# US 12,004,966 B2 · Systems and methods for using image analysis in superior capsule reconstruction > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,004,966 B2 - Document ID: US12004966B2 - Status: Granted - Kind: B2 - Application number: 18/555,252 - Filed: 2022-04-12 - Published: 2024-03-14 - Granted: 2024-06-11 - Earliest claimed: 2021-04-12 - Assignees: Kaliber Labs Inc. - Applicants: Kaliber Labs Inc. - Inventors: Chandra Jonelagadda - Web page: https://patentgrep.com/patent/US12004966B2 ## CPC - A61B 90/361: {Image-producing devices, e.g. surgical cameras} - A61F 2/4612: {of shoulders} - A61B 1/000096: {using artificial intelligence} - A61B 1/317: for bones or joints, e.g. osteoscopes or arthroscopes - G06T 7/62: of area, perimeter, diameter or volume - G16H 20/40: relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture - A61B 2090/365: {augmented reality, i.e. correlating a live optical image with another image} - A61B 2576/00: Medical imaging apparatus involving image processing or analysis (A61B1/00009, A61B6/52 and A61B8/52 take precedence) - A61F 2002/4633: {for selection of endoprosthetic joints or for pre-operative planning} - G06T 2207/10016: Video; Image sequence - G06T 2207/10068: Endoscopic image - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30008: Bone - G06T 2207/30204: Marker - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - A61F: FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS (dental prosthetics A61C) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract Embodiments provide systems and methods to guide arthroscopic SCR surgery by providing dimensional/shape information on an implanted graft. One example comprises receiving imaging data (ID) contained in a video stream captured by an imaging device at a treatment site (TS) and processing the ID. The ID is analyzed to determine ID that depicts a set of anchor structures (AS) in the TS, determine an orientation attribute of each AS of the set with respect to a reference; and determine a respective scale factor (SF) for portions of ID that depict each AS of the set, the respective SF of each portion of the ID reflecting a size distortion of that portion of the ID with respect to a reference scale. Geometric information is determined for at least a portion of the TS where the set of ASs are located and is used to determine shape and dimensions of the graft. ## Claims ### Claim 1 A computer-implemented method for guiding surgical reconstruction of an injury to a patient's shoulder joint, the method comprising: receiving imaging data contained in a video stream captured by an in-situ imaging device that traverses a treatment site in a shoulder area of the patient; processing image data from an-situ imaging device; analyzing the image data to (i) detect image data that depicts a set of anchor structures in the treatment site, (ii) determine an orientation attribute of each anchor structure of the set with respect to a reference; and (iii) determine a respective scale factor for one or more portions of image data that depict each anchor structure of the set, the respective scale factor of each portion of the image data reflecting a distortion in size of that portion of the image data with respect to a reference scale; determining geometric information for at least a portion of the treatment site where the set of anchor structures are located; and calculating at least a first measurement that represents a physical span between a pair of anchor structures of the set, based at least in part on the orientation attribute of each anchor structure of the set, the respective scale factor of each portion of the image data, and the determined geometric information. ### Claim 2 (depends on 1) The method of claim 1, further comprising calculating at second measurement that represents a physical span between a second pair of anchor structures of the set. ### Claim 3 (depends on 2) The method of claim 2, wherein the first and second measurements are used to determine at least one of location or dimensional information of a graft placed in the treatment site. ### Claim 4 (depends on 3) The method of claim 3, wherein the location information is a position of the graft at the treatment site. ### Claim 5 (depends on 4) The method of claim 4, wherein the position of the graft is with respect to a location of at least one of anchor structures. ### Claim 6 (depends on 3) The method of claim 3, wherein the dimensional information is at least one of a size, width or area of the graft. ### Claim 7 (depends on 3) The method of claim 3, wherein at least one of the location or dimensional information are configured to optimize a postoperative kinematic function of the patient's shoulder. ### Claim 8 (depends on 7) The method of claim 7, wherein the post-operative kinematic function includes at least one of a stability or range of motion of the patient's rotator cuff or shoulder. ### Claim 9 (depends on 7) The method of claim 7, wherein the post-operative