Scientific Reports (Apr 2024)

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

  • 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

DOI
https://doi.org/10.1038/s41598-024-59068-6
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 15

Abstract

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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.