Diagnostic accuracy of artificial intelligence-based multi-spectrum analysis for molecular fingerprint detection of SARS-CoV-2
Medicine (United States), cilt.104, sa.21, 2025 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 104 Sayı: 21
- Basım Tarihi: 2025
- Doi Numarası: 10.1097/md.0000000000041928
- Dergi Adı: Medicine (United States)
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CINAHL, EMBASE, Directory of Open Access Journals
- Anahtar Kelimeler: artificial intelligence, molecular fingerprint, multi-spectrum analysis, SARS-CoV-2
- Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
Reverse transcription-polymerase chain reaction (RT-PCR) is the reference standard for COVID-19 diagnosis, but the need for rapid, reproducible, and cost-effective diagnostic tools remains. This study investigated the diagnostic performance of a novel artificial intelligence-based multispectrum analysis (MSA, AP23) technique that detects the biomolecular fingerprint of severe acute respiratory syndrome coronavirus 2. A prospective, double-blinded observational design was used, involving 3614 volunteers. The artificial intelligence was trained with 2448 samples, validated with 816, and tested against RT-PCR using a blinded set of 350 samples. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated. During validation, MSA achieved 88.4% sensitivity, 88.76% specificity, 86.77% positive predictive value, and 90.18% negative predictive value. In the blinded comparison phase, these values were 81.73%, 81.99%, 75.16%, and 87.81%, respectively, with an area under the receiver operating characteristic curve of 0.89. These findings suggest that MSA offers reliable diagnostic performance and may be a promising alternative to RT-PCR in COVID-19 diagnosis. The study was registered on ClinicalTrials.gov (NCT04860895).