Artificial Intelligence in Point-of-Care Testing

Abstract

With the projected increase in the global population, current healthcare delivery models will face severe challenges. Rural and remote areas, whether in developed or developing countries, are characterized by the same challenges: the unavailability of hospitals, lack of trained and skilled staff performing tests, and poor compliance with quality assurance protocols. Point-of-care testing using artificial intelligence (AI) is poised to be able to address these challenges. In this review, we highlight some key areas of application of AI in point-of-care testing, including lateral flow immunoassays, bright-field microscopy, and hematology, demonstrating this rapidly expanding field of laboratory medicine.

Keywords: Anemia; Artificial intelligence; Bright-field microscopy; Convolutional neural network; Hematology; Hemoglobin; Immunoassay; Lateral flow immunoassay; Malaria; Microscopy; Point-of-care testing.

Fig. 1

Schematic of the electrophoretic process for POC testing of anemia and Hb variants. (A) A whole blood specimen (red) is mixed with a standard calibrator (blue) and electrophoresed on the cellulose acetate paper. (B) Within 2.5 minutes, the Hb is separated from the standard. A ML ANN is used to determine the Hb concentration and anemia status. (C) Within 8 minutes, the Hb is separated into the different variants, and major variants are determined (Adapted from An, et al. [27].). Abbreviations: POC, point-of-care; ML machine learning; ANN, artificial neural network.