  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.
