# US 12,178,560 B2 · Efficient artificial intelligence analysis of radiographic images with combined predictive modeling > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,178,560 B2 - Document ID: US12178560B2 - Status: Granted - Kind: B2 - Application number: 17/134,990 - Filed: 2020-12-28 - Published: 2021-07-01 - Granted: 2024-12-31 - Earliest claimed: 2019-12-27 - Assignees: Vetology Innovations, LLC - Applicants: Vetology Innovations, LLC - Inventors: Seth Wallack; Ruben Venegas; Ariel Ayaviri Omonte; Pratheev Sabaratnam Sreetharan; Yuan-Ching Spencer Teng - Web page: https://patentgrep.com/patent/US12178560B2 ## CPC - A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems} - A61B 5/0077: {Devices for viewing the surface of the body, e.g. camera, magnifying lens} - A61B 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging - G06F 18/2321: using statistics or function optimisation, e.g. modelling of probability density functions - G06F 18/23213: with fixed number of clusters, e.g. K-means clustering - G06T 7/0012: {Biomedical image inspection} - G06T 7/11: Region-based segmentation - G06V 10/763: {Non-hierarchical techniques, e.g. based on statistics of modelling distributions} - G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS - G16H 30/40: for processing medical images, e.g. editing - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G06T 2207/10116: X-ray image - G06T 2207/20132: Image cropping - G06T 2207/30004: Biomedical image processing - G06V 2201/03: Recognition of patterns in medical or anatomical images - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract A system, an image analyzer and a method for diagnosing a presence of a disease or a condition in an image of a subject, for example, a veterinary patient, are provided including: classifying the image to a body region, and obtaining a classified, labeled, cropped, and oriented sub-image; directing the sub-image to artificial intelligence processor for obtaining an evaluation result, and comparing the evaluation result to a database library of evaluation results and matched written templates or a dataset cluster to obtain at least one cluster result; measuring the distance between the cluster result and the evaluation result to obtain at least one cluster diagnosis; and assembling the cluster diagnosis and the matched written templates to obtain a report to display the report to a radiologist. These system, analyzer and method are achieved in greatly reduced lengths of time and are useful for cost and time savings. ## Claims ### Claim 1 A method for identifying and diagnosing a presence of a disease or a condition in at least one image of a subject, the method comprising: classifying the image to at least one body region of a plurality of body regions, labelling and orientating the image using a sub-image processor to obtain a classified, labeled and oriented sub-image based on the body region, the body region defining a veterinary species; directing the sub-image to at least one artificial intelligence (AI) evaluation processor based on the body region to obtain an evaluation result, and comparing the evaluation result to a database having a plurality of dataset clusters computed based on previous evaluation results and matched written templates corresponding to the classified image, or at least one dataset cluster, to obtain at least one cluster result; measuring the distance between the cluster result and the evaluation result to obtain at least one cluster diagnosis using a synthesis processor; and assembling the cluster diagnosis to obtain a report thereby identifying and diagnosing the presence of the disease or the condition in the subject. ### Claim 2 (depends on 1) The method according to claim 1 further comprising prior to classifying, obtaining at least one image or one data point of the subject. ### Claim 3 (depends on 1) The method according to claim 1 further comprising prior to comparing, compiling the dataset cluster using a clustering tool selected from: K-means clustering, Mean shift clustering, Density-Based Spatial Clustering, Expectation-Maximization (EM) Clustering, and Agglomerative Hierarchical Clustering. ### Claim 4 (depends on 3) The method according to claim 3 compiling further comprises obtaining, processing, evaluating, and constructing a library of a plurality of identified and diagnosed dataset and corresponding medical reports selected from: radiology reports, laboratory reports, histology reports, physical exam reports, and microbiology reports, with a plurality of known diseases or conditions. ### Claim 5 (depends on 4) The method according to claim 4, processing further comprises classifying the plurality of identified and diagnosed dataset images to the body regions to obtain a plurality of classified dataset images, and orienting and cropping the plurality of classified dataset images to obtain a plurality of oriented, cropped and labeled dataset sub-images. ### Claim 6 (depends on 5) The method according to claim 5, evaluating further comprises directing the plurality of oriented, cropped and labeled dataset sub-images and corresponding medical reports to at least one AI processor to obtain at least one diagnosed AI processor result. ### Claim 7 (depends on 6) The method according to claim 6 directing further comprises classifying the plurality of oriented, cropped and labeled dataset sub-images and corresponding medical reports with at least one variable selected from: age, species, breed, weight, sex, and location. ### Claim 8 (depends on 7) The method according to claim 7, constructing the library of the plurality of identified and diagnosed dataset images further comprises creating at least one cluster of the diagnosed AI processor result to obtain at least one AI processor exemplar result and thereby compiling the dataset cluster. ### Claim 9 (depends on 8) The method according to claim 8 further comprising assigning at least one cluster diagnosis to the cluster of the diagnosed AI processor result. ### Claim 10 (depends on 9) The method according to claim 9, assigning cluster diagnosis further comprises adding reports within the cluster and/or additional information written by an evaluator. ### Claim 11 (depends on 1) The method according to claim 1, measuring further comprises determining a distance between the cluster result and at least one selected from: the evaluation result, the dataset cluster, and a centroid of the cluster result. ### Claim 12 (depends on 11) The method according to claim 11 further comprises selecting a result from: