Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning
Authors: Das Debashish , Vongpromek Ranitha , Assawariyathipat Thanawat , Srinamon Ketsanee , Kennon Kalynn , Stepniewska Kasia , Ghose Aniruddha , Sayeed Abdullah Abu , Faiz M. Abul , Netto Rebeca Linhares Abreu , Siqueira Andre , Yerbanga Serge R. , Ouédraogo Jean Bosco , Callery James J. , Peto Thomas J. , Tripura Rupam , Koukouikila-Koussounda Felix , Ntoumi Francine , Ong’echa John Michael , Ogutu Bernhards , Ghimire Prakash , Marfurt Jutta , Ley Benedikt , Seck Amadou , Ndiaye Magatte , Moodley Bhavani , Sun Lisa Ming , Archasuksan Laypaw , Proux Stephane , Nsobya Sam L. , Rosenthal Philip J. , Horning Matthew P. , McGuire Shawn K. , Mehanian Courosh , Burkot Stephen , Delahunt Charles B. , Bachman Christine , Price Ric N. , Dondorp Arjen M. , Chappuis François , Guérin Philippe J. , Dhorda Mehul 
Published: April 2022

Abstract
Background
Microscopic examination of Giemsa-stained blood films remains the reference standard for malaria parasite detection and quantification, but is undermined by difficulties in ensuring high-quality manual reading and inter-reader reliability. Automated parasite detection and quantification may address this issue.

Methods
A multi-centre, observational study was conducted during 2018 and 2019 at 11 sites to assess the performance of the EasyScan Go, a microscopy device employing machine-learning-based image analysis. Sensitivity, specificity, accuracy of species detection and parasite density estimation were assessed with expert microscopy as the reference. Intra- and inter-device reliability of the device was also evaluated by comparing results from repeat reads on the same and two different devices. This study has been reported in accordance with the Standards for Reporting Diagnostic accuracy studies (STARD) checklist.

Results
In total, 2250 Giemsa-stained blood films were prepared and read independently by expert microscopists and the EasyScan Go device. The diagnostic sensitivity of EasyScan Go was 91.1% (95% CI 88.9–92.7), and specificity 75.6% (95% CI 73.1–78.0). With good quality slides sensitivity was similar (89.1%, 95%CI 86.2–91.5), but specificity increased to 85.1% (95%CI 82.6–87.4). Sensitivity increased with parasitaemia rising from 57% at < 200 parasite/µL, to ≥ 90% at > 200–200,000 parasite/µL. Species were identified accurately in 93% of Plasmodium falciparum samples (kappa = 0.76, 95% CI 0.69–0.83), and in 92% of Plasmodium vivax samples (kappa = 0.73, 95% CI 0.66–0.80). Parasite density estimates by the EasyScan Go were within ± 25% of the microscopic reference counts in 23% of slides.

Conclusions
The performance of the EasyScan Go in parasite detection and species identification accuracy fulfil WHO-TDR Research Malaria Microscopy competence level 2 criteria. In terms of parasite quantification and false positive rate, it meets the level 4 WHO-TDR Research Malaria Microscopy criteria. All performance parameters were significantly affected by slide quality. Further software improvement is required to improve sensitivity at low parasitaemia and parasite density estimations.
Trial registration ClinicalTrials.gov number NCT03512678.