Malvasi Antonio, Malgieri Lorenzo E, Stark Michael, Di Naro Edoardo, Farine Dan, Baldini Giorgio Maria, Dellino Miriam, Yassa Murat, Tinelli Andrea, Vimercati Antonella, Difonzo Tommaso
Obstetrics and Gynaecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari "Aldo Moro", 70124 Bari, Italy.
The New European Surgical Academy (NESA), 10117 Berlin, Germany.
J Imaging. 2025 Aug 16;11(8):276. doi: 10.3390/jimaging11080276.
Global cesarean section (CS) rates continue to rise, with the Robson classification widely used for analysis. However, Robson Group 2A patients (nulliparous women with induced labor) show disproportionately high CS rates that cannot be fully explained by demographic factors alone. This study explored how the Artificial Intelligence Dystocia Algorithm (AIDA) could enhance the Robson system by providing detailed information on geometric dystocia, thereby facilitating better understanding of factors contributing to CS and developing more targeted reduction strategies. The authors conducted a comprehensive literature review analyzing both classification systems across multiple databases and developed a theoretical framework for integration. AIDA categorized labor cases into five classes (0-4) by analyzing four key geometric parameters measured through intrapartum ultrasound: angle of progression (AoP), asynclitism degree (AD), head-symphysis distance (HSD), and midline angle (MLA). Significant asynclitism (AD ≥ 7.0 mm) was strongly associated with CS regardless of other parameters, potentially explaining many "failure to progress" cases in Robson Group 2A patients. The proposed integration created a combined classification providing both population-level and individual geometric risk assessment. The integration of AIDA with the Robson classification represented a potentially valuable advancement in CS risk assessment, combining population-level stratification with individual-level geometric assessment to enable more personalized obstetric care. Future validation studies across diverse settings are needed to establish clinical utility.
全球剖宫产（CS）率持续上升，罗布森分类法被广泛用于分析。然而，罗布森2A组患者（引产的初产妇）的剖宫产率高得不成比例，仅人口统计学因素无法完全解释这一现象。本研究探讨了人工智能难产算法（AIDA）如何通过提供有关几何性难产的详细信息来增强罗布森系统，从而有助于更好地理解导致剖宫产的因素并制定更具针对性的降低策略。作者进行了一项全面的文献综述，分析了多个数据库中的两种分类系统，并建立了一个整合的理论框架。AIDA通过分析经产时超声测量的四个关键几何参数，将分娩病例分为五类（0 - 4）：进展角度（AoP）、斜度（AD）、头部-耻骨联合距离（HSD）和中线角度（MLA）。无论其他参数如何，显著斜度（AD≥7.0 mm）与剖宫产密切相关，这可能解释了罗布森2A组患者中许多“产程停滞”的病例。提议的整合创建了一个综合分类，提供了人群水平和个体几何风险评估。AIDA与罗布森分类法的整合代表了剖宫产风险评估中一个潜在的有价值的进展，将人群水平分层与个体水平几何评估相结合，以实现更个性化的产科护理。需要在不同环境中进行未来的验证研究以确定其临床效用。
