⚡ Quick Summary
This systematic review and meta-analysis evaluated the diagnostic performance of Artificial Intelligence (AI) models in diagnosing Placenta Accreta Spectrum Disorders (PAS) and predicting adverse pregnancy outcomes (APO). The findings revealed that AI models achieved a sensitivity of 88% and a specificity of 88%, indicating their potential to significantly enhance diagnostic accuracy in obstetric care.
🔍 Key Details
- 📊 Dataset: 16 studies involving 4,457 participants
- 🧩 Diagnostic metrics: Sensitivity, specificity, AUC, positive and negative likelihood ratios
- ⚙️ Technology: AI-based models for image analysis
- 🏆 Performance: Sensitivity 88%, Specificity 88%, AUC 0.94 for PAS diagnosis
🔑 Key Takeaways
- 🤖 AI models show high diagnostic accuracy for PAS.
- 📈 AUC of 0.94 indicates excellent performance in diagnosing PAS.
- 💡 AI also predicts significant adverse pregnancy outcomes with a sensitivity of 80% and specificity of 86%.
- 🔍 Study design was identified as a primary source of heterogeneity in results.
- 🌍 Potential for AI to reduce maternal morbidity and mortality through improved diagnostics.
- 📚 Quality Assessment: QUADAS-2 tool used to evaluate study quality.
📚 Background
Diagnosing Placenta Accreta Spectrum Disorders (PAS) presents significant challenges in prenatal care, often leading to adverse pregnancy outcomes. Traditional imaging techniques have shown suboptimal diagnostic performance, prompting the exploration of Artificial Intelligence (AI) as a promising alternative for enhancing diagnostic accuracy and predicting potential complications.
🗒️ Study
This systematic review involved a comprehensive search across multiple databases, including PubMed and Embase, to identify studies that assessed the diagnostic performance of AI models in PAS. The researchers pooled various diagnostic metrics to evaluate the effectiveness of these AI technologies in clinical settings.
📈 Results
The analysis included a total of 16 studies with 4,457 participants. The pooled results demonstrated that AI models achieved a sensitivity of 88% (95% CI: 81%-93%) and a specificity of 88% (95% CI: 76%-94%) for diagnosing PAS. Furthermore, the models showed promising results in predicting clinically significant adverse pregnancy outcomes, with a pooled sensitivity of 80% (95% CI: 73%-85%) and specificity of 86% (95% CI: 78%-92%).
🌍 Impact and Implications
The findings from this study suggest that AI algorithms can significantly enhance the efficiency of diagnostic workflows in obstetric care. By improving the accuracy of PAS diagnosis and predicting adverse outcomes, AI has the potential to reduce maternal morbidity and mortality, ultimately leading to better health outcomes for mothers and their babies.
🔮 Conclusion
This systematic review highlights the transformative potential of AI in diagnosing Placenta Accreta Spectrum Disorders and predicting adverse pregnancy outcomes. As AI technologies continue to evolve, their integration into clinical practice could lead to more accurate and timely diagnoses, improving patient care in obstetrics. Continued research in this area is essential to fully realize the benefits of AI in healthcare.
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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.
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.
Author: [‘Chen K’, ‘Xue M’, ‘Meng X’, ‘Li G’, ‘Wang X’, ‘Long Y’, ‘Li H’]
Journal: Int J Surg
Citation: Chen K, et al. 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. 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. 2025; (unknown volume):(unknown pages). doi: 10.1097/JS9.0000000000004443