Acoustical Features as Knee Health Biomarkers: A Critical Analysis

TL;DR

This paper critically examines acoustical features as biomarkers for knee health, introducing a causal validation framework that reveals limitations in current machine learning approaches and provides guidance for future research.

Contribution

It presents a novel causal framework for validating acoustical knee biomarkers and demonstrates its application through real-world experiments, highlighting issues like shortcut learning and performance inflation.

Findings

Identified latent issues such as shortcut learning in acoustical knee diagnosis.

Reproduced key results independently, confirming previous findings.

Provided actionable insights to improve future research in acoustical biomarker validation.

Abstract

Acoustical knee health assessment has long promised an alternative to clinically available medical imaging tools, but this modality has yet to be adopted in medical practice. The field is currently led by machine learning models processing acoustical features, which have presented promising diagnostic performances. However, these methods overlook the intricate multi-source nature of audio signals and the underlying mechanisms at play. By addressing this critical gap, the present paper introduces a novel causal framework for validating knee acoustical features. We argue that current machine learning methodologies for acoustical knee diagnosis lack the required assurances and thus cannot be used to classify acoustic features as biomarkers. Our framework establishes a set of essential theoretical guarantees necessary to validate this claim. We apply our methodology to three real-world experiments investigating the effect of researchers' expectations, the experimental protocol and the wearable employed sensor. This investigation reveals latent issues such as underlying shortcut learning and performance inflation. This study is the first independent result reproduction study in the field of acoustical knee health evaluation. We conclude with actionable insights from our findings, offering valuable guidance to navigate these crucial limitations in future research.