kinematic function is that of a glenohumeral junction. ### Claim 10 (depends on 1) The method of claim 1, further comprising: analyzing the image data to determine anatomical characteristics of the treatment site; and determining the reference for the orientation attribute of the set of implant structures based at least in part on the determined anatomical characteristics. ### Claim 11 (depends on 1) The method of claim 1, wherein the injury is a torn rotator cuff and the surgical reconstruction is reconstruction of the patient's superior capsule. ### Claim 12 (depends on 1) The method of claim 1, processing the image data includes processing multiple frames of the video stream; and analyzing the image data includes identifying one or more markers for the target site in a first frame of the video feed, and tracking the one or more markers to one or more subsequent frames of the video feed. ### Claim 13 (depends on 12) The method of claim 12, wherein the one or more markers correspond to locations for the anchor structures. ### Claim 14 (depends on 1) The method of claim 1, wherein the imaging device is an arthroscope. ### Claim 15 (depends on 1) The method of claim 1, wherein the image data is processed using at least one trained computer algorithm. ### Claim 16 (depends on 15) The method of claim 15, wherein processing comprises identifying one or more elements in the image data, wherein said one or more elements comprise one or more of an anatomical structure, a surgical tool, or a pathology. ### Claim 17 (depends on 16) The method of claim 16, wherein processing further comprises: receiving an indication from an operator to select one or more of a plurality of points or an area in said video stream; calculating one or more measurements for said selected area in said video stream; wherein said one or more measurements comprise one or more of a length, a surface area, a circumference, or a volume; and display said measurements on a displaying device intraoperatively to be used by an operator during said arthroscopic procedure. ### Claim 18 (depends on 15) The method of claim 15, wherein the least one trained computer algorithm comprises one or more trained machine learning algorithms, one or more trained deep learning algorithms, or a combination of both. ### Claim 19 (depends on 15) The method of claim 15, wherein the image data is processed using one or more processing modules. ### Claim 20 (depends on 19) The method of claim 19, the one or more processing modules comprise at least one of a video stream decomposition module, a tool recognition module, a tool tip reference sizing module, an anatomy recognition module, a tool tracking module, a landmark establishment module, a distance aggregator module, a video blend module, a pathology detection module, a pathology sizing module, or a radiology imaging module. ### Claim 21 (depends on 19) The method of claim 19, wherein the one or more processing modules comprise trained machine learning algorithms. ### Claim 22 (depends on 21) The method of claim 21, wherein the trained machine learning algorithm is a neural network. ### Claim 23 (depends on 19) The method of claim 19, wherein the processing module is a video stream decomposition module that decomposes said video stream into a series of images. ### Claim 24 (depends on 23) The method of claim 23, wherein the series of images are stored in a memory device. ### Claim 25 (depends on 24) The method of claim 24, wherein the memory device is a non-volatile memory device or a flash drive. ### Claim 26 (depends on 24) The method of claim 24, wherein the memory device is volatile-memory device, a RAM device or an SRAM device. ### Claim 27 A non-transitory computer-readable medium including contents that are configured to cause one or more processors to perform a method comprising: receiving imaging data contained in a video stream captured by an in-situ imaging device that traverses a treatment site in a shoulder area of the patient; processing image data from an-situ imaging device; analyzing the image data to (i) detect image data that depicts a set of anchor structures in the treatment site, (ii) determine an orientation attribute of each anchor structure of the set with respect to a reference; and (iii) determine a respective scale factor for one or more portions of image data that depict each anchor structure of the set, the respective scale factor of each portion of the image data reflecting a distortion in size of that portion of the image data with respect to a reference scale; determining geometric information for at least a portion of the treatment site where the set of anchor structures are located; and calculating at least a first measurement that represents a physical span between a pair of anchor structures of the set, based at least in part on the orientation attribute of each anchor structure of the set, the respective scale factor of each portion of the image data, and the determined geometric information.