Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay
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, w...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-38104-5 |
| _version_ | 1797836366924480512 |
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| author | Seungmin Lee Sunmok Kim Dae Sung Yoon Jeong Soo Park Hyowon Woo Dongho Lee Sung-Yeon Cho Chulmin Park Yong Kyoung Yoo Ki- Baek Lee Jeong Hoon Lee |
| author_facet | Seungmin Lee Sunmok Kim Dae Sung Yoon Jeong Soo Park Hyowon Woo Dongho Lee Sung-Yeon Cho Chulmin Park Yong Kyoung Yoo Ki- Baek Lee Jeong Hoon Lee |
| author_sort | Seungmin Lee |
| collection | DOAJ |
| description | 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 (SMARTAI-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, we observed that the accuracy of SMARTAI-LFA was maintained at over 99% while there was a significant decrease in human accuracy, indicating the reliable performance of SMARTAI-LFA. We envision a smartphone-based SMARTAI-LFA that allows continuously enhanced performance by adding clinical tests and satisfies the new criterion for digitalized real-time diagnostics. |
| first_indexed | 2024-04-09T15:08:46Z |
| format | Article |
| id | doaj.art-9d92f4ddb064470da9a8eb324a3f3998 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-09T15:08:46Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-9d92f4ddb064470da9a8eb324a3f39982023-04-30T11:21:47ZengNature PortfolioNature Communications2041-17232023-04-0114111110.1038/s41467-023-38104-5Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assaySeungmin Lee 0Sunmok Kim 1Dae Sung Yoon2Jeong Soo Park3Hyowon Woo4Dongho Lee 5Sung-Yeon Cho6Chulmin Park7Yong Kyoung Yoo 8Ki- Baek Lee9Jeong Hoon Lee10Department of Electrical Engineering, Kwangwoon UniversityDepartment of Electrical Engineering, Kwangwoon UniversitySchool of Biomedical Engineering, Korea UniversityDepartment of Electrical Engineering, Kwangwoon UniversityDepartment of Electrical Engineering, Kwangwoon UniversityCALTH Inc.Vaccine Bio Research Institute, College of Medicine, The Catholic University of KoreaVaccine Bio Research Institute, College of Medicine, The Catholic University of KoreaDepartment of Electronic Engineering, Catholic Kwandong UniversityDepartment of Electrical Engineering, Kwangwoon UniversityDepartment of Electrical Engineering, Kwangwoon UniversityAbstract 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 (SMARTAI-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, we observed that the accuracy of SMARTAI-LFA was maintained at over 99% while there was a significant decrease in human accuracy, indicating the reliable performance of SMARTAI-LFA. We envision a smartphone-based SMARTAI-LFA that allows continuously enhanced performance by adding clinical tests and satisfies the new criterion for digitalized real-time diagnostics.https://doi.org/10.1038/s41467-023-38104-5 |
| spellingShingle | Seungmin Lee Sunmok Kim Dae Sung Yoon Jeong Soo Park Hyowon Woo Dongho Lee Sung-Yeon Cho Chulmin Park Yong Kyoung Yoo Ki- Baek Lee Jeong Hoon Lee Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay Nature Communications |
| title | Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay |
| title_full | Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay |
| title_fullStr | Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay |
| title_full_unstemmed | Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay |
| title_short | Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay |
| title_sort | sample to answer platform for the clinical evaluation of covid 19 using a deep learning assisted smartphone based assay |
| url | https://doi.org/10.1038/s41467-023-38104-5 |
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