Method and system for modeling predictive outcomes of arthroplasty surgical procedures

Abstract

An apparatus includes a processor and a non-transitory memory. The processor is configured to receive pre-operative patient specific data. The pre-operative patient specific data is inputted to a first machine learning model to determine a first predicted post-operative joint performance data output including first predicted post-operative outcome metrics. A reconstruction plan of the joint of the patient is generated based on a medical image of the joint, and at least one arthroplasty surgical parameter obtained from the user. The at least one arthroplasty surgical parameter is inputted into a second machine learning model to determine a second predicted post-operative joint performance data output including second predicted post-operative outcome metrics. The second predicted post-operative joint performance data output is updated to include an arthroplasty surgery recommendation, in response to the user varying the at least one arthroplasty surgical parameter, before the arthroplasty surgery, during the arthroplasty surgery, or both.

1 - 22 . (canceled) 
     
     
         23 . A system, comprising:
 a non-transitory memory storing software instructions;   at least one processor that, when executing the software instructions, is configured to:
 receive pre-operative patient specific data for an arthroplasty surgery to be performed on a joint of a patient;
 wherein the pre-operative patient specific data comprises: 
 (i) a medical history of the patient, 
 (ii) a measured range of movement for at least one type of joint movement of the j oint, and 
 (iii) at least one pain metric associated with the joint; 
 
 receive at least one medical image of the joint obtained from at least one medical imaging procedure performed on the patient; 
 receive at least one arthroplasty surgical parameter;
 wherein the at least one arthroplasty surgical parameter is selected from: 
 (i) at least one implant, 
 (ii) at least one implant size, 
 (iii) at least one arthroplasty surgical procedure, 
 (iv) at least one position for implanting the at least one implant in the j oint, or 
 (v) any combination thereof; 
 
 generate a reconstruction plan of the joint of the patient based at least in part on the at least one medical image of the joint and the at least one arthroplasty surgical parameter; 
 input the pre-operative patient specific data and reconstruction plan data into at least one machine learning model to determine a predicted post-operative joint performance data output at a plurality of post-operative timepoints after surgery;
 wherein the at least one machine learning model is trained to output data comprising a plurality of values for the predicted post-operative joint performance data output at the plurality of post-operative timepoints after surgery, each value is at a particular timepoint of the plurality of post-operative timepoints after surgery; 
 wherein input data to train the at least one machine learning model comprises at least: 
 (i) the pre-operative patient specific data, and 
 
 (ii) the reconstruction plan data; 
 
 instruct to display the reconstruction plan data and the predicted post-operative joint performance data output at the plurality of post-operative timepoints after surgery via a graphical user interface displayed on a display associated with a user on the display to the user; and 
 update the predicted post-operative joint performance data output determined from the at least one machine learning model in response to the user varying any parameter of the reconstruction plan data that is then inputted into the at least one machine learning model, before the arthroplasty surgery, during the arthroplasty surgery, or both. 
   
     
     
         24 . The system of  claim 23 , wherein the at least one processor is configured to receive the pre-operative patient specific data by receiving the pre-operative patient specific data over a communication network from at least one electronic medical resource. 
     
     
         25 . The system of  claim 23 , wherein the at least one medical image comprises at least one of: (a) an X-ray image, (b) a computerized tomography image, (c) a magnetic resonance image, (d) a three-dimensional (3D) image, (e) a 3D medical image generated from multiple X-ray images, (f) a frame of a video, or any combination thereof. 
     
     
         26 . The system of  claim 23 , wherein the at least one predicted post-operative joint performance data at the plurality of post-operative timepoints after surgery are predicted for at least one of: (a) a number of days, (b) a number of months, and (c) a number of years. 
     
     
         27 . The system of  claim 23 , wherein the at least one processor is configured to display the predicted post-operative joint performance data output with recommendations for the at least one arthroplasty surgical parameter. 
     
     
         28 . The system according to  claim 23 , wherein the joint is selected from the group consisting of a hip joint, a knee joint, a shoulder joint, an elbow joint, and an ankle joint. 
     
