A surgical system can be configured to obtain image data of a joint that comprises at least a portion of a humerus; segment the image data to determine a shape for a diaphysis of the humerus; based on the determined shape of the diaphysis, determine an estimated pre-morbid shape of the humerus; based on the estimated shape of the humerus, identify one or more bone fragments in the image data; and based on the identified bone fragments in the image data, generate an output.

A system may be configured to dynamically update deployed machine learning models. In some aspects, the system may receive sampled video information, generate first object detection information based on a cloud model and the sampled video information, and generate second object detection information based on a first edge model and the sampled video information. Further, the system may select, based on the first object detection information and the second object detection information, a plurality of training images from the sampled video information, detect motion information corresponding to motion of one or more detected objects within the plurality of training images, generate a plurality of annotated images based at least in part on the first object detection information and the motion information, and generate a second edge model based upon training the first edge model using the plurality of annotated images.

A system may be configured to dynamically update deployed machine learning models. In some aspects, the system may receive sampled video information, generate first object detection information based on a cloud model and the sampled video information, and generate second object detection information based on a first edge model and the sampled video information. Further, the system may select, based on the first object detection information and the second object detection information, a plurality of training images from the sampled video information, detect motion information corresponding to motion of one or more detected objects within the plurality of training images, generate a plurality of annotated images based at least in part on the first object detection information and the motion information, and generate a second edge model based upon training the first edge model using the plurality of annotated images.

Systems, methods, and executable programs for providing lung candidacy information to health care professionals. A method includes receiving three-dimensional image data categorized as lung lobe voxels, airway voxels, or lung fissure voxels. A fissure integrity score is generated for the lung fissure voxels. First perspective transparent views of the categorized lung lobe voxels, the categorized airway voxels, and the categorized lung fissure voxels are generated based on a first point of view. The first perspective view of the lung fissure voxels includes a visual representation of fissure integrity based on the generated fissure integrity scores for the corresponding voxels. A report is generated that includes the generated views. The report is outputted.

Systems, methods, and executable programs for providing lung candidacy information to health care professionals. A method includes receiving three-dimensional image data categorized as lung lobe voxels, airway voxels, or lung fissure voxels. A fissure integrity score is generated for the lung fissure voxels. First perspective transparent views of the categorized lung lobe voxels, the categorized airway voxels, and the categorized lung fissure voxels are generated based on a first point of view. The first perspective view of the lung fissure voxels includes a visual representation of fissure integrity based on the generated fissure integrity scores for the corresponding voxels. A report is generated that includes the generated views. The report is outputted.

A method for reproducing a lumen curve to a given tolerance in at least one image in optical coherence tomography (OCT). Examples of applications include imaging, evaluating and diagnosing biological objects, such as, but not limited to, cardio applications, and being obtained via one or more optical instruments, such as, but not limited to, catheters. The method may include obtaining a set of original points of the curve that correspond to measurements from an optical imaging device. Filtering the set of original points using at least one criteria to obtain a subset of original points. The method may also include determining if the subset of original points is less than a predetermined threshold and adjusting the at least one criteria to increase an amount of original points included in the subset of original points when it is determined that the subset of original points is less than the predetermined threshold.

Various embodiments of the present disclosure are directed towards a method for generating a risk group classification for an African American (AA) patient. The method includes extracting a first plurality of architectural features from a digitized H&E slide image of the AA patient. A risk score for the AA patient is generated based on the first plurality of architectural features, where the risk score is prognostic of overall survival (OS) of the AA patient. The risk group classification is generated for the AA patient, where generating the risk group classification includes classifying the AA patient into either a high risk group or a low risk group based on the risk score, where the high risk group indicates the AA patient will die before a threshold date and the low risk group indicates the AA patient will die after or on the threshold date.

In a method of segmenting a feature in an image, an image product related to the image is provided (102) to a model trained using a machine learning process. An indication of a shape descriptor for the feature in the image is received (104) from the model, based on the image product. The indicated shape descriptor is then used (106) in a model based segmentation, MBS, to initialize the MBS and segment the feature.

In a method of segmenting a feature in an image, an image product related to the image is provided (102) to a model trained using a machine learning process. An indication of a shape descriptor for the feature in the image is received (104) from the model, based on the image product. The indicated shape descriptor is then used (106) in a model based segmentation, MBS, to initialize the MBS and segment the feature.

A system (100) includes a computer readable storage medium (122) with computer executable instructions (124), including: a biophysical simulator component (126) configured to determine a fractional flow reserve value via simulation and a traffic light engine (128) configured to track a user-interaction with the computing system at one or more points of the simulation to determine the fractional flow reserve value. A processor (120) is configured to execute the biophysical simulator component to determine the fractional flow reserve value and configured to execute the traffic light engine to track the user-interaction with respect to determining the fractional flow reserve value and provide a warning in response to determining there is a potential incorrect interaction. A display is configured to display the warning requesting verification to proceed with the simulation from the point, wherein the simulation is resumed only in response to the processor receiving the requested verification.