Otesteanu, Corin F; Caldelari, Reto; Heussler, Volker; Sznitman, Raphael (2024). Machine learning for predicting Plasmodium liver stage development in vitro using microscopy imaging. Computational and structural biotechnology journal, 24, pp. 334-342. Elsevier 10.1016/j.csbj.2024.04.029
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Malaria, a significant global health challenge, is caused by Plasmodium parasites. The Plasmodium liver stage plays a pivotal role in the establishment of the infection. This study focuses on the liver stage development of the model organism Plasmodium berghei, employing fluorescent microscopy imaging and convolutional neural networks (CNNs) for analysis. Convolutional neural networks have been recently proposed as a viable option for tasks such as malaria detection, prediction of host-pathogen interactions, or drug discovery. Our research aimed to predict the transition of Plasmodium-infected liver cells to the merozoite stage, a key development phase, 15 hours in advance. We collected and analyzed hourly imaging data over a span of at least 38 hours from 400 sequences, encompassing 502 parasites. Our method was compared to human annotations to validate its efficacy. Performance metrics, including the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, were evaluated on an independent test dataset. The outcomes revealed an AUC of 0.873, a sensitivity of 84.6%, and a specificity of 83.3%, underscoring the potential of our CNN-based framework to predict liver stage development of P. berghei. These findings not only demonstrate the feasibility of our methodology but also could potentially contribute to the broader understanding of parasite biology.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research 10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory 08 Faculty of Science > Department of Biology > Institute of Cell Biology 08 Faculty of Science > Department of Biology > Institute of Cell Biology > Malaria |
UniBE Contributor: |
Otesteanu, Corin-Felix, Caldelari, Reto, Heussler, Volker, Sznitman, Raphael |
Subjects: |
500 Science > 570 Life sciences; biology 600 Technology > 610 Medicine & health |
ISSN: |
2001-0370 |
Publisher: |
Elsevier |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
07 May 2024 14:34 |
Last Modified: |
07 May 2024 14:34 |
Publisher DOI: |
10.1016/j.csbj.2024.04.029 |
PubMed ID: |
38690550 |
Uncontrolled Keywords: |
Deep learning Malaria Microscopy imaging Neural networks |
BORIS DOI: |
10.48350/196429 |
URI: |
https://boris.unibe.ch/id/eprint/196429 |
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