Is plantar thermography a valid digital biomarker for characterising diabetic foot ulceration risk?

TL;DR

This study explores the potential of plantar thermography as a digital biomarker for diabetic foot ulceration risk, finding strong associations with risk factors but limited predictive power, highlighting challenges in biomarker validation.

Contribution

It demonstrates the association between thermography-based clusters and DFU risk factors, emphasizing the complexity of validating thermography as a predictive biomarker.

Findings

Strong associations between thermography clusters and DFU risk factors.

Poor predictive performance of models based on thermography.

Highlights challenges in standardising digital biomarker validation.

Abstract

Background: In the absence of prospective data on diabetic foot ulcers (DFU), cross-sectional associations with causal risk factors (peripheral neuropathy, and peripheral arterial disease (PAD)) could be used to establish the validity of plantar thermography for DFU risk stratification. Methods: First, we investigated the associations between the intrinsic clusters of plantar thermographic images with several DFU risk factors using an unsupervised deep-learning framework. We then studied associations between obtained thermography clusters and DFU risk factors. Second, to identify those associations with predictive power, we used supervised learning to train Convolutional Neural Network (CNN) regression/classification models that predicted the risk factor based on the thermograph (and visual) input. Findings: Our dataset comprised 282 thermographs from type 2 diabetes mellitus patients (aged 56.31 +- 9.18 years, 51.42 % males). On clustering, we found two overlapping clusters (silhouette score = 0.10, indicating weak separation). There was strong evidence for associations between assigned clusters and several factors related to diabetic foot ulceration such as peripheral neuropathy, PAD, number of diabetes complications, and composite DFU risk prediction scores such as Martins-Mendes, PODUS-2020, and SIGN. However, models predicting said risk factors had poor performances. Interpretation: The strong associations between intrinsic thermography clusters and several DFU risk factors support the validity of using thermography for characterising DFU risk. However, obtained associations did not prove to be predictive, likely due to, spectrum bias, or because thermography and classical risk factors characterise incompletely overlapping portions of the DFU risk construct. Our findings highlight the challenges in standardising ground truths when defining novel digital biomarkers.