     
         29 . The system according to  claim 23 , wherein the joint is a shoulder joint. 
     
     
         30 . The system of  claim 29 , wherein the pre-operative patient specific data comprises: (a) patient demographics, (b) a patient diagnosis, (c) a patient comorbidity, (d) a patient medical history, (e) a shoulder active range of motion measure, (f) a patient self-reported measure of pain, function, or both, (g) a patient score based on American Shoulder and Elbow Surgeons Shoulder Score (ASES), (h) a patient score based on Constant Shoulder Score (CSS), or any combination thereof. 
     
     
         31 . The system of  claim 29 , wherein the at least one arthroplasty surgical procedure is selected from the group consisting of an anatomic total shoulder arthroplasty, a reverse total shoulder arthroplasty, deltopectoral technique, and a superior-lateral technique. 
     
     
         32 . The system of  claim 29 , wherein the at least one predicted post-operative joint performance data at the plurality of post-operative timepoints after surgery are selected from the group consisting of an American Shoulder and Elbow (ASES) score, a University of California, Los Angeles (UCLA) patient reported outcome measures score, a constant score, a global shoulder function score, a Visual Analogue Scale (VAS) Pain score, an abduction score, a forward elevation score, and an external rotation score. 
     
     
         33 . The system according to  claim 23 , wherein the at least one processor is further configured to determine from the at least one machine learning model, at least one arthroplasty surgery recommendation to display to the user on the display. 
     
     
         34 . The system according to  claim 23 , wherein the at least one processor is further configured to:
 input the pre-operative patient specific data to at least one second machine learning model to determine a second predicted post-operative joint performance data output at a plurality of second post-operative timepoints after surgery prior to generating the reconstruction plan;
 wherein the at least one second machine learning model is trained to output data comprising a plurality of second values for the second predicted post-operative joint performance data output at the plurality of second post-operative timepoints after surgery, each second value is at each particular second timepoint of the plurality of second post-operative timepoints after surgery; 
 wherein input data to train the at least one second machine learning model comprises at least the pre-operative patient specific data; 
 
 display the second predicted post-operative joint performance data output on the display to the user as a displayed second predicted post-operative joint performance data output; and 
 wherein the at least one processor is further configured to receive from the user, through the graphical user interface displayed on the display, the at least one arthroplasty surgical parameter based on the displayed second predicted post-operative joint performance data output to generate the reconstruction plan. 
 
     
     
         35 . A method, comprising:
 receiving, by at least one processor, pre-operative patient specific data for an arthroplasty surgery to be performed on a joint of a patient;
 wherein the pre-operative patient specific data comprises: 
 (i) a medical history of the patient, 
 (ii) a measured range of movement for at least one type of joint movement of the joint, and 
 (iii) at least one pain metric associated with the joint; 
   receiving, by the at least one processor, at least one medical image of the joint obtained from at least one medical imaging procedure performed on the patient;   receiving, by the at least one processor, at least one arthroplasty surgical parameter; 
 wherein the at least one arthroplasty surgical parameter is selected from: 
 (i) at least one implant, 
 (ii) at least one implant size, 
 (iii) at least one arthroplasty surgical procedure, 
 (iv) at least one position for implanting the at least one implant in the joint, or 
 (v) any combination thereof; 
   generating, by the at least one processor, a reconstruction plan of the joint of the patient based at least in part on the at least one medical image of the joint and the at least one arthroplasty surgical parameter;   inputting, by the at least one processor, the pre-operative patient specific data and reconstruction plan data into at least one machine learning model to determine a predicted post-operative joint performance data output at a plurality of post-operative timepoints after surgery;
 wherein the at least one machine learning model is trained to output data comprising a plurality of values for the predicted post-operative joint performance data output at the plurality of post-operative timepoints after surgery, each value is at a particular timepoint of the plurality of post-operative timepoints after surgery; 
 wherein input data to train the at least one machine learning model comprises at least:
 (i) the pre-operative patient specific data, and 
 (ii) the reconstruction plan data; 
 
   instructing, by the at least one processor, to display the reconstruction plan data and the predicted post-operative joint performance data output at the plurality of post-operative timepoints after surgery via a graphical user interface displayed on a display associated with a user; and   updating, by the at least one processor, the predicted post-operative joint performance data output determined from the at least one machine learning model in response to the user varying any parameter of the reconstruction plan data that is then inputted into the at least one machine learning model, before the arthroplasty surgery, during the arthroplasty surgery, or both.   
     
