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Evaluation of Non-Invasive Thermal Imaging for Detection of Viability of Onchocerciasis Worms
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. Clinical Relevance- This is the first ever research effort of using thermal imaging in the assessment of viability of onchocerca worms and it resulted in a very high specificity>95% which makes it a promising modality to pursue further.
Insights
This study introduces a non-invasive method using thermal imaging and machine learning to assess the viability of onchocerca worms, crucial for drug development against onchocerciasis (river blindness). This approach avoids invasive procedures, offering a more accessible and efficient diagnostic tool.
Area of Science:
- Medical Imaging
- Machine Learning
- Parasitology
Background:
- Onchocerciasis causes significant global blindness.
- Current drug efficacy assessment for onchocerciasis relies on invasive nodulectomy.
- This invasive method is costly, time-consuming, and requires specialized resources.
Purpose of the Study:
- To develop a non-invasive method for predicting the viability of onchocerca worms.
- To utilize thermal imaging and machine learning for accurate worm viability assessment.
- To overcome limitations of current invasive diagnostic procedures.
Main Methods:
- A novel thermal imaging protocol was established.
- Image pre-processing included alignment, registration, and segmentation.
- Machine learning classifiers were developed to detect viable worms in nodules.
Main Results:
- The study achieved an Area Under the Curve (AUC) of 0.85 on test data.
- High specificity (>95%) was obtained in predicting worm viability.
- The method demonstrated accurate detection of viable worms.
Conclusions:
- Thermal imaging combined with machine learning offers a promising non-invasive approach for assessing onchocerca worm viability.
- This technique could significantly advance drug development for onchocerciasis.
- The high specificity suggests clinical relevance for future diagnostic applications.
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