Artificial Intelligence (AI) in the diagnosis and prediction of adverse pregnancy outcomes for Placenta Accreta Spectrum Disorders (PAS): a systematic review and meta-analysis of diagnostic accuracy

Kai Chen; Mei Xue; Xianhua Meng; Guifang Li; Xiaohong Wang; Yu Long; Hui Li

doi:10.1097/JS9.0000000000004443

Artificial Intelligence (AI) in the diagnosis and prediction of adverse pregnancy outcomes for Placenta Accreta Spectrum Disorders (PAS): a systematic review and meta-analysis of diagnostic accuracy

Int J Surg. 2025 Dec 16. doi: 10.1097/JS9.0000000000004443. Online ahead of print.

Authors

Kai Chen¹, Mei Xue², Xianhua Meng¹, Guifang Li¹, Xiaohong Wang¹, Yu Long³, Hui Li¹

Affiliations

¹ Department of Obstetrics and Gynecology, Jinan City People's Hospital, People's Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, China.
² Department of Obstetrics, Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University, Jinan, Shandong, China.
³ Medical Simulation Center & Department of Obstetrics, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.

PMID: 41405278
DOI: 10.1097/JS9.0000000000004443

Abstract

Background: Precise prenatal diagnosis of Placenta accreta spectrum disorders (PAS) is challenging, and the diagnostic performance of conventional imaging modalities remains suboptimal. Artificial intelligence (AI) technologies have emerged as promising tools in assisting image analysis and improving diagnostic accuracy of PAS. Therefore, this study aims to systematically evaluate the diagnostic performance of AI models in diagnosing PAS and predicting adverse pregnancy outcomes (APO) associated with PAS.

Methods: A systematic search was conducted across multiple databases, including PubMed, Embase, and Cochrane Library, to identify studies assessing the diagnostic performance of AI-based models in PAS or their ability to predict APO. Diagnostic metrics such as sensitivity, specificity, area under the curve (AUC), positive likelihood ratio, negative likelihood ratio, and summary receiver operating characteristic (SROC) curves were pooled to evaluate diagnostic accuracy. Heterogeneity was assessed using Cochran Q and I2 statistics, and meta-regression and subgroup analysis were conducted to examine potential sources of heterogeneity. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was utilized to assess the study quality.

Results: A total of 16 studies involving 4,457 participants were included. The pooled results showed that AI models exhibit high sensitivity (88%, 95% CI: 81%-93%) and specificity (88%, 95% CI: 76%-94%) for diagnosing PAS, with an excellent AUC of 0.94 (95% CI: 0.91-0.96). Moreover, AI models also indicated promising performance in predicting clinically significant APO such as massive hemorrhage and hysterectomy, yielding a pooled sensitivity of 80% (95% CI: 73%-85%), specificity of 86% (95% CI: 78%-92%), and AUC of 0.87 (95% CI: 0.84-0.90). Meta-regression and subgroup analysis identified study design as a primary source of heterogeneity.

Conclusions: AI algorithms exhibited favorable performance for diagnosing PAS and predicting APO associated with PAS, suggesting the clinical translation potential of AI in enhancing the efficiency of diagnostic workflows and potentially reducing maternal morbidity and mortality.

Keywords: adverse pregnancy outcomes; artificial intelligence; diagnosis; placenta accreta spectrum disorders; prediction.