Patent ID: 11877801
Assignee: MEDICREA INTERNATIONAL
Field: Medical technology (Instruments)
Classification: CPC A  G | IPC A

Claim 21:
22. A computer-implemented method of training a predictive model for predicting a surgical outcome a spinal surgery of a subject, the method comprising:
inputting, into a computer system, one or more preoperative inputs and one or more postoperative inputs relating to one or more previous subjects, wherein each of the one or more preoperative inputs and the one or more postoperative inputs relating to one or more previous subjects comprise one or more preoperative medical images and one or more postoperative medical images of a spine of the one or more previous subjects and one or more preoperative non-imaging data inputs and one or more postoperative non-imaging data inputs of the one or more previous subjects;
determining, using the computer system, one or more measurements from the inputted one or more preoperative medical images and one or more postoperative medical images of the spine of the one or more previous subjects, wherein the one or more measurements comprise a position of one or more vertebrae of the spine of the one or more previous subjects;
determining, using the computer system, one or more preoperative spinopelvic parameters and one or more postoperative spinopelvic parameters of the spine of the one or more previous subjects based at least in part on the one or more determined measurements, wherein the one or more preoperative spinopelvic parameters and the one or more postoperative spinopelvic parameters of the one or more previous subjects comprise one or more of lumbar lordosis (LL), preoperative thoracic kyphosis (TK), pelvic incidence (PI), pelvic tilt (PT), or sagittal vertical axis (SVA) for one or more vertebrae;
applying, using the computer system, a data compression technique to the determined one or more preoperative spinopelvic parameters and the one or more postoperative spinopelvic parameters of the one or more previous subjects to obtain compressed one or more preoperative spinopelvic parameters and one or more postoperative spinopelvic parameters of the one or more previous subjects;
filtering, using the computer system, the compressed one or more preoperative spinopelvic parameters and the one or more postoperative spinopelvic parameters of the one or more previous subjects, wherein the filtering comprises filtering out one or more compressed preoperative spinopelvic parameters and one or more postoperative spinopelvic parameters of the one or more previous subjects comprising a noise level above a predetermined threshold;
training, using the computer system, one or more predictive models based at least in part on the filtered compressed one or more preoperative spinopelvic parameters and one or more postoperative spinopelvic parameters of the one or more previous subjects, the one or more preoperative non-imaging data inputs of the one or more previous subjects, and the one or more postoperative non-imaging data inputs of the one or more previous subjects; and
testing, using the computer system, the trained one or more predicted models on one or more test preoperative inputs and one or more test postoperative inputs relating to one or more test subjects, wherein each of the one or more test preoperative inputs and the one or more test postoperative inputs relating to one or more test subjects comprise one or more test preoperative medical images and one or more test postoperative medical images of a spine of the one or more test subjects, wherein the one or more test subjects are separate from the one or more previous subjects,
wherein the trained and tested one or more predictive models are configured to predict the surgical outcome of the spinal surgery of the subject based at least in part on one or more spinopelvic parameters derived from one or more preoperative medical images of a spine of the subject,
wherein the computer system comprises a computer processor and an electronic storage medium.