Smith + Nephew

United Services Automobile Association (usaa)

Research Institute at Nationwide Children's Hospital

Yangqiu Hu

Stephen Mirdo

Gaetano Calabrese

Jess Witman Gingrich

David Rogers

Nathaniel Milton Lenz

Angela Black Mines

Edward Domanski

Mohammed Rahman

Ting Lu

Peter White

David Lawrence Newsom

Kyle Lonidier

Angela B Mines

A process according to certain embodiments includes generating a distal femur model including an intercondylar surface model, receiving information related to user-selected points on the intercondylar surface model, generating a datum line extending between the points, generating an axis line, and determining an AP axis based upon the axis line. Generating the axis line includes performing an axis line procedure including generating a plurality of planes along the datum line, generating a plurality of contours at intersections between the intercondylar surface model and the planes, generating saddle points at local extrema of the contours, and fitting the axis line to the saddle points. The process may further include generating an updated datum line based upon the axis line, and performing a subsequent iteration of the axis line procedure using the updated datum line.

Interactive anterior-posterior axis determination

Systems, devices, and methods are described for providing patient anatomy models with indications of model accuracy included with the model. Accuracy is determined, for example, by analyzing gradients at tissue boundaries or by analyzing tissue surface curvature in a three-dimensional anatomy model. The determined accuracy is graphically provided to an operator along with the patient model. The overlaid accuracy indications facilitate the operator's understanding of the model, for example by showing areas of the model that may deviate from the modeled patient's actual anatomy.

Systems and methods for evaluating accuracy in a patient model

A system and method for processing occluded aerial images of structures to assess structural damage is disclosed. The system employs a generative model that is trained on data created by adding occlusive features to clear aerial images. Both the clear images and their simulated occluded counterparts (image pairs) are inputted into the generative model as training data, and the model learns to recognize and discriminate patterns between the pairs. This process is fully automated and does not require manual labeling. Following training, the model is capable of receiving real (non-simulated) occluded images and generating a clarified image that can reveal structural details previously not apparent to automatically determine a damage classification for the property.

System and method for assessing structural damage in occluded aerial images

A system and method for capturing and automated processing aerial images of structures to assess structural damage is disclosed. The system comprises a computing system used to obtain images of selected locations associated with known structures at different points in time before and after a natural disaster. The images are used to automatically create three-dimensional models that are used to detect the specific portions of a structure that may be damaged as well as the spatial extent of that damage. In addition, the imagery can be fed into a damage classifier that automatically classifies the degree of damage and generates accurate estimates of repair costs. The system and method may be deployed to quickly assess damage of structures in a disaster area and provide reports of the damage to homeowners and/or insurers.

Intelligent system and method for assessing structural damage using aerial imagery

Embodiments of the present disclosure provide a software program that displays both a volume as images and segmentation results as surface models in 3D. Multiple 2D slices are extracted from the 3D volume. The 2D slices may be interactively rotated by the user to best follow an oblique structure. The 2D slices can 'cut' the surface models from the segmentation so that only half of the models are displayed. The border curves resulting from the cuts are displayed in the 2D slices. The user may click a point on the surface model to designate a landmark point. The corresponding location of the point is highlighted in the 2D slices. A 2D slice can be reoriented such that the line lies in the slice. The user can then further evaluate or refine the landmark points based on both surface and image information.

Surface and image integration for model evaluation and landmark determination

A computing system according to an embodiment includes at least one processor and at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the computing system to determine a plurality of implant size predictions with associated confidence levels based on one or more patient or surgical parameters, wherein each of the implant size predictions identifies a confidence level that a prospective implant of a corresponding size will fit a patient, determine whether a combined confidence level determined based on a subset of the plurality of associated confidence levels is at least a threshold value, and recommend, in response to a determination that the combined confidence level is not at least the threshold value, incorporation of at least one of an additional implant size prediction of the plurality of implant size predictions in the subset or digital templating data to improve an accuracy of an implant size estimation.

Growing community of inventors

Lewis Center, OH, United States of America

Yangqiu Hu