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This work has been partially funded by the European Union's H2020 Innovation In SMEs research and innovation programme (grant agreement No 881062) and the Bill and Melinda Gates Foundation (grant number Edge-Spot project INV-051355). This work was supported by the Comunidad de Madrid Industrial Predoctoral grant (IND2019/TIC-17167 to LL and Universidad Politecnica de Madrid). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy

Publicado en:Plos Neglected Tropical Diseases. 18 (4): e0012117- - 2024-04-01 18(4), DOI: 10.1371/journal.pntd.0012117

Autores: Lin, L; Dacal, E; Díez, N; Carmona, C; Ramirez, AM; Argos, LB; Bermejo-Peláez, D; Caballero, C; Cuadrado, D; Darias-Plasencia, O; García-Villena, J; Bakarjiev, A; Postigo, M; Recalde-Jaramillo, E; Flores-Chavez, M; Santos, A; Ledesma-Carbayo, MJ; Rubio, JM; Luengo-Oroz, M

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Resumen

Filariasis, a neglected tropical disease caused by roundworms, is a significant public health concern in many tropical countries. Microscopic examination of blood samples can detect and differentiate parasite species, but it is time consuming and requires expert microscopists, a resource that is not always available. In this context, artificial intelligence (AI) can assist in the diagnosis of this disease by automatically detecting and differentiating microfilariae. In line with the target product profile for lymphatic filariasis as defined by the World Health Organization, we developed an edge AI system running on a smartphone whose camera is aligned with the ocular of an optical microscope that detects and differentiates filarias species in real time without the internet connection. Our object detection algorithm that uses the Single-Shot Detection (SSD) MobileNet V2 detection model was developed with 115 cases, 85 cases with 1903 fields of view and 3342 labels for model training, and 30 cases with 484 fields of view and 873 labels for model validation before clinical validation, is able to detect microfilarias at 10x magnification and distinguishes four species of them at 40x magnification: Loa loa, Mansonella perstans, Wuchereria bancrofti, and Brugia malayi. We validated our augmented microscopy system in the clinical environment by replicating the diagnostic workflow encompassed examinations at 10x and 40x with the assistance of the AI models analyzing 18 samples with the AI running on a middle range smartphone. It achieved an overall precision of 94.14%, recall of 91.90% and F1 score of 93.01% for the screening algorithm and 95.46%, 97.81% and 96.62% for the species differentiation algorithm respectively. This innovative solution has the potential to support filariasis diagnosis and monitoring, particularly in resource-limited settings where access to expert technicians and laboratory equipment is scarce. Filariasis is a common tropical infectious disease. Depending on the parasite, it causes lymphoedema, elephantiasis, itching, blindness, etc. It is estimated that more than 1 billion people require preventive chemotherapy to stop the spread of this infection. The diagnosis of this disease is made through microscopical examination of a blood smear by a human expert, which is not always available. In this study we propose an edge Artificial Intelligence (AI) system that detects and quantifies four species of microfilariae (Loa loa, Mansonella perstans, Wuchereria bancrofti and Brugia malayi) using the camera of a smartphone attached to an optical microscope with a 3D printed adapter. The system works in real time and does not need internet connectivity as the AI models are run locally in a medium range smartphone. We have replicated the diagnostic workflow that is typically performed by an expert microscopist augmented by the support of the AI system.

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