Digital Platform for Automatic Qualitative and Quantitative Reading of a Cryptococcal Antigen Point-of-Care Assay Leveraging Smartphones and Artificial Intelligence

• Published in: Journal of fungi (Basel) Vol. 9; no. 2; p. 217
• Main Authors: Bermejo-Peláez, David, Medina, Narda, Álamo, Elisa, Soto-Debran, Juan Carlos, Bonilla, Oscar, Luengo-Oroz, Miguel, Rodriguez-Tudela, Juan Luis, Alastruey-Izquierdo, Ana
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01-02-2023; MDPI
• Subjects: Algorithms; Antigens; Artificial intelligence; artificial intelligence (AI); cryptococcal antigen; Cryptococcosis; Cryptococcus; Digitization; Disease; Health aspects; HIV; Human immunodeficiency virus; Internet access; lateral flow assay (LFA); Medical care, Cost of; Meningitis; Mortality; Quality control; rapid diagnostic test (POCT); Reading; Severe acute respiratory syndrome coronavirus 2; Smart phones; smartphone; Smartphones; Spain; United States > US; Madrid Spain; test line quantification; smartphone; artificial intelligence (AI); lateral flow assay (LFA); Cryptococcus; cryptococcal antigen; rapid diagnostic test (POCT)
• ISSN: 2309-608X; 2309-608X
• DOI: 10.3390/jof9020217

Cryptococcosis is a fungal infection that causes serious illness, particularly in immunocompromised individuals such as people living with HIV. Point of care tests (POCT) can help identify and diagnose patients with several advantages including rapid results and ease of use. The cryptococcal antigen (CrAg) lateral flow assay (LFA) has demonstrated excellent performance in diagnosing cryptococcosis, and it is particularly useful in resource-limited settings where laboratory-based tests may not be readily available. The use of artificial intelligence (AI) for the interpretation of rapid diagnostic tests can improve the accuracy and speed of test results, as well as reduce the cost and workload of healthcare professionals, reducing subjectivity associated with its interpretation. In this work, we analyze a smartphone-based digital system assisted by AI to automatically interpret CrAg LFA as well as to estimate the antigen concentration in the strip. The system showed excellent performance for predicting LFA qualitative interpretation with an area under the receiver operating characteristic curve of 0.997. On the other hand, its potential to predict antigen concentration based solely on a photograph of the LFA has also been demonstrated, finding a strong correlation between band intensity and antigen concentration, with a Pearson correlation coefficient of 0.953. The system, which is connected to a cloud web platform, allows for case identification, quality control, and real-time monitoring.