Can artificial intelligence help decision-making in arthroscopy? Part 2: The IA-RTRHO model – a decision-making aid for long head of the biceps diagnoses in small rotator cuff tears

Abstract

IntroductionThe possible applications of artificial intelligence (AI) in orthopedic surgery are promising. Deep learning can be utilized in arthroscopic surgery due to the video signal used by computer vision. The intraoperative management of the long head of biceps (LHB) tendon is the subject of a long-standing controversy. The main objective of this study was to model a diagnostic AI capable of determining the healthy or pathological state of the LHB on arthroscopic images. The secondary objective was to create a second diagnostic AI model based on arthroscopic images and the medical, clinical and imaging data of each patient, to determine the healthy or pathological state of the LHB. HypothesisThe hypothesis of this study was that it was possible to construct an AI model from operative arthroscopic images to aid in the diagnosis of the healthy or pathological state of the LHB, and its analysis would be superior to a human analysis. Materials and methodsProspective clinical and imaging data from 199 patients were collected and associated with images from a validated protocoled arthroscopic video analysis, called “ground truth”, made by the operating surgeon. A model based on a convolutional neural network (CNN) modeled via transfer learning on the Inception V3 model was built for the analysis of arthroscopic images. This model was then coupled to MultiLayer Perceptron (MLP), integrating clinical and imaging data. Each model was trained and tested using supervised learning. ResultsThe accuracy of the CNN in diagnosing the healthy or pathological state of the LHB was 93.7% in learning and 80.66% in generalization. Coupled with the clinical data of each patient, the accuracy of the model assembling the CNN and MLP were respectively 77% and 58% in learning and in generalization. ConclusionThe AI model built from a CNN manages to determine the healthy or pathological state of the LHB with an accuracy rate of 80.66%. An increase in input data to limit overfitting, and the automation of the detection phase by a Mask-R-CNN are ways of improving the model. This study is the first to assess the ability of an AI to analyze arthroscopic images, and its results need to be confirmed by further studies on this subject. Level of evidenceIII Diagnostic study.