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. 2022 Oct 17;107(5):1047-1054.
doi: 10.4269/ajtmh.22-0276. Print 2022 Nov 14.

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

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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

Brice Meulah et al. Am J Trop Med Hyg. .

Abstract

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.

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Figures

Figure 1.
Figure 1.
(A) Schistoscope 5.0 device (right) connected to a computer monitor (left), showing an image of a digitally screened sample. (B) Schistoscope 5.0 operated by a laboratory technician in the field. (C) Digital image of a urine filtered membrane showing several Schistosoma eggs captured with the Schistoscope 5.0 (4× objective). The red circle indicates a S. haematobium egg, the blue circle indicates a S. mansoni egg. (D) Image of a urine filtered membrane with several S. haematobium eggs captured by a camera attached to a conventional microscope (10× objective). This figure appears in color at www.ajtmh.org.
Figure 2.
Figure 2.
Flow chart of urine sample collection and analysis comparing conventional microscopy with semiautomated and fully automated digital microscopy.
Figure 3.
Figure 3.
Correlation in Schistosoma haematobium egg counts per 10 mL of urine on a Log10 scale on samples collected at baseline screening. Negative data points are excluded. (A) Semiautomated digital microscopy versus conventional microscopy (n = 133, r = 0.90, P < 0.001). (B) Fully automated digital microscopy versus conventional microscopy (n = 145, r = 0.80, P < 0.001). (C) Semiautomated versus fully automated digital microscopy (n = 137, r = 0.80, P < 0.001). The depicted solid line indicates y = x. This figure appears in color at www.ajtmh.org.
Figure 4.
Figure 4.
Bland–Altman plots showing the level of agreement between (A) conventional microscopy and semiautomated digital microscopy counts and (B) conventional microscopy and fully automated digital microscopy counts. This figure appears in color at www.ajtmh.org.

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