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Kamari F, Eller E, Bøgebjerg ME, Capella IM, Galende BA, Korim T, Øland P, Borup ML, Frederiksen AR, Ranjouriheravi A, Al-Jwadi AF, Mansour M, Hansen S, Diethelm I, Burek M, Alvarez F, Buch AG, Mojtahedi N, Röttger R, Segtnan EA. Clinical performance of AI-integrated risk assessment pooling reveals cost savings even at high prevalence of COVID-19. Sci Rep 2024; 14:8853. [PMID: 38632289 PMCID: PMC11024139 DOI: 10.1038/s41598-024-59068-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 04/06/2024] [Indexed: 04/19/2024] Open
Abstract
Individual testing of samples is time- and cost-intensive, particularly during an ongoing pandemic. Better practical alternatives to individual testing can significantly decrease the burden of disease on the healthcare system. Herein, we presented the clinical validation of Segtnan™ on 3929 patients. Segtnan™ is available as a mobile application entailing an AI-integrated personalized risk assessment approach with a novel data-driven equation for pooling of biological samples. The AI was selected from a comparison between 15 machine learning classifiers (highest accuracy = 80.14%) and a feed-forward neural network with an accuracy of 81.38% in predicting the rRT-PCR test results based on a designed survey with minimal clinical questions. Furthermore, we derived a novel pool-size equation from the pooling data of 54 published original studies. The results demonstrated testing capacity increase of 750%, 60%, and 5% at prevalence rates of 0.05%, 22%, and 50%, respectively. Compared to Dorfman's method, our novel equation saved more tests significantly at high prevalence, i.e., 28% (p = 0.006), 40% (p = 0.00001), and 66% (p = 0.02). Lastly, we illustrated the feasibility of the Segtnan™ usage in clinically complex settings like emergency and psychiatric departments.
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Affiliation(s)
- Farzin Kamari
- Department of Neurophysiology, Institute of Physiology, Eberhard Karls University of Tübingen, Tübingen, Germany
| | | | - Mathias Emil Bøgebjerg
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | | | - Borja Arroyo Galende
- Grupo de Aplicación de Telecomunicaciones Visuales, Universidad Politécnica de Madrid, Madrid, Spain
| | | | | | | | | | - Amir Ranjouriheravi
- Research Center for Translational Medicine (KUTTAM), Graduate School of Sciences and Engineering, Koç University, Istanbul, Turkey
| | | | - Mostafa Mansour
- SDU Health Informatics and Technology, Maersk Mc-Kinney Moller Institute, Faculty of Engineering, University of Southern Denmark, Odense, Denmark
| | - Sara Hansen
- SDU Health Informatics and Technology, Maersk Mc-Kinney Moller Institute, Faculty of Engineering, University of Southern Denmark, Odense, Denmark
| | - Isabella Diethelm
- SDU Health Informatics and Technology, Maersk Mc-Kinney Moller Institute, Faculty of Engineering, University of Southern Denmark, Odense, Denmark
| | - Marta Burek
- SDU Health Informatics and Technology, Maersk Mc-Kinney Moller Institute, Faculty of Engineering, University of Southern Denmark, Odense, Denmark
| | - Federico Alvarez
- Grupo de Aplicación de Telecomunicaciones Visuales, Universidad Politécnica de Madrid, Madrid, Spain
| | - Anders Glent Buch
- Department of Engineering, Maersk Mc-Kinney Moller Institute, Faculty of Engineering, University of Southern Denmark, Odense, Denmark
| | - Nima Mojtahedi
- Department of Neurophysiology, Institute of Physiology, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Richard Röttger
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Eivind Antonsen Segtnan
- Department of Neurosurgery, Odense University Hospital, Odense, Denmark.
- Synaptic ApS, Copenhagen, Denmark.
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