A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy

Abstract

Background Malaria remains a major global health problem with a need for improved field-usable diagnostic tests. We have developed a portable, low-cost digital microscope scanner, capable of both brightfield and fluorescence imaging. Here, we used the instrument to digitize blood smears, and applied deep learning (DL) algorithms to detect Plasmodium falciparum parasites. Methods Thin blood smears (n = 125) were collected from patients with microscopy-confirmed P. falciparum infections in rural Tanzania, prior to and after initiation of artemisinin-based combination therapy. The samples were stained using the 4′,6-diamidino-2-phenylindole fluorogen and digitized using the prototype microscope scanner. Two DL algorithms were trained to detect malaria parasites in the samples, and results compared to the visual assessment of both the digitized samples, and the Giemsa-stained thick smears. Results Detection of P. falciparum parasites in the digitized thin blood smears was possible both by visual assessment and by DL-based analysis with a strong correlation in results (r = 0.99, p < 0.01). A moderately strong correlation was observed between the DL-based thin smear analysis and the visual thick smear-analysis (r = 0.74, p < 0.01). Low levels of parasites were detected by DL-based analysis on day three following treatment initiation, but a small number of fluorescent signals were detected also in microscopy-negative samples. Conclusion Quantification of P. falciparum parasites in DAPI-stained thin smears is feasible using DL-supported, point-of-care digital microscopy, with a high correlation to visual assessment of samples. Fluorescent signals from artefacts in samples with low infection levels represented the main challenge for the digital analysis, thus highlighting the importance of minimizing sample contaminations. The proposed method could support malaria diagnostics and monitoring of treatment response through automated quantification of parasitaemia and is likely to be applicable also for diagnostics of other Plasmodium species and other infectious diseases.

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

“…A number of AI models have been developed for the automated detection of Plasmodium parasites, the causative agent of malaria, which may be highly beneficial in affected regions. CNN using transfer learning has been proposed to automatically detect and quantify Plasmodium falciparum at different cellular stages of infection, where diagnostic accuracy is heavily dependent on the expertise of the microscopist [ 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ]. In their study, Oliveria et al has applied multilayer perceptron and decision tree as a new approach for detecting malaria parasites in full images of thick blood smears using pixel classifiers.…”

Utilizing Image Analysis with Machine Learning and Deep Learning to Identify Malaria Parasites in Conventional Microscopic Blood Smear Images

“…The examination of Giemsa-stained blood films via microscopy remains the gold standard for Plasmodium detection, as reported by studies like Tangpukdee et al [4]; Yang et al [5]; Mavandadi et al [6]; and Holmström et al [7]. The microscope's cost-effectiveness, simplicity, and adaptability make it particularly suitable for low-resource, high-disease-burden regions.…”

A portable brightfield and fluorescence microscope toward automated malarial parasitemia quantification in thin blood smears

“…In the past decade, the overall size of portable fluorescent systems has begun to shrink, utilizing new optical configurations, and their performance have begun to be evaluated in field settings [ 23 – 25 ]. More recently, the advent of machine-learning based image processing platforms has been extended to the interpretation of results from fluorescence scans of Plasmodium infected blood films [ 26 ].…”