Publications by authors named "Daniel Capellan-Martin"
Article Synopsis
- Cryptococcosis poses a major global health risk, prompting the need for fast and dependable diagnostic methods, particularly through point-of-care tests (POCTs) like the cryptococcal antigen semi-quantitative lateral flow assay (CrAgSQ).
- Researchers developed an AI platform to automate the interpretation of LFA results, using a dataset of 1272 images collected from various CrAg concentrations, demonstrating improved sensitivity and accuracy compared to manual readings.
- The AI system shows strong potential in transforming how cryptococcosis is diagnosed by providing consistent and efficient results, with implications for broader diagnostic applications beyond just cryptococcosis.
Background: Tuberculosis (TB) remains a major cause of morbidity and death worldwide, with a significant impact on children, especially those under the age of 5 years. The complex diagnosis of pediatric TB, compounded by limited access to more accurate diagnostic tests, underscores the need for improved tools to enhance diagnosis and care in resource-limited settings.
Objective: This study aims to present a telemedicine web platform, BITScreen PTB (Biomedical Image Technologies Screen for Pediatric Tuberculosis), aimed at improving the evaluation of pulmonary TB in children based on digital chest x-ray (CXR) imaging and clinical information in resource-limited settings.
Combining collective and artificial intelligence for global health diseases diagnosis using crowdsourced annotated medical images.
Annu Int Conf IEEE Eng Med Biol Soc
November 2021
Visual inspection of microscopic samples is still the gold standard diagnostic methodology for many global health diseases. Soil-transmitted helminth infection affects 1.5 billion people worldwide, and is the most prevalent disease among the Neglected Tropical Diseases.
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