-09-28 2021-12-28 D5Ai Llc Learning coach for machine learning system
US10839294B2 (en) 2016-09-28 2020-11-17 D5Ai Llc Soft-tying nodes of a neural network
CN110114834A (zh) * 2016-11-23 2019-08-09 通用电气公司 用于医疗程序的深度学习医疗系统和方法
CN110114834B (zh) * 2016-11-23 2024-04-26 通用电气公司 用于医疗程序的深度学习医疗系统和方法
US11915152B2 (en) 2017-03-24 2024-02-27 D5Ai Llc Learning coach for machine learning system
CN107729911A (zh) * 2017-07-26 2018-02-23 江西中科九峰智慧医疗科技有限公司 一种基于dr的肺结核智能识别方法及系统
CN107730484A (zh) * 2017-07-26 2018-02-23 江西中科九峰智慧医疗科技有限公司 一种基于深度学习的异常胸片智能识别方法及系统
CN108596868A (zh) * 2017-07-26 2018-09-28 江西中科九峰智慧医疗科技有限公司 一种基于深度学习的胸部dr中肺结节识别方法及系统
CN108596198A (zh) * 2017-07-26 2018-09-28 江西中科九峰智慧医疗科技有限公司 一种基于深度学习的气胸x光图像识别方法及系统
US11321612B2 (en) 2018-01-30 2022-05-03 D5Ai Llc Self-organizing partially ordered networks and soft-tying learned parameters, such as connection weights
CN108846840B (zh) * 2018-06-26 2021-11-09 张茂 肺部超声图像分析方法、装置、电子设备及可读存储介质
CN108846840A (zh) * 2018-06-26 2018-11-20 张茂 肺部超声图像分析方法、装置、电子设备及可读存储介质
US11621081B1 (en) * 2018-11-13 2023-04-04 Iqvia Inc. System for predicting patient health conditions
CN113039578A (zh) * 2018-11-16 2021-06-25 医科达有限公司 利用深度神经网络的实时运动监测
US11361434B2 (en) 2019-01-25 2022-06-14 Otonexus Medical Technologies, Inc. Machine learning for otitis media diagnosis
KR102391817B1 (ko) * 2019-02-18 2022-04-29 주식회사 아이도트 딥 러닝 시스템
CN111582434A (zh) * 2019-02-18 2020-08-25 韩国宝之铂株式会社 深度学习系统
CN111582434B (zh) * 2019-02-18 2023-10-17 韩国宝之铂株式会社 深度学习系统
WO2020171321A1 (fr) * 2019-02-18 2020-08-27 주식회사 아이도트 Système d'apprentissage profond
KR20200100388A (ko) * 2019-02-18 2020-08-26 주식회사 아이도트 딥 러닝 시스템
US10593431B1 (en) 2019-06-03 2020-03-17 Kpn Innovations, Llc Methods and systems for causative chaining of prognostic label classifications
US11468363B2 (en) 2019-07-03 2022-10-11 Kpn Innovations, Llc. Methods and systems for classification to prognostic labels using expert inputs
WO2023081183A1 (fr) * 2021-11-03 2023-05-11 Liveramp, Inc. Apprentissage vertical fractionné à confidentialité différentielle (dp)

Also Published As

Publication number Publication date
AU7749100A (en) 2001-05-10
WO2001026026A3 (fr) 2002-05-16

Similar Documents

Publication Publication Date Title
WO2001026026A2 (fr) Diagnostic local et reseaux de neurones de formation a distance pour diagnostic medical
US5839438A (en) Computer-based neural network system and method for medical diagnosis and interpretation
JP3231810B2 (ja) ニューラル・ネットワークを用いた鑑別診断支援方法
CN102395975B (zh) 临床决策支持系统和方法
US7428323B2 (en) Method and system for automatic diagnosis of possible brain disease
CN110114834A (zh) 用于医疗程序的深度学习医疗系统和方法
CN108784655A (zh) 针对医疗患者的快速评估和后果分析
US20050105788A1 (en) Methods and apparatus for processing image data to aid in detecting disease
CN106372390A (zh) 一种基于深度卷积神经网络的预防肺癌自助健康云服务系统
Yoon et al. Medical image analysis using artificial intelligence
JP6503535B1 (ja) 医用画像をａｉの判断で症状度合いごと画像に表示する診断方法。
CN109791804A (zh) 对cad系统个性化以提供cad系统推荐的置信水平指示的方法和部件
Zhang et al. Hybrid graph convolutional network for semi-supervised retinal image classification
CN111815643A (zh) 基于人工智能的copd评估
CN113808738A (zh) 一种基于自识别影像的疾病识别系统
Dashti et al. An expert system to diagnose spinal disorders
Iftikhar Iqra Naz, Anmol Zahra, and Syeda zainab Yousuf Zaidi. 2022. Report Generation of Lungs Diseases From Chest X-Ray Using NLP”
Tang et al. Detection of COVID-19 using deep convolutional neural network on chest X-ray (CXR) images
CN116844733B (zh) 一种基于人工智能的医疗数据完整性分析方法
Wang et al. Deep learning for automatic identification of nodule morphology features and prediction of lung cancer
CN117322865B (zh) 基于深度学习的颞下颌关节盘移位mri检查诊断系统
Woodard et al. Performance assessment for radiologists interpreting screening mammography
Innocent et al. Type 2 fuzzy representations of lung scans to predict pulmonary emboli
Jabbar et al. Deep learning based classification of wrist cracks from X-ray imaging
Ferrero et al. Detection of breast lesions in medical digital imaging using neural networks

Legal Events

Date Code Title Description
AK Designated states

Kind code of ref document: A2

Designated state(s): AE AG AL AM AT AU AZ BA BB BG BR BY BZ CA CH CN CR CU CZ DE DK DM DZ EE ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MA MD MG MK MN MW MX MZ NO NZ PL PT RO RU SD SE SG SI SK SL TJ TM TR TT TZ UA UG UZ VN YU ZA ZW

AL Designated countries for regional patents

Kind code of ref document: A2

Designated state(s): GH GM KE LS MW MZ SD SL SZ TZ UG ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE BF BJ CF CG CI CM GA GN GW ML MR NE SN TD TG

121 Ep: the epo has been informed by wipo that ep was designated in this application
DFPE Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101)
AK Designated states

Kind code of ref document: A3

Designated state(s): AE AG AL AM AT AU AZ BA BB BG BR BY BZ CA CH CN CR CU CZ DE DK DM DZ EE ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MA MD MG MK MN MW MX MZ NO NZ PL PT RO RU SD SE SG SI SK SL TJ TM TR TT TZ UA UG UZ VN YU ZA ZW

AL Designated countries for regional patents

Kind code of ref document: A3

Designated state(s): GH GM KE LS MW MZ SD SL SZ TZ UG ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE BF BJ CF CG CI CM GA GN GW ML MR NE SN TD TG

REG Reference to national code

Ref country code: DE

Ref legal event code: 8642

122 Ep: pct application non-entry in european phase
NENP Non-entry into the national phase

Ref country code: JP

DPE2 Request for preliminary examination filed before expiration of 19th month from priority date (pct application filed from 20040101)