Abstract
The need for non-invasive estimation of the fecundity of Onchocerca nodules has increasingly become more important for testing drug effectiveness. The current diagnostic methodology includes nodulectomy, which can be performed at the end of the treatment but is insufficient to track the nodule changes during the treatment. Further, it is an expensive, time-consuming, and skill-required procedure. In recent studies, a non-invasive approach such as thermal imaging that captures the thermal signature of onchocerca nodules has shown the potential to detect the presence of female onchocerca worms when combined with machine learning. In this paper, we extended this research to propose automated algorithms to determine the fecundity of worms in onchocerca nodules. When the proposed algorithms were evaluated on a prospective blind set of 47 nodules (30 subjects), it resulted in a high AUC of 0.838 in distinguishing onchocerca worms with fecundity.
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Acknowledgements
This publication is based on research funded by the Bill & Melinda Gates Foundation [INV-007341]. The findings and conclusions contained within are those of the authors and do not necessarily reflect the positions or policies of the Bill & Melinda Gates Foundation. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission. We thank Dr. Jonathan Arm for his guidance in the project. We thank Edem and Donkor from UHAS Ghana for their support in data collection, imaging, and ground truth assessment.
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Dedhiya, R. et al. (2022). Non-invasive Thermal Imaging for Estimation of the Fecundity of Live Female Onchocerca Worms. In: Kakileti, S.T., et al. Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery. MIABID AIIIMA 2022 2022. Lecture Notes in Computer Science, vol 13602. Springer, Cham. https://doi.org/10.1007/978-3-031-19660-7_10
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DOI: https://doi.org/10.1007/978-3-031-19660-7_10
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