Clinical performance of AI-integrated risk assessment pooling reveals cost savings even at high prevalence of COVID-19.
Sci Rep
; 14(1): 8853, 2024 04 17.
Article
en En
| MEDLINE
| ID: mdl-38632289
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
COVID-19
Límite:
Humans
Idioma:
En
Revista:
Sci Rep
Año:
2024
Tipo del documento:
Article
País de afiliación:
Alemania
Pais de publicación:
Reino Unido