Clinical performance of AI-integrated risk assessment pooling reveals cost savings even at high prevalence of COVID-19

Autor: Farzin Kamari, Esben Eller, Mathias Emil Bøgebjerg, Ignacio Martínez Capella, Borja Arroyo Galende, Tomas Korim, Pernille Øland, Martin Lysbjerg Borup, Anja Rådberg Frederiksen, Amir Ranjouriheravi, Ahmed Faris Al-Jwadi, Mostafa Mansour, Sara Hansen, Isabella Diethelm, Marta Burek, Federico Alvarez, Anders Glent Buch, Nima Mojtahedi, Richard Röttger, Eivind Antonsen Segtnan
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-024-59068-6
Popis: 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.
Databáze: Directory of Open Access Journals
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