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Performance Evaluation of the Schistoscope 5.0 for (Semi-)automated Digital Detection and Quantification of Schistosoma haematobium Eggs in Urine: A Field-based Study in Nigeria
Conventional microscopy is the standard procedure for the diagnosis of schistosomiasis, despite its limited sensitivity, reliance on skilled personnel, and the fact that it is error prone. Here, we report the performance of the innovative (semi-)automated Schistoscope 5.0 for optical digital detecti...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The American Society of Tropical Medicine and Hygiene
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9709021/ https://www.ncbi.nlm.nih.gov/pubmed/36252803 http://dx.doi.org/10.4269/ajtmh.22-0276 |
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author | Meulah, Brice Oyibo, Prosper Bengtson, Michel Agbana, Temitope Lontchi, Roméo Aimé Laclong Adegnika, Ayola Akim Oyibo, Wellington Hokke, Cornelis Hendrik Diehl, Jan Carel van Lieshout, Lisette |
author_facet | Meulah, Brice Oyibo, Prosper Bengtson, Michel Agbana, Temitope Lontchi, Roméo Aimé Laclong Adegnika, Ayola Akim Oyibo, Wellington Hokke, Cornelis Hendrik Diehl, Jan Carel van Lieshout, Lisette |
author_sort | Meulah, Brice |
collection | PubMed |
description | Conventional microscopy is the standard procedure for the diagnosis of schistosomiasis, despite its limited sensitivity, reliance on skilled personnel, and the fact that it is error prone. Here, we report the performance of the innovative (semi-)automated Schistoscope 5.0 for optical digital detection and quantification of Schistosoma haematobium eggs in urine, using conventional microscopy as the reference standard. At baseline, 487 participants in a rural setting in Nigeria were assessed, of which 166 (34.1%) tested S. haematobium positive by conventional microscopy. Captured images from the Schistoscope 5.0 were analyzed manually (semiautomation) and by an artificial intelligence (AI) algorithm (full automation). Semi- and fully automated digital microscopy showed comparable sensitivities of 80.1% (95% confidence interval [CI]: 73.2–86.0) and 87.3% (95% CI: 81.3–92.0), but a significant difference in specificity of 95.3% (95% CI: 92.4–97.4) and 48.9% (95% CI: 43.3–55.0), respectively. Overall, estimated egg counts of semi- and fully automated digital microscopy correlated significantly with the egg counts of conventional microscopy (r = 0.90 and r = 0.80, respectively, P < 0.001), although the fully automated procedure generally underestimated the higher egg counts. In 38 egg positive cases, an additional urine sample was examined 10 days after praziquantel treatment, showing a similar cure rate and egg reduction rate when comparing conventional microscopy with semiautomated digital microscopy. In this first extensive field evaluation, we found the semiautomated Schistoscope 5.0 to be a promising tool for the detection and monitoring of S. haematobium infection, although further improvement of the AI algorithm for full automation is required. |
format | Online Article Text |
id | pubmed-9709021 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The American Society of Tropical Medicine and Hygiene |
record_format | MEDLINE/PubMed |
spelling | pubmed-97090212022-11-30 Performance Evaluation of the Schistoscope 5.0 for (Semi-)automated Digital Detection and Quantification of Schistosoma haematobium Eggs in Urine: A Field-based Study in Nigeria Meulah, Brice Oyibo, Prosper Bengtson, Michel Agbana, Temitope Lontchi, Roméo Aimé Laclong Adegnika, Ayola Akim Oyibo, Wellington Hokke, Cornelis Hendrik Diehl, Jan Carel van Lieshout, Lisette Am J Trop Med Hyg Research Article Conventional microscopy is the standard procedure for the diagnosis of schistosomiasis, despite its limited sensitivity, reliance on skilled personnel, and the fact that it is error prone. Here, we report the performance of the innovative (semi-)automated Schistoscope 5.0 for optical digital detection and quantification of Schistosoma haematobium eggs in urine, using conventional microscopy as the reference standard. At baseline, 487 participants in a rural setting in Nigeria were assessed, of which 166 (34.1%) tested S. haematobium positive by conventional microscopy. Captured images from the Schistoscope 5.0 were analyzed manually (semiautomation) and by an artificial intelligence (AI) algorithm (full automation). Semi- and fully automated digital microscopy showed comparable sensitivities of 80.1% (95% confidence interval [CI]: 73.2–86.0) and 87.3% (95% CI: 81.3–92.0), but a significant difference in specificity of 95.3% (95% CI: 92.4–97.4) and 48.9% (95% CI: 43.3–55.0), respectively. Overall, estimated egg counts of semi- and fully automated digital microscopy correlated significantly with the egg counts of conventional microscopy (r = 0.90 and r = 0.80, respectively, P < 0.001), although the fully automated procedure generally underestimated the higher egg counts. In 38 egg positive cases, an additional urine sample was examined 10 days after praziquantel treatment, showing a similar cure rate and egg reduction rate when comparing conventional microscopy with semiautomated digital microscopy. In this first extensive field evaluation, we found the semiautomated Schistoscope 5.0 to be a promising tool for the detection and monitoring of S. haematobium infection, although further improvement of the AI algorithm for full automation is required. The American Society of Tropical Medicine and Hygiene 2022-11 2022-10-17 /pmc/articles/PMC9709021/ /pubmed/36252803 http://dx.doi.org/10.4269/ajtmh.22-0276 Text en © The author(s) https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) 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 Meulah, Brice Oyibo, Prosper Bengtson, Michel Agbana, Temitope Lontchi, Roméo Aimé Laclong Adegnika, Ayola Akim Oyibo, Wellington Hokke, Cornelis Hendrik Diehl, Jan Carel van Lieshout, Lisette Performance Evaluation of the Schistoscope 5.0 for (Semi-)automated Digital Detection and Quantification of Schistosoma haematobium Eggs in Urine: A Field-based Study in Nigeria |
title | Performance Evaluation of the Schistoscope 5.0 for (Semi-)automated Digital Detection and Quantification of Schistosoma haematobium Eggs in Urine: A Field-based Study in Nigeria |
title_full | Performance Evaluation of the Schistoscope 5.0 for (Semi-)automated Digital Detection and Quantification of Schistosoma haematobium Eggs in Urine: A Field-based Study in Nigeria |
title_fullStr | Performance Evaluation of the Schistoscope 5.0 for (Semi-)automated Digital Detection and Quantification of Schistosoma haematobium Eggs in Urine: A Field-based Study in Nigeria |
title_full_unstemmed | Performance Evaluation of the Schistoscope 5.0 for (Semi-)automated Digital Detection and Quantification of Schistosoma haematobium Eggs in Urine: A Field-based Study in Nigeria |
title_short | Performance Evaluation of the Schistoscope 5.0 for (Semi-)automated Digital Detection and Quantification of Schistosoma haematobium Eggs in Urine: A Field-based Study in Nigeria |
title_sort | performance evaluation of the schistoscope 5.0 for (semi-)automated digital detection and quantification of schistosoma haematobium eggs in urine: a field-based study in nigeria |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9709021/ https://www.ncbi.nlm.nih.gov/pubmed/36252803 http://dx.doi.org/10.4269/ajtmh.22-0276 |
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