Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning.
Debashish DasRanitha VongpromekThanawat AssawariyathipatKetsanee SrinamonKalynn KennonKasia StepniewskaAniruddha GhoseAbdullah Abu SayeedM Abul FaizRebeca Linhares Abreu NettoAndre SiqueiraSerge R YerbangaJean Bosco OuédraogoJames J CalleryThomas J PetoRupam TripuraFelix Koukouikila-KoussoundaFrancine NtoumiJohn Michael Ong'echaBernhards OgutuPrakash GhimireJutta MarfurtBenedikt LeyAmadou SeckMagatte NdiayeBhavani MoodleyLisa Ming SunLaypaw ArchasuksanStephane ProuxSam L NsobyaPhilip J RosenthalMatthew P HorningShawn K McGuireCourosh MehanianStephen BurkotCharles B DelahuntChristine BachmanRic N PriceArjen M DondorpFrançois ChappuisPhilippe J GuérinMehul DhordaPublished in: Malaria journal (2022)
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.