Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium

ABSTRACT

Background: Microscopy remains the gold standard in the diagnosis of neglected tropical diseases. As resource limited, rural areas often lack laboratory equipment and trained personnel, new diagnostic techniques are needed. Low-cost, point-of-care imaging devices show potential in the diagnosis of these diseases. Novel, digital image analysis algorithms can be utilized to automate sample analysis.

Objective: Evaluation of the imaging performance of a miniature digital microscopy scanner for the diagnosis of soil-transmitted helminths and Schistosoma haematobium, and training of a deep learning-based image analysis algorithm for automated detection of soil-transmitted helminths in the captured images.

Methods: A total of 13 iodine-stained stool samples containing Ascaris lumbricoides, Trichuris trichiura and hookworm eggs and 4 urine samples containing Schistosoma haematobium were digitized using a reference whole slide-scanner and the mobile microscopy scanner. Parasites in the images were identified by visual examination and by analysis with a deep learning-based image analysis algorithm in the stool samples. Results were compared between the digital and visual analysis of the images showing helminth eggs.

Results: Parasite identification by visual analysis of digital slides captured with the mobile microscope was feasible for all analyzed parasites. Although the spatial resolution of the reference slide-scanner is higher, the resolution of the mobile microscope is sufficient for reliable identification and classification of all parasites studied. Digital image analysis of stool sample images captured with the mobile microscope showed high sensitivity for detection of all helminths studied (range of sensitivity = 83.3–100%) in the test set (n = 217) of manually labeled helminth eggs.

Conclusions: In this proof-of-concept study, the imaging performance of a mobile, digital microscope was sufficient for visual detection of soil-transmitted helminths and Schistosoma haematobium. Furthermore, we show that deep learning-based image analysis can be utilized for the automated detection and classification of helminths in the captured images.

SPECIAL ISSUE:

• mHealth for Improved Access and Equity in Health Care

KEYWORDS:

• Neglected tropical diseases
• helminth
• point-of-care
• computer vision
• global health

Responsible Editor Nawi Ng, Umeå University, Sweden

Responsible Editor Nawi Ng, Umeå University, Sweden

Acknowledgments

We would like to thank Taru Meri for assisting with the acquisition and preparation of the samples. The article was published thanks to financial support from the Wallenberg Foundation and Umeå University.

Disclosure statement

Johan Lundin and Mikael Lundin are founders and co-owners of Fimmic Oy, Helsinki, Finland.

Ethics and consent

This manuscript reports a retrospective study of routinely collected samples. The study was approved by the Coordinating Ethical Committee of Surgery of the Hospital District of Helsinki and Uusimaa (DNo. HUS/1655/2016). According to the Ministry of Social Affairs and Health, Finland Act on the Medical Use of Human Organs, Tissues and Cells (Amendments up to 277/2013 included), written informed consent was not required because no clinical records were retrieved and the study contained no personal identifiers.

Paper context

Neglected tropical diseases affect more than a billion people globally, mainly in rural areas lacking resources for diagnostics. Portable, digital microscopes have been suggested as potential novel diagnostic tools. Here we demonstrate how a low-cost, miniature digital microscope scanner, supported by artificial intelligence-based sample analysis, can be used for field diagnostics of common parasitic diseases. Parasite identification with this technology is feasible, although further clinical validation is needed before wider implementation.

Additional information

Funding

This work was supported by grants from the Swedish Research Council, Sigrid Jusélius Foundation, Finska Läkaresällskapet, Medicinska Understödsföreningen Liv och Hälsa rf and Tekes – the Finnish Funding Agency for Innovation. In addition, this study has received funding from the ‘European Advanced Translational Research Infrastructure in Medicine’ (EATRIS)/Academy of Finland. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Notes on contributors

Oscar Holmström

Conceived and designed the experiments: OH, JL, NL, BN. Performed the experiments: OH, JL. Analyzed the data: OH, NL, JL. Contributed analysis tools and software: ML. Wrote the paper: OH, NL, JL. Commented on the manuscript: OH, NL, JL, HM, AS, AM, VD, EL. Supervised the project: JL.