Miyata Kohei, Shibata Chihiro, Fukunishi Hiroaki, Hemmi Kazunari, Kinoshita Hayato, Hirakawa Toyofumi, Urushiyama Daichi, Kurakazu Masamitsu, Yotsumoto Fusanori
Obstetrics and Gynecology, Faculty of Medicine, Fukuoka University, Fukuoka, JPN.
Advanced Sciences, Graduate School of Science and Engineering, Hosei University, Tokyo, JPN.
Cureus. 2025 Jan 31;17(1):e78282. doi: 10.7759/cureus.78282. eCollection 2025 Jan.
Cardiotocography (CTG) has long been the standard method for monitoring fetal status during delivery. Despite its widespread use, human error and variability in CTG interpretation contribute to adverse neonatal outcomes, with over 70% of stillbirths, neonatal deaths, and brain injuries potentially avoidable through accurate analysis. Recent advancements in artificial intelligence (AI) offer opportunities to address these challenges by complementing human judgment. This study experimentally compared the diagnostic accuracy of AI and human specialists in predicting fetal asphyxia using CTG data. Machine learning (ML) and deep learning (DL) algorithms were developed and trained on 3,519 CTG datasets. Human specialists independently assessed 50 CTG figures each through web-based questionnaires. A total of 984 CTG figures from singleton pregnancies were evaluated, and outcomes were compared using receiver operating characteristic (ROC) analysis. Human diagnosis achieved the highest area under the curve (AUC) of 0.693 (p = 0.0003), outperforming AI-based methods (ML: AUC = 0.514, p = 0.788; DL: AUC = 0.524, p = 0.662). Although DL-assisted judgment improved sensitivity and identified cases missed by humans, it did not surpass the accuracy of human judgment alone. Combining human and AI predictions yielded a lower AUC (0.693) than human diagnosis alone, but improved specificity (91.92% for humans, 98.03% for humans and DL), highlighting AI's potential to complement human judgment by reducing false-positive rates. Our findings underscore the need for further refinement of AI algorithms and the accumulation of CTG data to enhance diagnostic accuracy. Integrating AI into clinical workflows could reduce human error, optimize resource allocation, and improve neonatal outcomes, particularly in resource-limited settings. These advancements promise a future where AI assists obstetricians in making more objective and accurate decisions during delivery.
产时胎心监护（CTG）长期以来一直是分娩期间监测胎儿状况的标准方法。尽管其应用广泛，但CTG解读中的人为误差和变异性会导致不良新生儿结局，超过70%的死产、新生儿死亡和脑损伤通过准确分析可能避免。人工智能（AI）的最新进展为通过补充人类判断来应对这些挑战提供了机会。本研究通过实验比较了AI和人类专家使用CTG数据预测胎儿窒息的诊断准确性。开发了机器学习（ML）和深度学习（DL）算法，并在3519个CTG数据集上进行训练。人类专家通过基于网络的问卷各自独立评估50个CTG图形。共评估了984个单胎妊娠的CTG图形，并使用受试者操作特征（ROC）分析比较结果。人类诊断的曲线下面积（AUC）最高，为0.693（p = 0.0003），优于基于AI的方法（ML：AUC = 0.514，p = 0.788；DL：AUC = 0.524，p = 0.662）。虽然DL辅助判断提高了敏感性并识别出人类漏诊的病例，但并未超过单独人类判断的准确性。将人类和AI预测相结合产生的AUC（0.693）低于单独人类诊断，但提高了特异性（人类为91.92%，人类和DL为98.03%），突出了AI通过降低假阳性率来补充人类判断的潜力。我们的研究结果强调了进一步优化AI算法和积累CTG数据以提高诊断准确性的必要性。将AI整合到临床工作流程中可以减少人为误差，优化资源分配，并改善新生儿结局，特别是在资源有限的环境中。这些进展预示着一个未来，即AI协助产科医生在分娩期间做出更客观准确的决策。
