A usability study of an innovative optical device for the diagnosis of schistosomiasis in Nigeria

Bengtson, Michel; Onasanya, Adeola; Oyibo, Prosper; Meulah, Brice; Samenjo, Karl Tondo; Braakman, Ingeborg; Oyibo, Wellington; Diehl, Jan Carel

doi:10.1109/ghtc55712.2022.9911019

Cited by 1 publication

(3 citation statements)

References 9 publications

(14 reference statements)

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“…haematobium . Although the Schistoscope has been fully automated, the aesthetics are currently unsatisfactory [ 20 ]. Furthermore, there is need to make the Schistoscope field-friendly and compatible to very rural settings, including the addition of a power source, improving the user interface and making it more compact and portable.…”

Section: Discussionmentioning

confidence: 99%

“…A tool as such would complement the existing POC-CCA urine test, which has been recommended by the WHO for S. mansoni infections, in settings co-endemic for S. haematobium. Although the Schistoscope has been fully automated, the aesthetics are currently unsatisfactory [20]. Furthermore, there is need to make the Schistoscope field-friendly and compatible to very rural settings, including the addition of a power source, improving the user interface and making it more compact and portable.…”

Section: Plos Neglected Tropical Diseasesmentioning

confidence: 99%

“…The Schistoscope is an automated digital microscope with an integrated AI algorithm to detect S. haematobium eggs in urine samples. It was developed for use at point-of-need and is relatively easy to operate requiring minimal training [19,20]. The Schistoscope was first assessed in Nigeria for diagnosing urogenital schistosomiasis, revealing a high sensitivity but a rather low specificity compared to conventional microscopy [12].…”

Section: Introductionmentioning

confidence: 99%

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Validation of artificial intelligence-based digital microscopy for automated detection of Schistosoma haematobium eggs in urine in Gabon

Meulah,

Oyibo,

Hoekstra

et al. 2024

PLoS Negl Trop Dis

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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. Trial registration NCT04505046 ClinicalTrials.gov.

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Section: Discussionmentioning

confidence: 99%

Section: Plos Neglected Tropical Diseasesmentioning

confidence: 99%