{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["14(1)"],"submitter":["Lee S"],"funding":["Kwangwoon University","Ministry of Science and ICT (MSIT)"],"pubmed_abstract":["Since many lateral flow assays (LFA) are tested daily, the improvement in accuracy can greatly impact individual patient care and public health. However, current self-testing for COVID-19 detection suffers from low accuracy, mainly due to the LFA sensitivity and reading ambiguities. Here, we present deep learning-assisted smartphone-based LFA (SMART<sup>AI</sup>-LFA) diagnostics to provide accurate decisions with higher sensitivity. Combining clinical data learning and two-step algorithms enables a cradle-free on-site assay with higher accuracy than the untrained individuals and human experts via blind tests of clinical data (n = 1500). We acquired 98% accuracy across 135 smartphone application-based clinical tests with different users/smartphones. Furthermore, with more low-titer tests, w"],"journal":["Nature communications"],"pagination":["2361"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10124933"],"repository":["biostudies-literature"],"pubmed_title":["Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay."],"pmcid":["PMC10124933"],"pubmed_authors":["Kim S","Lee S","Woo H","Lee KB","Yoo YK","Yoon DS","Cho SY","Park C","Lee D","Park JS","Lee JH"],"additional_accession":[]},"is_claimable":false,"name":"Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay.","description":"Since many lateral flow assays (LFA) are tested daily, the improvement in accuracy can greatly impact individual patient care and public health. However, current self-testing for COVID-19 detection suffers from low accuracy, mainly due to the LFA sensitivity and reading ambiguities. Here, we present deep learning-assisted smartphone-based LFA (SMART<sup>AI</sup>-LFA) diagnostics to provide accurate decisions with higher sensitivity. Combining clinical data learning and two-step algorithms enables a cradle-free on-site assay with higher accuracy than the untrained individuals and human experts via blind tests of clinical data (n = 1500). We acquired 98% accuracy across 135 smartphone application-based clinical tests with different users/smartphones. Furthermore, with more low-titer tests, w","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Apr","modification":"2025-04-18T13:21:17.68Z","creation":"2025-04-06T22:58:58.565Z"},"accession":"S-EPMC10124933","cross_references":{"pubmed":["37095107"],"doi":["10.1038/s41467-023-38104-5"]}}