Fig 1.
Simplified study design and workflow.
Fig 2.
(A) Field of view image, digitized with smartphone. (B) Mosaic augmentation example.
Table 1.
Dataset distribution for model training and validation.
Fig 3.
Microscopic images of blood and cerebrospinal fluid samples highlighting field of view and parasite visualization.
The left panels display the full field of view for each sample type, while the right panels provide close-up views of parasites identified within the samples. Specifically, A’ shows a parasite from the thin blood smear (Panel A), B’ depicts a parasite from the thick blood smear (Panel B), C’ presents a parasite from the mice thin blood smear (Panel C), and D’ highlights a parasite from the cerebrospinal fluid smear (Panel D).
Table 2.
Cross-validation performance summary (mean ± SD, %).
Table 3.
Model performance on human and mice samples, and the inference time measured on an Oppo Reno 6 smartphone.
Table 4.
Performance of SSD MobileNet v2 separated by sample type.
Fig 4.
Operational deployment of real-time AI Algorithm.
(A) Setup for image acquisition and real-time processing using a smartphone coupled to a light microscope. (B) Screenshot illustrating the real-time AI detection of parasites on the smartphone interface.