Title
Validation of artificial intelligence-based digital microscopy for automated detection of Schistosoma haematobium eggs in urine in Gabon
Author
Meulah, Brice (Leiden University Medical Center; CERMEL)
Oyibo, P.O. (TU Delft Team Michel Verhaegen) 
Hoekstra, Pytsje T. (Leiden University Medical Center)
Mour, Paul Alvyn Nguema (CERMEL; Ecole doctorale regionale d’Afrique centrale en infectiologie tropicale de Franceville)
Maloum, Moustapha Nzamba (CERMEL)
Laclong Lontchi, Roméo Aimé (CERMEL)
Honkpehedji, Yabo Josiane (Leiden University Medical Center; CERMEL; Fondation pour la Recherche Scientifique)
Bengtson, Michel (Leiden University Medical Center)
Hokke, Cornelis (Leiden University Medical Center)
Diehl, J.C. (TU Delft Design for Sustainability)
Date
2024
Abstract
Introduction
Schistosomiasis is a significant public health concern, especially in Sub-Saharan Africa. Conventional microscopy is the standard diagnostic method in resource-limited settings, but with limitations, such as the need for expert microscopists. An automated digital microscope with artificial intelligence (Schistoscope), offers a potential solution. This field study aimed to validate the diagnostic performance of the Schistoscope for detecting and quantifying Schistosoma haematobium eggs in urine compared to conventional microscopy and to a composite reference standard (CRS) consisting of real-time PCR and the up-converting particle (UCP) lateral flow (LF) test for the detection of schistosome circulating anodic antigen (CAA).
Methods
Based on a non-inferiority concept, the Schistoscope was evaluated in two parts: study A, consisting of 339 freshly collected urine samples and study B, consisting of 798 fresh urine samples that were also banked as slides for analysis with the Schistoscope. In both studies, the Schistoscope, conventional microscopy, real-time PCR and UCP-LF CAA were performed and samples with all the diagnostic test results were included in the analysis. All diagnostic procedures were performed in a laboratory located in a rural area of Gabon, endemic for S. haematobium.
Results
In study A and B, the Schistoscope demonstrated a sensitivity of 83.1% and 96.3% compared to conventional microscopy, and 62.9% and 78.0% compared to the CRS. The sensitivity of conventional microscopy in study A and B compared to the CRS was 61.9% and 75.2%, respectively, comparable to the Schistoscope. The specificity of the Schistoscope in study A (78.8%) was significantly lower than that of conventional microscopy (96.4%) based on the CRS but comparable in study B (90.9% and 98.0%, respectively).
Conclusion
Overall, the performance of the Schistoscope was non-inferior to conventional microscopy with a comparable sensitivity, although the specificity varied. The Schistoscope shows promising diagnostic accuracy, particularly for samples with moderate to higher infection intensities as well as for banked sample slides, highlighting the potential for retrospective analysis in resource-limited settings.
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DOI
https://doi.org/10.1371/journal.pntd.0011967
ISSN
1935-2735
Source
PLoS Neglected Tropical Diseases (Online), 18 (2)
Part of collection
Institutional Repository
Document type
journal article
Rights
© 2024 Brice Meulah, P.O. Oyibo, Pytsje T. Hoekstra, Paul Alvyn Nguema Mour, Moustapha Nzamba Maloum, Roméo Aimé Laclong Lontchi, Yabo Josiane Honkpehedji, Michel Bengtson, Cornelis Hokke, J.C. Diehl, More Authors