# US 2025/0095355 A1 · Bone fracture detection and classification > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0095355 A1 - Document ID: US20250095355A1 - Status: Published application - Kind: A1 - Application number: 18/960,785 - Filed: 2024-11-26 - Published: 2025-03-20 - Earliest claimed: 2018-11-26 - Assignees: metamorphosis GmbH - Applicants: metamorphosis GmbH - Inventors: Arno BLAU - Web page: https://patentgrep.com/patent/US20250095355A1 ## CPC - G06V 10/95: {structured as a network, e.g. client-server architectures} - G06T 7/0012: {Biomedical image inspection} - G06T 7/13: Edge detection - G06T 7/75: {involving models} - G06V 10/44: Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components - G06T 2207/10116: X-ray image - G06T 2207/30008: Bone - G06V 2201/033: of skeletal patterns - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract Method and apparatus are provided for assisting with bone fracture detection. In particular, image data of a medical image is received in a processing unit from a device, which may be an imaging device, a data detection device or an image storage device. A bone structure is identified in the medical image. A fracture line in the identified bone structure is determined. A bone feature, which may include a portion of an outline of the identified bone structure, a point of the fracture line on an outline of the identified bone structure, a relative displacement of bone parts of the identified bone structure, or a combination thereof is detected. The bone feature may be classified and a corresponding output generated. ## Claims ### Claim 1 A device for assisting with bone fracture detection, the device comprising a processing unit configured to: receive image data of a medical image from a device selected from the group consisting of a C-arm based X-ray device, a diagnostic X-ray device, a computer tomography device, a magnet resonance device, an ultrasound device, a data detection device or an image storage device; identify a bone structure in the medical image; utilize a neural net for detecting a bone fracture based on a bone feature of the bone structure, wherein the neural net is trained on the basis of a multiplicity of images including at least one of medical images and simulated medical images of bones of interest, wherein the bone fracture detection is selected from the group consisting of detection of a point of a fracture line on an outline of the identified bone structure, detection of the fracture line, detection of a displacement of bone parts relative to each other, or a combination thereof; and generate a corresponding output based on the bone fracture detection of the neural net. ### Claim 2 (depends on 1) The device of claim 1, wherein the neural net is further trained with information from a data base that is selected from a group consisting of a bone model, possible fracture lines and their likelihood, possible bone fragmentations and their likelihood, a classification of bone fractures or a combination thereof. ### Claim 3 (depends on 1) The device of claim 1, wherein the output includes information of the likely location of the detected bone fracture. ### Claim 4 (depends on 1) The device of claim 1, wherein the multiplicity of images shows bones with the same type of fracture. ### Claim 5 (depends on 1) The device of claim 1, wherein the processing unit is further configured to provide information about secondary fractures that typically occur in connection with the bone fracture detection, even though such a secondary fracture may not be visible in the received medical image. ### Claim 6 (depends on 1) The device of claim 1, further comprising an input device, wherein the processing unit is further configured to receive a user-prompted input from the input device, wherein the user-prompted input is selected from a group consisting of bone structure identification, bone outline detection, fracture line detection, fracture line starting point detection, bone part displacement detection, bone fracture classification, or a combination thereof. ### Claim 7 A method of assisting with bone fracture classification, the method comprising: receiving image data of a medical image from a device selected from the group consisting of a C-arm based X-ray device, a diagnostic X-ray device, a computer tomography device, a magnet resonance device, an ultrasound device, a data detection device or an image storage device; identifying a bone structure in the medical image; utilizing a neural net to detect a bone fracture based on a bone feature of the bone structure, wherein the neural net is trained on the basis of a multiplicity of images including at least one of medical images and simulated medical images of bones of interest, wherein the bone fracture detection is selected from the group consisting of detection of a point of a fracture line on an outline of the identified bone structure, detection of the fracture line, detection of a displacement of bone parts relative to each other, or a combination thereof; and generating a corresponding output based on the bone fracture detection of the neural net. ### Claim 8 (depends on 7) The method of claim 7, wherein the neural net is trained with information from a data base that is selected from a group consisting of a bone model, possible fracture lines and their likelihood, possible bone fragmentations and their likelihood, a classification of bone fractures or a combination thereof. ### Claim 9 (depends on 7) The method of claim 7, wherein the output includes information of the likely location of the detected bone fracture. ### Claim 10 (depends on 7) The method of claim 7, wherein the multiplicity of images shows bones with the same type of fracture. ### Claim 11 (depends on 7) The method of claim 7, further comprising providing information about secondary fractures that typically occur in connection with the bone fracture detection, even though such a secondary fracture may not be visible in the received medical image. ### Claim 12 (depends on 7) The method of claim 7, further comprising receiving a user-prompted input by means of an input device, wherein the user-prompted input is selected from the group consisting of bone structure identification, bone outline detection, fracture line detection, fracture line starting point detection, bone part displacement detection, and bone fracture classification. ### Claim 13 A non-transitory, computer-readable memory storing a set of instructions that, when executed on a processing unit, performs a method of assisting with bone fracture classification comprising: receiving image data of a medical image from a device selected from the group consisting of a C-arm based X-ray device, a diagnostic X-ray device, a computer tomography device, a magnet resonance device, an ultrasound device, a data detection device or an image storage device; identifying a bone structure in the medical image; utilizing a neural net to detect a bone fracture based on a bone feature of the bone structure, wherein the neural net is trained on the basis of a multiplicity of images including at least one of medical images and simulated medical images of bones of interest, wherein the bone fracture detection is selected from the group consisting of detection of a point of a fracture line on an outline of the identified bone structure, detection of the fracture line, detection of a displacement of bone parts relative to each other, or a combination thereof; and generating a corresponding output based on the bone fracture detection of the neural net. ### Claim 14 (depends on 13) The non-transitory, computer-readable memory of claim 13, wherein the neural net is trained with information from a data base that is selected from a group consisting of a bone model, possible fracture lines and their likelihood, possible bone fragmentations and their likelihood, a classification of bone fractures or a combination thereof. ### Claim 15 (depends on 13) The non-transitory, computer-readable memory of claim 13, wherein the output includes information of the likely location of the detected bone fracture. ### Claim 16 (depends on 13) The non-transitory, computer-readable memory of claim 13, wherein the multiplicity of images shows bones with the same type of fracture. ### Claim 17 (depends on 13) The non-transitory, computer-readable memory of claim 13, wherein the set of instruction that, when executed on a processing unit, performs a method further comprising providing information about secondary fractures that typically occur in connection with the bone fracture detection, even though such a secondary fracture may not be visible in the received medical image. ### Claim 18 (depends on 13) The non-transitory, computer-readable memory of claim 13, wherein the set of instruction that, when executed on a processing unit, performs a method further comprising receiving a user-prompted input by means of an input device, wherein the user-prompted input is selected from the group consisting of bone structure identification, bone outline detection, fracture line detection, fracture line starting point detection, bone part displacement detection, and bone fracture classification.