Explainable machine learning for the multiclass classification of diffuse reflectance spectroscopy signals in orthopaedic applications

Abstract

Revision total hip arthroplasty suffers from low visibility with intra-body navigation hinging primarily on auditory and tactile cues. Consequently, the risk of surgical injury increases. One proposition to increase surgical precision is integrating an algorithm which classifies encountered tissues based on their reflectance spectra into the surgical tools. Previous works have developed machine learning applications for the automatic, binary, classification of tissue based on diffuse reflectance spectroscopy (DRS) signals and exploratory investigations have successfully integrated DRS probes into surgical devices including surgical drills. However, one problem with these studies is a lack of transparency in the algorithms, which is important to increase practitioners' trust and prevent bias. This study developed four machine learning algorithms which simultaneously classified broadband DRS signals (355 -1850 nm) of six ovine tissue classes. The algorithms were Linear Discriminant Analysis (LDA), Random Forrest, Convolutional Neural Network (CNN), and a Transformer model. Class-wise wavelength importance was visualized using model-based methods to understand classification mechanisms and increase model-explainability. It is concluded that CNNs hold the potential for successful initial device design and medical integration.