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Affordable artificial intelligence-based digital pathology for neglected tropical diseases: A proof-of-concept for the detection of soil-transmitted helminths and Schistosoma mansoni eggs in Kato-Katz stool thick smears

BACKGROUND: With the World Health Organization’s (WHO) publication of the 2021–2030 neglected tropical diseases (NTDs) roadmap, the current gap in global diagnostics became painfully apparent. Improving existing diagnostic standards with state-of-the-art technology and artificial intelligence has th...

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Autores principales: Ward, Peter, Dahlberg, Peter, Lagatie, Ole, Larsson, Joel, Tynong, August, Vlaminck, Johnny, Zumpe, Matthias, Ame, Shaali, Ayana, Mio, Khieu, Virak, Mekonnen, Zeleke, Odiere, Maurice, Yohannes, Tsegaye, Van Hoecke, Sofie, Levecke, Bruno, Stuyver, Lieven J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9258839/
https://www.ncbi.nlm.nih.gov/pubmed/35714140
http://dx.doi.org/10.1371/journal.pntd.0010500
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author Ward, Peter
Dahlberg, Peter
Lagatie, Ole
Larsson, Joel
Tynong, August
Vlaminck, Johnny
Zumpe, Matthias
Ame, Shaali
Ayana, Mio
Khieu, Virak
Mekonnen, Zeleke
Odiere, Maurice
Yohannes, Tsegaye
Van Hoecke, Sofie
Levecke, Bruno
Stuyver, Lieven J.
author_facet Ward, Peter
Dahlberg, Peter
Lagatie, Ole
Larsson, Joel
Tynong, August
Vlaminck, Johnny
Zumpe, Matthias
Ame, Shaali
Ayana, Mio
Khieu, Virak
Mekonnen, Zeleke
Odiere, Maurice
Yohannes, Tsegaye
Van Hoecke, Sofie
Levecke, Bruno
Stuyver, Lieven J.
author_sort Ward, Peter
collection PubMed
description BACKGROUND: With the World Health Organization’s (WHO) publication of the 2021–2030 neglected tropical diseases (NTDs) roadmap, the current gap in global diagnostics became painfully apparent. Improving existing diagnostic standards with state-of-the-art technology and artificial intelligence has the potential to close this gap. METHODOLOGY/PRINCIPAL FINDINGS: We prototyped an artificial intelligence-based digital pathology (AI-DP) device to explore automated scanning and detection of helminth eggs in stool prepared with the Kato-Katz (KK) technique, the current diagnostic standard for diagnosing soil-transmitted helminths (STHs; Ascaris lumbricoides, Trichuris trichiura and hookworms) and Schistosoma mansoni (SCH) infections. First, we embedded a prototype whole slide imaging scanner into field studies in Cambodia, Ethiopia, Kenya and Tanzania. With the scanner, over 300 KK stool thick smears were scanned, resulting in total of 7,780 field-of-view (FOV) images containing 16,990 annotated helminth eggs (Ascaris: 8,600; Trichuris: 4,083; hookworms: 3,623; SCH: 684). Around 90% of the annotated eggs were used to train a deep learning-based object detection model. From an unseen test set of 752 FOV images containing 1,671 manually verified STH and SCH eggs (the remaining 10% of annotated eggs), our trained object detection model extracted and classified helminth eggs from co-infected FOV images in KK stool thick smears, achieving a weighted average precision (± standard deviation) of 94.9% ± 0.8% and a weighted average recall of 96.1% ± 2.1% across all four helminth egg species. CONCLUSIONS/SIGNIFICANCE: We present a proof-of-concept for an AI-DP device for automated scanning and detection of helminth eggs in KK stool thick smears. We identified obstacles that need to be addressed before the diagnostic performance can be evaluated against the target product profiles for both STH and SCH. Given that these obstacles are primarily associated with the required hardware and scanning methodology, opposed to the feasibility of AI-based results, we are hopeful that this research can support the 2030 NTDs road map and eventually other poverty-related diseases for which microscopy is the diagnostic standard.
