iMAGING : a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope
Rubio Maturana, Carles (Universitat Autònoma de Barcelona. Departament de Genètica i de Microbiologia)
de Oliveira, Allisson Dantas (Universitat Politècnica de Catalunya)
Nadal, Sergi (Universitat Politècnica de Catalunya)
Serrat, Francesc Zarzuela (Hospital Universitari Vall d'Hebron)
Sulleiro Igual, Elena (Universitat Autònoma de Barcelona. Departament de Genètica i de Microbiologia)
Ruiz, Edurne (Hospital Universitari Vall d'Hebron)
Bilalli, Besim (Universitat Politècnica de Catalunya)
Veiga, Anna (Probitas Foundation)
Espasa, Mateu (Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT))
Abelló, Alberto (Universitat Politècnica de Catalunya)
Pumarola Suñé, Tomàs (Universitat Autònoma de Barcelona. Departament de Genètica i de Microbiologia)
Segú, Marta (Futbol Club Barcelona Foundation)
López-Codina, Daniel (Universitat Politècnica de Catalunya)
Clols, Elisa Sayrol (Universitat Pompeu Fabra)
Joseph-Munné, Joan (Hospital Universitari Vall d'Hebron)

Data:	2023
Resum:	Malaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it. In this study, a dataset of 2571 labeled thick blood smear images were created. YOLOv5x, Faster R-CNN, SSD, and RetinaNet object detection neural networks were trained on the same dataset to evaluate their performance in Plasmodium parasite detection. Attention modules were applied and compared with YOLOv5x results. To automate the entire diagnostic process, a prototype of 3D-printed pieces was designed for the robotization of conventional optical microscopy, capable of auto-focusing the sample and tracking the entire slide. Comparative analysis yielded a performance for YOLOv5x on a test set of 92. 10% precision, 93. 50% recall, 92. 79% F-score, and 94. 40% mAP0. 5 for leukocyte, early and mature Plasmodium trophozoites overall detection. F-score values of each category were 99. 0% for leukocytes, 88. 6% for early trophozoites and 87. 3% for mature trophozoites detection. Attention modules performance show non-significant statistical differences when compared to YOLOv5x original trained model. The predictive models were integrated into a smartphone-computer application for the purpose of image-based diagnostics in the laboratory. The system can perform a fully automated diagnosis by the auto-focus and X-Y movements of the robotized microscope, the CNN models trained for digital image analysis, and the smartphone device. The new prototype would determine whether a Giemsa-stained thick blood smear sample is positive/negative for Plasmodium infection and its parasite levels. The whole system was integrated into the iMAGING smartphone application. The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Artificial intelligence ; Convolutional neural networks ; Malaria ; Malaria diagnosis ; Robotized microscope ; Smartphone application ; Thick blood smears ; YOLOv5
Publicat a:	Frontiers in microbiology, Vol. 14 (november 2023) , ISSN 1664-302X

DOI: 10.3389/fmicb.2023.1240936
PMID: 38075929

13 p, 4.5 MB

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