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

Nicola Rossberg; Celina L. Li; Stefan Andersson-Engels; Barry O'Sullivan; Katarzyna Komolibus; Andrea Visentin

doi:10.1117/12.3017001

18 June 2024 Explainable machine learning for the multiclass classification of diffuse reflectance spectroscopy signals in orthopaedic applications

Nicola Rossberg, Celina L. Li, Stefan Andersson-Engels, Barry O'Sullivan, Katarzyna Komolibus, Andrea Visentin

Author Affiliations +

Proceedings Volume 13011, Data Science for Photonics and Biophotonics; 130110B (2024) https://doi.org/10.1117/12.3017001
Event: SPIE Photonics Europe, 2024, Strasbourg, France

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nicola Rossberg, Celina L. Li, Stefan Andersson-Engels, Barry O'Sullivan, Katarzyna Komolibus, and Andrea Visentin "Explainable machine learning for the multiclass classification of diffuse reflectance spectroscopy signals in orthopaedic applications", Proc. SPIE 13011, Data Science for Photonics and Biophotonics, 130110B (18 June 2024); https://doi.org/10.1117/12.3017001

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