Evaluation of Non-Invasive Thermal Imaging for detection of Viability of Onchocerciasis worms

TL;DR

This study introduces a novel non-invasive method using thermal imaging and machine learning to assess the viability of onchocerciasis worms, potentially replacing invasive procedures for drug efficacy evaluation.

Contribution

First-ever application of thermal imaging combined with machine learning for non-invasive viability detection of onchocerciasis worms.

Findings

Achieved an AUC of 0.85 in classifying viable worms.

Developed a thermal imaging protocol with pre-processing and feature extraction.

Created classifiers capable of accurately detecting worm viability.

Abstract

Onchocerciasis is causing blindness in over half a million people in the world today. Drug development for the disease is crippled as there is no way of measuring effectiveness of the drug without an invasive procedure. Drug efficacy measurement through assessment of viability of onchocerca worms requires the patients to undergo nodulectomy which is invasive, expensive, time-consuming, skill-dependent, infrastructure dependent and lengthy process. In this paper, we discuss the first-ever study that proposes use of machine learning over thermal imaging to non-invasively and accurately predict the viability of worms. The key contributions of the paper are (i) a unique thermal imaging protocol along with pre-processing steps such as alignment, registration and segmentation to extract interpretable features (ii) extraction of relevant semantic features (iii) development of accurate classifiers for detecting the existence of viable worms in a nodule. When tested on a prospective test data of 30 participants with 48 palpable nodules, we achieved an Area Under the Curve (AUC) of 0.85.