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spelling pubmed-92588392022-07-07 Affordable artificial intelligence-based digital pathology for neglected tropical diseases: A proof-of-concept for the detection of soil-transmitted helminths and Schistosoma mansoni eggs in Kato-Katz stool thick smears Ward, Peter Dahlberg, Peter Lagatie, Ole Larsson, Joel Tynong, August Vlaminck, Johnny Zumpe, Matthias Ame, Shaali Ayana, Mio Khieu, Virak Mekonnen, Zeleke Odiere, Maurice Yohannes, Tsegaye Van Hoecke, Sofie Levecke, Bruno Stuyver, Lieven J. PLoS Negl Trop Dis Research Article BACKGROUND: With the World Health Organization’s (WHO) publication of the 2021–2030 neglected tropical diseases (NTDs) roadmap, the current gap in global diagnostics became painfully apparent. Improving existing diagnostic standards with state-of-the-art technology and artificial intelligence has the potential to close this gap. METHODOLOGY/PRINCIPAL FINDINGS: We prototyped an artificial intelligence-based digital pathology (AI-DP) device to explore automated scanning and detection of helminth eggs in stool prepared with the Kato-Katz (KK) technique, the current diagnostic standard for diagnosing soil-transmitted helminths (STHs; Ascaris lumbricoides, Trichuris trichiura and hookworms) and Schistosoma mansoni (SCH) infections. First, we embedded a prototype whole slide imaging scanner into field studies in Cambodia, Ethiopia, Kenya and Tanzania. With the scanner, over 300 KK stool thick smears were scanned, resulting in total of 7,780 field-of-view (FOV) images containing 16,990 annotated helminth eggs (Ascaris: 8,600; Trichuris: 4,083; hookworms: 3,623; SCH: 684). Around 90% of the annotated eggs were used to train a deep learning-based object detection model. From an unseen test set of 752 FOV images containing 1,671 manually verified STH and SCH eggs (the remaining 10% of annotated eggs), our trained object detection model extracted and classified helminth eggs from co-infected FOV images in KK stool thick smears, achieving a weighted average precision (± standard deviation) of 94.9% ± 0.8% and a weighted average recall of 96.1% ± 2.1% across all four helminth egg species. CONCLUSIONS/SIGNIFICANCE: We present a proof-of-concept for an AI-DP device for automated scanning and detection of helminth eggs in KK stool thick smears. We identified obstacles that need to be addressed before the diagnostic performance can be evaluated against the target product profiles for both STH and SCH. Given that these obstacles are primarily associated with the required hardware and scanning methodology, opposed to the feasibility of AI-based results, we are hopeful that this research can support the 2030 NTDs road map and eventually other poverty-related diseases for which microscopy is the diagnostic standard. Public Library of Science 2022-06-17 /pmc/articles/PMC9258839/ /pubmed/35714140 http://dx.doi.org/10.1371/journal.pntd.0010500 Text en © 2022 Ward et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ward, Peter
Dahlberg, Peter
Lagatie, Ole
Larsson, Joel
Tynong, August
Vlaminck, Johnny
Zumpe, Matthias
Ame, Shaali
Ayana, Mio
Khieu, Virak
Mekonnen, Zeleke
Odiere, Maurice
Yohannes, Tsegaye
Van Hoecke, Sofie
Levecke, Bruno
Stuyver, Lieven J.
Affordable artificial intelligence-based digital pathology for neglected tropical diseases: A proof-of-concept for the detection of soil-transmitted helminths and Schistosoma mansoni eggs in Kato-Katz stool thick smears
title Affordable artificial intelligence-based digital pathology for neglected tropical diseases: A proof-of-concept for the detection of soil-transmitted helminths and Schistosoma mansoni eggs in Kato-Katz stool thick smears
title_full Affordable artificial intelligence-based digital pathology for neglected tropical diseases: A proof-of-concept for the detection of soil-transmitted helminths and Schistosoma mansoni eggs in Kato-Katz stool thick smears
title_fullStr Affordable artificial intelligence-based digital pathology for neglected tropical diseases: A proof-of-concept for the detection of soil-transmitted helminths and Schistosoma mansoni eggs in Kato-Katz stool thick smears
title_full_unstemmed Affordable artificial intelligence-based digital pathology for neglected tropical diseases: A proof-of-concept for the detection of soil-transmitted helminths and Schistosoma mansoni eggs in Kato-Katz stool thick smears
title_short Affordable artificial intelligence-based digital pathology for neglected tropical diseases: A proof-of-concept for the detection of soil-transmitted helminths and Schistosoma mansoni eggs in Kato-Katz stool thick smears
title_sort affordable artificial intelligence-based digital pathology for neglected tropical diseases: a proof-of-concept for the detection of soil-transmitted helminths and schistosoma mansoni eggs in kato-katz stool thick smears
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9258839/
https://www.ncbi.nlm.nih.gov/pubmed/35714140
http://dx.doi.org/10.1371/journal.pntd.0010500
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