Artificial intelligence intra-operative surgical guidance system and method of use
The inventive subject matter is directed to a computing platform configured to execute one or more automated artificial intelligence models, wherein the one or more automated artificial intelligence models includes a neural network model, wherein the one or more automated artificial intelligence models are trained on a plurality of radiographic images from a data layer to detect a plurality of anatomical structures or a plurality of hardware, wherein at least one anatomical structure is a pelvic teardrop and a symphysis pubis joint; detecting at a plurality of anatomical structures in a radiographic image of a subject, wherein the plurality of anatomical structures are detected by the computing platform by the step of classifying the radiographic image with reference to a subject good side radiographic image; and constructing a graphical representation of data, wherein the graphical representation is a subject specific functional pelvis grid; the subject specific functional pelvis grid generated based upon the anatomical structures detected by the computing platform in the radiographic image. Various types of functional grids can be generated based on the situation detected.
1. An artificial intelligence assisted total hip arthroplasty comprising:
providing a computing platform comprised of an at least one image processing algorithm for the classification of a plurality of intra-operative medical images, the computing platform configured to execute one or more automated artificial intelligence models, wherein the one or more automated artificial intelligence models comprises a neural network model, wherein the one or more automated artificial intelligence models are trained on data from a data layer to identify a plurality of anatomical structures;
receiving a preoperative radiographic image of a subject;
detecting the plurality of anatomical structures in the preoperative radiographic image of the subject;
generating a subject specific functional pelvis grid from the plurality of anatomical structures detected in the preoperative radiographic image of the subject,
receiving an intraoperative anteroposterior pelvis radiographic image of the subject;
identifying an at least one anatomical landmark in the intraoperative anteroposterior pelvis radiographic image, whereby the computing platform performs the step of: selecting a situation specific grid selected from the group consisting of: functional pelvis grid, spinopelvic grid, level pelvis grid, reference grid, neck cut grid, reamer depth grid, center of rotation grid, cup grid, leg length and offset grid, and femur abduction grid; and registering the selected grid to the anatomical landmark in the intraoperative anteroposterior pelvis radiographic image.
2. The method of claim 1 , comprising the step of registering a subject good side radiographic image to the intraoperative anteroposterior pelvis radiographic image.
3. The method of claim 1 , wherein the data layer includes at least a plurality of fluoroscopic surgical images, wherein the automated artificial intelligence models are trained to calculate intra-operative surgical decision risks, further comprising the step of: arranging a graphical representation within a visual display to provide situational awareness to a user as a function of the classification of surgical outcomes.
4. The method of claim 1 , further comprising the step of:
constructing a graphical representation of data, wherein the graphical representation is an anatomy map of an entire anatomical region; an anatomy map generated based upon the anatomical landmark detected by the computing platform.
5. An artificial intelligence assisted total hip arthroplasty comprising:
providing a computing platform comprised of an at least one image processing algorithm for the classification of a plurality of intra-operative medical images, the computing platform configured to execute one or more automated artificial intelligence models, wherein the one or more automated artificial intelligence models comprises a neural network model, wherein the one or more automated artificial intelligence models are trained on data from a data layer to identify a plurality of hardware;
receiving a preoperative radiographic image of a subject;
detecting a plurality of anatomical structures in the preoperative radiographic image of the subject;
generating a subject specific functional pelvis grid from the plurality of anatomical structures detected in the preoperative radiographic image of the subject,
receiving an intraoperative anteroposterior pelvis radiographic image of the subject;
identifying an at least one hardware in the intraoperative anteroposterior pelvis radiographic image, whereby the computing platform performs the step of: selecting a situation specific grid selected from the group consisting of: functional pelvis grid, spinopelvic grid, level pelvis grid, reference grid, neck cut grid, reamer depth grid, center of rotation grid, cup grid, leg length and offset grid, and femur abduction grid; and registering the selected grid to the anatomical landmark in the intraoperative anteroposterior pelvis radiographic image.
6. The method of claim 5 , comprising the step of registering a subject good side radiographic image to the intraoperative anteroposterior pelvis radiographic image.
7. The method of claim 5 , wherein the data layer includes at least a plurality of fluoroscopic surgical images, wherein the automated artificial intelligence models are trained to calculate intra-operative surgical decision risks, further comprising the step of: arranging a graphical representation within a visual display to provide situational awareness to a user as a function of the classification of surgical outcomes.
8. The method of claim 5 , further comprising the step of:
constructing a graphical representation of data, wherein the graphical representation is an anatomy map of an entire anatomical region; an anatomy map generated based upon the anatomical landmark detected by the computing platform.