# US 12,112,844 B2 · Machine learning for automatic detection of intracranial hemorrhages with uncertainty measures from medical images > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,112,844 B2 - Document ID: US12112844B2 - Status: Granted - Kind: B2 - Application number: 17/249,783 - Filed: 2021-03-12 - Published: 2022-09-15 - Granted: 2024-10-08 - Earliest claimed: 2021-03-12 - Assignees: Siemens Healthineers AG - Applicants: Siemens Healthineers AG - Inventors: Eli Gibson; Bogdan Georgescu; Pascal Ceccaldi; Youngjin Yoo; Jyotipriya Das; Thomas Re; Eva Eibenberger; Andrei Chekkoury; Barbara Brehm; Thomas Flohr; Dorin Comaniciu; Pierre-Hugo Trigan - Web page: https://patentgrep.com/patent/US12112844B2 ## CPC - G16H 30/40: for processing medical images, e.g. editing - G06N 20/00: Machine learning - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/30: for calculating health indices; for individual health risk assessment - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Systems and method for performing a medical imaging analysis task for making a clinical decision are provided. One or more input medical images of a patient are received. A medical imaging analysis task is performed from the one or more input medical images using a machine learning based network. The machine learning based network generates a probability score associated with the medical imaging analysis task. An uncertainty measure associated with the probability score is determined. A clinical decision is made based on the probability score and the uncertainty measure. ## Claims ### Claim 1 A computer-implemented method comprising: receiving one or more input medical images of a patient; performing a medical imaging analysis task from the one or more input medical images using a machine learning based network, the machine learning based network generating a probability score associated with the medical imaging analysis task; determining an uncertainty measure representing an error associated with the probability score by: selecting a calibration function comprising a fixed point defined according to a user selected threshold at which probability scores are totally uncertain; applying the calibration function to the probability score, and calculating an entropy of the probability score as the uncertainty measure based on results of the applied calibration function; and making a clinical decision based on the probability score and the uncertainty measure. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the medical imaging analysis task comprises at least one of detection, subtyping, or segmentation of an intracranial hemorrhage of the patient. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein making a clinical decision based on the probability score and the uncertainty measure comprises: stratifying the patient into one of a plurality of patient groups based on the probability score and the uncertainty measure. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, wherein the medical imaging analysis task comprises detection of an intracranial hemorrhage of the patient, and the plurality of patient groups comprises a high confidence positive detection patient group, a high confidence negative detection patient group, and a low confidence patient group. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein making a clinical decision based on the probability score and the uncertainty measure comprises: determining whether to treat the patient based on the probability score and the uncertainty measure. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein making a clinical decision based on the probability score and the uncertainty measure comprises: determining whether to perform a clinical test on the patient based on the probability score and the uncertainty measure. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein making a clinical decision based on the probability score and the uncertainty measure comprises: prioritizing a worklist of a radiologist based on the probability score and the uncertainty measure. ### Claim 8 An apparatus comprising: means for receiving one or more input medical images of a patient; means for performing a medical imaging analysis task from the one or more input medical images using a machine learning based network, the machine learning based network generating a probability score associated with the medical imaging analysis task; means for determining an uncertainty measure representing an error associated with the probability score by: selecting a calibration function comprising a fixed point defined according to a user selected threshold at which probability scores are totally uncertain; applying the calibration function to the probability score, and calculating an entropy of the probability score as the uncertainty measure based on results of the applied calibration function; and means for making a clinical decision based on the probability score and the uncertainty measure. ### Claim 9 (depends on 8) The apparatus of claim 8, wherein the means for making a clinical decision based on the probability score and the uncertainty measure comprises: means for stratifying the patient into one of a plurality of patient groups based on the probability score and the uncertainty measure. ### Claim 10 (depends on 9) The apparatus of claim 9, wherein the medical imaging analysis task comprises detection of an intracranial hemorrhage of the patient, and the plurality of patient groups comprises a high confidence positive detection patient group, a high confidence negative detection patient group, and a low confidence patient group. ### Claim 11 A non-transitory computer readable medium storing computer program instructions, the computer program instructions when executed by a processor cause the processor to perform operations comprising: receiving one or more input medical images of a patient; performing a medical imaging analysis task from the one or more input medical images using a machine learning based network, the machine learning based network generating a probability score associated with the medical imaging analysis task; determining an uncertainty measure representing an error associated with the probability score by: selecting a calibration function comprising a fixed point defined according to a user selected threshold at which probability scores are totally uncertain; applying the calibration function to the probability score, and calculating an entropy of the probability score as the uncertainty measure based on results of the applied calibration function; and making a clinical decision based on the probability score and the uncertainty measure. ### Claim 12 (depends on 11) The non-transitory computer readable medium of claim 11, wherein the medical imaging analysis task comprises at least one of detection, subtyping, or segmentation of an intracranial hemorrhage of the patient. ### Claim 13 (depends on 11) The non-transitory computer readable medium of claim 11, wherein making a clinical decision based on the probability score and the uncertainty measure comprises: determining whether to treat the patient based on the probability score and the uncertainty measure. ### Claim 14 (depends on 11) The non-transitory computer readable medium of claim 11, wherein making a clinical decision based on the probability score and the uncertainty measure comprises: determining whether to perform a clinical test on the patient based on the probability score and the uncertainty measure. ### Claim 15 (depends on 11) The non-transitory computer readable medium of claim 11, wherein making a clinical decision based on the probability score and the uncertainty measure comprises: prioritizing a worklist of a radiologist based on the probability score and the uncertainty measure.