Applications of artificial intelligence and machine learning models in the prognosis and diagnosis of ovarian cancer
Dina M. Khodeer, Celestin Ukozehasi, Ali M. Alaseem, Sally M. Abdelmonem · 2026
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Description
Dina M. Khodeer and co-authors reviewed in 2026 artificial intelligence methods for detecting ovarian cancer and forecasting its course. The cancer is usually found late because early disease causes few clear symptoms and no good screening test exists. Imaging remains central to diagnosis, yet human reading of scans varies between specialists. Blood markers and molecular data offer further signals that programs can combine with images.
Radiomics methods extract hundreds of measurable features from scans that the eye cannot score directly. Such features have separated benign from malignant masses and hinted at underlying gene mutations in studies reviewed. Deep learning models often beat standard methods on accuracy for diagnosis and outcome prediction in the literature surveyed. Joining imaging with multi-layer molecular data may sharpen personal forecasts further. The authors see medical adoption as promising yet still dependent on checking.
Catalog record
| CREATOR | Dina M. Khodeer, Celestin Ukozehasi, Ali M. Alaseem, Sally M. Abdelmonem |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 41 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |