AI approach outperformed human experts in identifying cervical precancer
Recommended treatment significantly increases survival rates in women aged 65 years and older with early-stage cervical cancer.
Researchers trained a deep‑learning algorithm on over 60,000 digitized cervical images from a Costa Rican population study. The algorithm, named automated visual evaluation, achieved an AUC of 0.91 for detecting precancerous lesions, outperforming both expert reviewers (AUC 0.69) and conventional cytology (AUC 0.71).
The method provides a scalable, low‑cost, and minimally trained screening tool that could greatly improve cervical cancer detection in low‑resource settings, potentially reducing the disease burden and death rates worldwide.
Bizonyítékszint: Korai humán adat. Kis vagy feltáró emberi adat.
Kapcsolódó jelek
- A multinational cross-sectional study on human papillomavirus and cervical cancer knowledge, vaccination attitudes, and risk factors in the Middle East
- Genomic features of cervical cancer
- Screening for Cervical Cancer
- Cervical cancer: Epidemiology, risk factors and screening
- Cervical Cancer Screening in Transition: Global Challenges and Local Realities