Introduction: Schistosomiasis is a public health concern and there is a need for reliable field-compatible diagnostic methods in endemic settings. The AiDx Assist, an artificial intelligence (AI)-based automated microscope, has shown promising results for the detection of Schistosoma haematobium eggs in urine. It has been further developed to detect Schistosoma mansoni eggs in stool.

Methods: In this study, we evaluated the performance of the AiDx Assist for the detection of S. mansoni eggs in stool samples and further validated the performance of the AiDx Assist for the detection of S. haematobium eggs in urine samples. Additionally, the potential of the AiDx Assist for the detection of other helminths in stool samples was explored. In total, 405 participants from an area endemic for both S. mansoni and S. haematobium provided stool and urine samples which were subjected to AiDx Assist (semi- and fully automated), while conventional microscopy was used as the diagnostic reference.

Results: Only samples with complete test results were included in the final analysis, resulting in 375 stool and 398 urine samples, of which 38.4% and 65.3% showed Schistosoma eggs by conventional microscopy. The collected images of the stool samples were retrospectively examined for other helminth eggs via manual analysis. For the detection of S. mansoni eggs, the sensitivity of the semi-automated AiDx Assist (86.8%) was significantly higher compared to the fully automated AiDx Assist (56.9%) while the specificity was comparable, with 81.4% and 86.8%, respectively. Retrospectively, eggs of Ascaris lumbricoides and Trichuris trichiura were visualized. For the examination of urine samples, a comparable sensitivity in the detection of S. haematobium eggs was found between the semi-and the fully automated modes of the AiDx Assist, showing 94.6% and 91.9%, respectively. Furthermore, the specificity was comparable, with 90.6%and 91.3% respectively.

Discussion: The AiDx Assist met the World Health Organization Target Product Profile criteria in terms of diagnostic accuracy for the detection of S. haematobium eggs in urine samples and performed modestly in the detection of S. mansoni eggs in stool samples. With some further improvements, it has the potential to become a valuable diagnostic tool for screening multiple helminth parasites in stool and urine samples.@en

Community mobilisation is a vital process for raising awareness and increasing participation in healthcare interventions, research, and programmes that require human sample collection and mass management. In this report, we present the community mobilisation approach undertaken for the implementation of the operational mapping and assessment of granular schistosomiasis and soil-transmitted helminths in Ekiti State, Nigeria. The mobilisation was conducted in 177 communities/wards of the 16 local government areas. A total of 15,340 urine and stool samples were collected in 34 days. The efficacy and success of the strategy were evaluated through the following three performance metrics: community compliance rate, the participant response rate at the community level, and the overall compliance response rate of the four most sensitive LGAs. Community compliance was 93.7% as sample collection was denied in nine communities and two other communities demanded the return of the collected samples despite our mobilisation effort because of cultural bias and myths that connect the collection of stool and urine samples to ritual activities in the local context. The participant response rate at the community level was 86.7%. Three of the four sensitive LGAs (based on previous assessment programmes) demonstrated satisfactory compliance rates of 100%, while a response rate of 64.0% was computed for one of the LGAs. We believe our approach contributed to effective community mobilisation and awareness and that the developed model has the potential to improve participation rates in large healthcare assessments and intervention programmes.@en

Community awareness and participation in mass screening is critical for schistosomiasis control. This study assessed the impact of sharing anonymized image-based positive test results on the uptake of screening during community mobilization outreach. We conducted an observational study to compare the population response to standard and image-based strategies in 14 communities
in Abuja, Nigeria. Six hundred and ninety-one (341 females, 350 males) individuals participated in this study. We analyzed the response ratio, relative increase, and sample collection time. The potential treatment uptake and change in social behavior were determined based on a semi-structured questionnaire. The mean response ratio of the image-based strategy was 89.7% representing a
significantly higher ratio than the 27.8%, which was observed under the standard mobilization approach (p ≤ 0.001). The image-based method was associated with 100% of the participants agreeing to provide urine samples, 94% willing to be treated, 89% claiming to have been invited to participate in the study by a friend, and 91% desiring to change a predisposing behavioral habit. These findings indicate that image-based community awareness campaigns may increase the population’s perception about schistosomiasis transmission and treatment. This raises new possibilities for local resource mobilization to expand services in reaching the last mile in schistosomiasis control.@en

In this research, we report on the performance of automated optical digital detection and quantification of Schistosoma haematobium provided by AiDx NTDx multi-diagnostic Assist microscope. Our study was community-based, and a convenient sampling method was used in 17 communities in Abuja Nigeria, based on the disease prevalence information extracted from the baseline database on schistosomiasis, NTD Division, of the Federal Ministry of Health. At baseline, samples from 869 participants were evaluated of which 358 (34.1%) tested S. haematobium positive by the reference diagnostic standard. Registered images from the fully automated (autofocusing, scanning, image registration and processing, AI image analysis and automatic parasite count) AiDx assist microscope were analyzed. The Semi automated (autofocusing, scanning, image registration & processing and manual parasite count) and the fully automated AiDx Assist showed comparable sensitivities and specificities of [90.3%, 98%] and [89%, 99%] respectively. Overall, estimated egg counts of the semi-automated & fully automated AiDx Assist correlated significantly with the egg counts of conventional microscopy (r = 0.93, p ≤ 0.001 and r = 0.89, p ≤ 0.001 respectively). The AiDx Assist device performance is consistent with requirement of the World Health Organization diagnostic target product profile for monitoring, evaluation, and surveillance of Schistosomiasis elimination Programs.@en