Performance evaluation of the AiDx multi-diagnostic automated microscope for the detection of schistosomiasis in Abuja, Nigeria
Louise Makau-Barasa (The Ending Neglected Diseases (END) Fund)
Liya Assefa (The Ending Neglected Diseases (END) Fund)
Jacob Solomon (Federal Ministry of Health, Abuja)
Juliana A-Enegela (CBM International, Cambridge)
James G. Damen (University of Jos, Jos)
Samuel Popoola (Aidx Medical BV)
JC Diehl (TU Delft - Design for Sustainability)
Gleb Vdovin (TU Delft - Team Michel Verhaegen)
Tope Agbana (Aidx Medical BV, TU Delft - Team Michel Verhaegen)
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Abstract
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.
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