Schistoscope: Smartphone versus Raspberry Pi based low-cost diagnostic device for urinary Schistosomiasis

Diehl, Jan Carel; Oyibo, Prosper; Agbana, Temitope; Jujjavarapu, Satyajith; Van, G-Young; Vdovin, Gleb; Oyibo, Wellington

doi:10.1109/ghtc46280.2020.9342871

Cited by 4 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can operate as either a semi-automated or a fully automated digital microscope with integrated AI for the identification and quantification of S. haematobium eggs [ 203 ]. Utilizing a Raspberry Pi camera module and a trained deep neural network, it automatically identifies and quantifies parasitic eggs in urine [ 203 , 215 ]. The AI algorithm in the Schistoscope excels in segmenting and counting, demonstrating reliability in resource-limited settings and diverse real-world conditions.…”

Section: Applications Of Artificial Intelligence In the Detection Of ...mentioning

confidence: 99%

A comprehensive exploration of schistosomiasis: Global impact, molecular characterization, drug discovery, artificial intelligence and future prospects

Ekloh,

Asafu-Adjaye,

Tawiah-Mensah

et al. 2024

Heliyon

View full text Add to dashboard Cite

Section: Applications Of Artificial Intelligence In the Detection Of ...mentioning

confidence: 99%

A comprehensive exploration of schistosomiasis: Global impact, molecular characterization, drug discovery, artificial intelligence and future prospects

Ekloh,

Asafu-Adjaye,

Tawiah-Mensah

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…6 Advancements in digital optics and artificial intelligence have revolutionised the field of microscopy. [7][8][9] Digital microscopes equipped with high-resolution cameras and sophisticated imaging software can capture whole-slide images of specimens. These images can be analysed using artificial intelligence algorithms, enabling automated detection and classification of parasites.…”

Section: Introductionmentioning

confidence: 99%

“…Advancements in digital optics and artificial intelligence have revolutionised the field of microscopy 7–9 . Digital microscopes equipped with high‐resolution cameras and sophisticated imaging software can capture whole‐slide images of specimens.…”

Section: Introductionmentioning

confidence: 99%

An automated slide scanning system for membrane filter imaging in diagnosis of urogenital schistosomiasis

Oyibo,

Agbana,

van Lieshout

et al. 2024

Journal of Microscopy

Self Cite

View full text Add to dashboard Cite

Traditionally, automated slide scanning involves capturing a rectangular grid of field‐of‐view (FoV) images which can be stitched together to create whole slide images, while the autofocusing algorithm captures a focal stack of images to determine the best in‐focus image. However, these methods can be time‐consuming due to the need for X‐, Y‐ and Z‐axis movements of the digital microscope while capturing multiple FoV images. In this paper, we propose a solution to minimise these redundancies by presenting an optimal procedure for automated slide scanning of circular membrane filters on a glass slide. We achieve this by following an optimal path in the sample plane, ensuring that only FoVs overlapping the filter membrane are captured. To capture the best in‐focus FoV image, we utilise a hill‐climbing approach that tracks the peak of the mean of Gaussian gradient of the captured FoVs images along the Z‐axis. We implemented this procedure to optimise the efficiency of the Schistoscope, an automated digital microscope developed to diagnose urogenital schistosomiasis by imaging Schistosoma haematobium eggs on 13 or 25 mm membrane filters. Our improved method reduces the automated slide scanning time by 63.18% and 72.52% for the respective filter sizes. This advancement greatly supports the practicality of the Schistoscope in large‐scale schistosomiasis monitoring and evaluation programs in endemic regions. This will save time, resources and also accelerate generation of data that is critical in achieving the targets for schistosomiasis elimination.

show abstract

Target product profiles for devices to diagnose urinary schistosomiasis in Nigeria

Sluiter

Onasanya

Oladepo

et al. 2020

2020 IEEE Global Humanitarian Technology Conference (GHTC)

Self Cite

View full text Add to dashboard Cite

Schistosomiasis is a treatable and preventable neglected tropical disease of Public Health importance affecting over 200 million people worldwide while Nigeria is one of the high burden countries. Currently, available diagnostic tests are cumbersome, low in sensitivity and not field-adaptable given the high skills required that are not available in the rural settings where the diseases are majorly prevalent. There is an urgent need for an easy to use automated diagnostic device to replace the current gold standard, the human-operated microscope. Many promising automated diagnostic technologies are under development. However, a good understanding of the real needs within the local healthcare context is crucial in order to develop and implement a new health diagnostic device. Too often, there is a mismatch between what is needed and what is developed. A target product profile can guide the R&D process in matching with the needs in the local healthcare context. The goal of this project is to combine gaps in the healthcare system and needs from stakeholders with technological possibilities in order to develop a target product profile for a diagnostic device for S. haematobium for specific healthcare scenarios in Nigeria.

show abstract

Schistoscope: Smartphone versus Raspberry Pi based low-cost diagnostic device for urinary Schistosomiasis

Cited by 4 publications

References 13 publications

A comprehensive exploration of schistosomiasis: Global impact, molecular characterization, drug discovery, artificial intelligence and future prospects

A comprehensive exploration of schistosomiasis: Global impact, molecular characterization, drug discovery, artificial intelligence and future prospects

An automated slide scanning system for membrane filter imaging in diagnosis of urogenital schistosomiasis

Target product profiles for devices to diagnose urinary schistosomiasis in Nigeria

Contact Info

Product

Resources

About