     
         36 . The method of  claim 35 , wherein receiving the pre-operative patient specific data comprises receiving the pre-operative patient specific data over a communication network from at least one electronic medical resource. 
     
     
         37 . The method of  claim 35 , wherein the at least one medical image comprises at least one of: (a) an X-ray image, (b) a computerized tomography image, (c) a magnetic resonance image, (d) a three-dimensional (3D) image, (e) a 3D medical image generated from multiple X-ray images, (f) a frame of a video, or any combination thereof. 
     
     
         38 . The method of  claim 35 , wherein the at least one predicted post-operative joint performance data at the plurality of post-operative timepoints after surgery are predicted for at least one of: (a) a number of days, (b) a number of months, and (c) a number of years. 
     
     
         39 . The method of  claim 35 , wherein displaying the predicted post-operative joint performance data output comprises displaying the predicted post-operative joint performance data output with recommendations for the at least one arthroplasty surgical parameter. 
     
     
         40 . The method of  claim 35 , wherein the joint is selected from the group consisting of a hip joint, a knee joint, a shoulder joint, an elbow j oint, and an ankle joint. 
     
     
         41 . The method of  claim 35 , wherein the joint is a shoulder joint. 
     
     
         42 . The method of  claim 41 , wherein the pre-operative patient specific data comprises: (a) patient demographics, (b) a patient diagnosis, (c) a patient comorbidity, (d) a patient medical history, (e) a shoulder active range of motion measure, (f) a patient self-reported measure of pain, function, or both, (g) a patient score based on American Shoulder and Elbow Surgeons Shoulder Score (ASES), (h) a patient score based on Constant Shoulder Score (CSS), or any combination thereof. 
     
     
         43 . The method of  claim 41 , wherein the at least one arthroplasty surgical procedure is selected from the group consisting of an anatomic total shoulder arthroplasty, a reverse total shoulder arthroplasty, deltopectoral technique, and a superior-lateral technique. 
     
     
         44 . The method of  claim 41 , wherein the at least one predicted post-operative joint performance data at the plurality of post-operative timepoints after surgery are selected from the group consisting of an American Shoulder and Elbow (ASES) score, a University of California, Los Angeles (UCLA) patient reported outcome measures score, a constant score, a global shoulder function score, a Visual Analogue Scale (VAS) Pain score, an abduction score, a forward elevation score, and an external rotation score. 
     
     
         45 . The method according to  claim 35 , further comprising determining, by the at least one processor, from the at least one machine learning model, at least one arthroplasty surgery recommendation to display to the user on the display. 
     
     
         46 . The method according to  claim 35 , further comprising inputting, by the at least one processor, the pre-operative patient specific data to at least one second machine learning model to determine a second predicted post-operative joint performance data output at a plurality of second post-operative timepoints after surgery prior to generating the reconstruction plan;
 wherein the at least one second machine learning model is trained to output data comprising a plurality of second values for the second predicted post-operative joint performance data output at the plurality of second post-operative timepoints after surgery, each second value is at each particular second timepoint of the plurality of second post-operative timepoints after surgery;   wherein input data to train the at least one second machine learning model comprises at least the pre-operative patient specific data; 
 displaying, by the at least one processor, the second predicted post-operative joint performance data output on the display to the user as a displayed second predicted post-operative joint performance data output; and
 wherein the receiving from the user the at least one arthroplasty surgical parameter comprises receiving through the graphical user interface displayed on the display, the at least one arthroplasty surgical parameter based on the displayed second predicted post-operative joint performance data output for generating the reconstruction plan.