# US 11,896,408 B2 · Automated patient modeling and positioning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,896,408 B2 - Document ID: US11896408B2 - Status: Granted - Kind: B2 - Application number: 17/525,488 - Filed: 2021-11-12 - Published: 2023-05-18 - Granted: 2024-02-13 - Earliest claimed: 2021-11-12 - Assignees: Shanghai United Imaging Intelligence Co., Ltd. - Applicants: Shanghai United Imaging Intelligence Co., LTD. - Inventors: Meng Zheng; Abhishek Sharma; Srikrishna Karanam; Ziyan Wu - Web page: https://patentgrep.com/patent/US11896408B2 ## CPC - A61B 6/04: Positioning of patients; Tiltable beds or the like - A61B 6/0407: {Supports, e.g. tables or beds, for the body or parts of the body} - G06F 18/24: Classification techniques - G06N 3/045: Combinations of networks - G06N 3/08: Learning methods - G06N 20/20: Ensemble learning - G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80) - G06T 7/73: using feature-based methods - G06V 40/103: {Static body considered as a whole, e.g. static pedestrian or occupant recognition} - A61B 6/032: {Transmission computed tomography [CT]} - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30004: Biomedical image processing - G06T 2207/30196: Human being; Person - G06V 10/462: {Salient features, e.g. scale invariant feature transforms [SIFT]} - G06V 10/806: {of extracted features} - 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) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Automated patient positioning and modelling includes a hardware processor to obtain image data from an imaging sensor, classify the image data, using a first machine learning model, as a patient pose based on one or more pre-defined protocols for patient positioning, provide a confidence score based on the classification of the image data and if the confidence score is less than a pre-determined value, re-classify the image data using a second machine learning model; or if the confidence score is greater than a pre-determined value, identify the image data as corresponding to a patient pose based on one or more pre-defined protocols for patient positioning during a scan procedure. ## Claims ### Claim 1 A method for automated patient positioning and modelling, the method comprising using a hardware processor to: obtain image data from an imaging sensor; classify the image data, using a first machine learning model, as a patient pose based on one or more pre-defined protocols for patient positioning; provide a confidence score based on the classification of the image data; and if the confidence score is less than a pre-determined value, re-classify the image data using a second machine learning model; or if the confidence score is greater than a pre-determined value, identify the image data as corresponding to a patient pose based on the one or more pre-defined protocols for patient positioning. ### Claim 2 (depends on 1) The method according to claim 1 further comprising initiating a scan corresponding to the one or more pre-defined protocols for patient positioning when the image data is identified as corresponding to the patient pose based on the one or more pre-defined protocols for patient positioning. ### Claim 3 (depends on 1) The method according to claim 1 wherein the first machine learning model is an ensemble model and the second machine learning model is a deep convolutional neural network model. ### Claim 4 (depends on 1) The method according to claim 1, wherein the image data is patient pose image data. ### Claim 5 (depends on 1) The method according to claim 1, further comprising providing a ranking of confidence scores and enabling a selection of a pose associated with a confidence score. ### Claim 6 (depends on 5) The method according to claim 5, further comprising comparing the selected pose to a required pose of the pre-defined protocol; and confirming the selected pose as corresponding to the pose of the pre-defined protocol and beginning the scan; or provide pose adjustment instructions if the selected pose is not confirmed. ### Claim 7 A system for automated patient positions and modelling, comprising: an imaging sensor; and a hardware processor configured to receive image data from the imaging sensor, wherein the hardware processor is further configured to: classify the image data, using a first machine learning model, as a patient pose based on one or more pre-defined protocols for patient positioning; provide a confidence score based on the classification of the image data; and if the confidence score is less than a pre-determined value, re-classifying the image data using a second machine learning model; or if the confidence score is greater than a pre-determined value, identify the image data as corresponding to a patient pose based on the one or more pre-defined protocols for patient positioning. ### Claim 8 (depends on 7) The system according to claim 7, wherein the hardware processor is further configured to initiate a scan corresponding to the one or more pre-defined protocols for patient positioning when the image data is identified as corresponding to the patient pose based on the one or more pre-defined protocols for patient positioning. ### Claim 9 (depends on 7) The system according to claim 7, wherein the first machine learning model is an ensemble model and the second machine learning model is a deep convolutional neural network model. ### Claim 10 (depends on 7) The system according to claim 7, wherein the image data is patient pose image data. ### Claim 11 (depends on 7) The system according to claim 7, wherein the hardware processor is further configured to output via a user interface a ranking of confidence scores and enable a selection of a pose associated with a confidence score. ### Claim 12 (depends on 11) The system according to claim 11, wherein the hardware processor is further configured to compare the selected pose to a required pose of the pre-defined protocol; and confirm the selected pose as corresponding to the pose of the pre-defined protocol and begin the scan; or provide pose adjustment instructions if the selected pose is not confirmed. ### Claim 13 A computer program product comprising a non-transitory computer-readable medium having machine-readable instructions stored thereon, which when executed by a computer causes the computer to: obtain image data from an imaging sensor; classifying the image data, using a first machine learning model as a patient pose based on one or more pre-defined protocols for patient positioning; providing a confidence score based on the classification of the image data; and if the confidence score is less than a pre-determined value, re-classifying the image data using a second machine learning model; or if the confidence score is greater than a pre-determined value, identify the image data as corresponding to a patient pose based on the one or more pre-defined protocols for patient positioning. ### Claim 14 (depends on 13) The computer program product according to claim 13, further comprising execution of the machine-readable instructions by the computer to automatically begin a scan according to the one or more pre-defined scanning protocol when the correct patient pose is selected.