TY  - JOUR
AU  - Xu, He-Li
AU  - Li, Xiao-Ying
AU  - Jia, Ming-Qian
AU  - Ma, Qi-Peng
AU  - Zhang, Ying-Hua
AU  - Liu, Fang-Hua
AU  - Qin, Ying
AU  - Chen, Yu-Han
AU  - Li, Yu
AU  - Chen, Xi-Yang
AU  - Xu, Yi-Lin
AU  - Li, Dong-Run
AU  - Wang, Dong-Dong
AU  - Huang, Dong-Hui
AU  - Xiao, Qian
AU  - Zhao, Yu-Hong
AU  - Gao, Song
AU  - Qin, Xue
AU  - Tao, Tao
AU  - Gong, Ting-Ting
AU  - Wu, Qi-Jun
PY  - 2025
DA  - 2025/3/24
TI  - AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis
JO  - J Med Internet Res
SP  - e67922
VL  - 27
KW  - artificial intelligence
KW  - AI
KW  - blood biomarker
KW  - ovarian cancer
KW  - diagnosis
KW  - PRISMA
AB  - Background: Emerging evidence underscores the potential application of artificial intelligence (AI) in discovering noninvasive blood biomarkers. However, the diagnostic value of AI-derived blood biomarkers for ovarian cancer (OC) remains inconsistent. Objective: We aimed to evaluate the research quality and the validity of AI-based blood biomarkers in OC diagnosis. Methods: A systematic search was performed in the MEDLINE, Embase, IEEE Xplore, PubMed, Web of Science, and the Cochrane Library databases. Studies examining the diagnostic accuracy of AI in discovering OC blood biomarkers were identified. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies–AI tool. Pooled sensitivity, specificity, and area under the curve (AUC) were estimated using a bivariate model for the diagnostic meta-analysis. Results: A total of 40 studies were ultimately included. Most (n=31, 78%) included studies were evaluated as low risk of bias. Overall, the pooled sensitivity, specificity, and AUC were 85% (95% CI 83%-87%), 91% (95% CI 90%-92%), and 0.95 (95% CI 0.92-0.96), respectively. For contingency tables with the highest accuracy, the pooled sensitivity, specificity, and AUC were 95% (95% CI 90%-97%), 97% (95% CI 95%-98%), and 0.99 (95% CI 0.98-1.00), respectively. Stratification by AI algorithms revealed higher sensitivity and specificity in studies using machine learning (sensitivity=85% and specificity=92%) compared to those using deep learning (sensitivity=77% and specificity=85%). In addition, studies using serum reported substantially higher sensitivity (94%) and specificity (96%) than those using plasma (sensitivity=83% and specificity=91%). Stratification by external validation demonstrated significantly higher specificity in studies with external validation (specificity=94%) compared to those without external validation (specificity=89%), while the reverse was observed for sensitivity (74% vs 90%). No publication bias was detected in this meta-analysis. Conclusions: AI algorithms demonstrate satisfactory performance in the diagnosis of OC using blood biomarkers and are anticipated to become an effective diagnostic modality in the future, potentially avoiding unnecessary surgeries. Future research is warranted to incorporate external validation into AI diagnostic models, as well as to prioritize the adoption of deep learning methodologies. Trial Registration: PROSPERO CRD42023481232; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023481232 
SN  - 1438-8871
UR  - https://www.jmir.org/2025/1/e67922
UR  - https://doi.org/10.2196/67922
UR  - http://www.ncbi.nlm.nih.gov/pubmed/40126546
DO  - 10.2196/67922
ID  - info:doi/10.2196/67922
ER  -