Machine learning can predict anterior elevation after reverse total shoulder arthroplasty: A new tool for daily outpatient clinic?

The aim of the present study was to individuate and compare specific machine learning algorithms that could predict postoperative anterior elevation score after reverse shoulder arthroplasty surgery at different time points. Data from 105 patients who underwent reverse shoulder arthroplasty at the same institute have been collected with the purpose of generating algorithms which could predict the target. Twenty-eight features were extracted and applied to two different machine learning techniques: Linear regression and support vector regression (SVR). These two techniques were also compared in order to define to most faithfully predictive. Using the extracted features, the SVR algorithm resulted in a mean absolute error (MAE) of 11.6° and a classification accuracy (PCC) of 0.88 on the test-set. Linear regression, instead, resulted in a MAE of 13.0° and a PCC of 0.85 on the test-set. Our machine learning study demonstrates that machine learning could provide high predictive algorithms for anterior elevation after reverse shoulder arthroplasty. The differential analysis between the utilized techniques showed higher accuracy in prediction for the support vector regression. Level of Evidence III: Retrospective cohort comparison; Computer Modeling.

Predicting the results of rhinoplasty before surgery: Easy noses versus difficult noses.

A major problem for many rhinoplastic surgeons is the ability to predict, before surgery, the difficulty of the procedure (whether the rhinoplasties will be technically easy or technically difficult to perform) and the success rate of the result (whether the rhinoplasty will likely give good results or poor ones).The present paper outlines a systematic approach to nasal analysis, allowing the surgeon to consistently estimate, before surgery, the degree of technical difficulty of each rhinoplasty, as well as predicting its future result in terms of patient satisfaction. This preoperative evaluation is based on the analysis of the skin texture and the osteocartilagenous framework on lateral and frontal views. It allows for the nose to be classified as green (easy), yellow (moderate) or red (difficult), depending on two factors: the degree of surgical difficulty and the expected patient's satisfaction with the result.The essence of the present paper is to introduce a simple, systematic approach to assist the novice rhinoplastic surgeon to assess the complexity, the risks and the expected outcome of a rhinoplasty in the preoperative period, rather than postoperatively.