a case within the cluster that has the nearest match, a result from another case in the cluster, and a centroid case. ### Claim 13 (depends on 12) The method according to claim 12 selecting further comprises adding result information of the cluster result by an evaluator to the report generated from the cluster. ### Claim 14 (depends on 11) The method according to claim 11 further comprising editing the report by removing a portion of the report of the cluster diagnosis which is less than a threshold of prevalence in a plurality of reports in the cluster. ### Claim 15 (depends on 14) The method according to claim 14, wherein the threshold of prevalence designated by an evaluator, is selected to be less than 100% and more than 0%. ### Claim 16 (depends on 14) The method according to claim 14 wherein the threshold of prevalence designated by an evaluator, is selected to be less than about 80%. ### Claim 17 (depends on 1) The method according to claim 1, rapidly processing the evaluation result by a diagnostic AI processor to obtain the report. ### Claim 18 (depends on 17) The method according to claim 17, the diagnostic AI processor processing the image to obtain the report in: less than ten minutes. ### Claim 19 (depends on 4) The method according to claim 4, the library of identified and diagnosed dataset images with known diseases and conditions are categorized to at least one of a plurality of animal species. ### Claim 20 (depends on 9) The method according to claim 9 further comprising identifying the diagnosed AI processor result with an identification tag. ### Claim 21 (depends on 3) The method according to claim 3 further comprising selecting and adding the image and/or a medical result of the subject to the dataset cluster. ### Claim 22 A system for diagnosing a presence of a disease or a condition in an image and/or a medical result, of a subject, the system comprising: a receiver to receive an image and/or the medical result of the subject; at least one processor to automatically classify the image to at least one body region of a plurality of body regions, run an image identification and processing algorithm to identify, crop, orient and label at least one body region in the image to obtain a sub-image, the body region defining a veterinary species; a plurality of artificial intelligence processors selected based on the body region to evaluate the sub-image and/or the medical result and obtain an evaluation result; a database having a plurality of dataset clusters computed based on previous evaluation results and matched written templates; and at least one diagnostic artificial intelligence processor to automatically run a cluster algorithm to compare the evaluation result to the database to obtain a cluster result and measure a distance between the cluster result and a previously created cluster result from a specific dataset to obtain a cluster diagnosis, and assemble a report. ### Claim 23 (depends on 22) The system according to claim 22, the diagnostic AI processor automatically rapidly processes the image, and/or the medical result to generate a report. ### Claim 24 (depends on 23) The system according to claim 23, the diagnostic AI processor processes the image and/or the medical result to obtain the report in: less than ten minutes. ### Claim 25 (depends on 22) The system according to claim 22 further comprises a device to display the generated report. ### Claim 26 A method for diagnosing a presence of a disease or a condition in at least one image of a subject, the method comprising: classifying the image to at least one body region of a plurality of body regions, labelling, cropping, and orientating the image to obtain at least one classified, labeled, cropped, and oriented sub-image based on the body region, the body region defining a veterinary species; directing the sub-image to at least one artificial intelligence (AI) processor based on the body region for processing and obtaining an evaluation result, and comparing the evaluation result to a database library having a plurality of dataset clusters computed based on previous evaluation results and matched written templates corresponding to the classified image, or at least one dataset cluster, to obtain at least one cluster result; measuring the distance between the cluster result and the evaluation result to obtain at least one cluster diagnosis; and assembling the cluster diagnosis and the matched written templates to obtain a report and displaying the report to a radiologist thereby identifying and diagnosing the presence of the disease or the condition in the subject. ### Claim 27 (depends on 26) The method according to claim 26 further comprising after displaying, analyzing the report and confirming the presence of the disease or the condition. ### Claim 28 (depends on 27) The method according to claim 27 further comprising editing the written templates. ### Claim 29 (depends on 26) The method according to claim 26, wherein the method of obtaining the report has a process time: less than about 5 minutes, less than about 2 minutes, or less than about 1 minute. ### Claim 30 (depends on 26) The method according to claim 26, wherein the method of obtaining the report has a process time: less than about 10 minutes, less than about 7 minutes, or less than about 6 minutes. ### Claim 31 (depends on 26) The method according to claim 26, processing the sub-image further comprises training the AI processor for diagnosing the presence of the disease or the condition in the image of the subject. ### Claim 32 (depends on 31) The method according to claim 31, training the AI processor further comprises: communicating a library of training images to the AI processor; choosing a training image having the disease or the condition from the library of training images; and comparing the training image to the library of training images thereby training the AI processor. ### Claim 33 (depends on 32) The method according to claim 32, the library of training images comprises positive control training images and negative control training images. ### Claim 34 (depends on 33) The method according to claim 33, the positive control training images have the disease or the condition of the training image. ### Claim 35 (depends on 33) The method according to claim 33, the negative control training images do not have the disease or the condition of the training image. ### Claim 36 (depends on 32) The method according to claim 32, the library of training images further comprises at least one of medical data, metadata, and auxiliary data.