Manescu, P;
Shaw, MJ;
Elmi, M;
Neary-Zajiczek, L;
Claveau, R;
Pawar, V;
Kokkinos, I;
... Fernandez-Reyes, D; + view all
(2020)
Expert-Level Automated Malaria Diagnosis on Routine Blood Films with Deep Neural Networks.
American Journal of Hematology
, 95
(8)
pp. 883-891.
10.1002/ajh.25827.
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Abstract
Over 200 million malaria cases globally lead to half a million deaths annually. Accurate malaria diagnosis remains a challenge for which automated imaging processing approaches to analyze Thick Blood Films (TBF) could provide scalable solutions for urban healthcare providers in the holoendemic malaria sub-Saharan region. Although several approaches have been attempted to identify malaria parasites in TBF, none have achieved negative and positive predictive performance suitable for clinical use in the west sub-Saharan region. While malaria parasite object detection remains an intermediary step in achieving automatic patient diagnosis, training state-of-the-art deep-learning object detectors requires the human-expert labor-intensive process of labelling a large dataset of digitized TBF. To overcome these challenges and to achieve a clinically usable system, we show a novel approach to leverage routine clinical-microscopy labels from our quality-controlled malaria clinics to train a Deep Malaria Convolutional Neural Network classifier (DeepMCNN) for automated malaria diagnosis. Our system also provides total Malaria Parasite (MP) and White Blood Cell (WBC) counts allowing parasitemia estimation in MP/μl as recommended by the WHO. Prospective validation of the DeepMCNN achieves sensitivity/specificity of 0.92/0.90 against expert-level malaria diagnosis. Our approach PPV/NPV performance is of 0.92/0.90 which is clinically usable in our holoendemic settings in the densely populated metropolis of Ibadan, located within the most populous African country (Nigeria) and with one of the largest burdens of P. falciparum malaria. Our openly available method is of importance for strategies aimed to scale malaria diagnosis in urban regions where daily assessment of thousands of specimens is required.
| Type: | Article |
|---|---|
| Title: | Expert-Level Automated Malaria Diagnosis on Routine Blood Films with Deep Neural Networks |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/ajh.25827 |
| Publisher version: | https://doi.org/10.1002/ajh.25827 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10095302 |
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