The Contribution of AIDA (Artificial Intelligence Dystocia Al-2gorithm) to Cesarean Section within Robson classification 3group

The escalating global cesarean section (CS) rates presented a sig- nificant challenge in contemporary obstetric practice. The Robson classification system became a widely adopted framework for analyzing CS rates; however, classes such as 2 and 2A (nulliparous women with term singleton cephalic pregnancies, particularly those with induced labor) demonstrated high cesarean rates of 33.5% compared to 18.4% in C i spontaneous labor counterparts, which could not be fully explained by the classification t alone. Objectives. This paper explored how the Artificial Intelligence Dystocia Algorithm a (AIDA) could enhance the Robson system by providing detailed information on geomet- t i ric dystocia, thereby facilitating a better understanding of factors contributing to CS and o developing more targeted reduction strategies. Methods. The authors conducted a com- n prehensive literature review analyzing both classification systems across multiple data- : bases. The researchers examined studies implementing the Robson classification globally and evaluated how AIDA's objective geometric assessment could complement this ap- proach. The investigators then developed a theoretical framework for integration, identi- fying implementation strategies for various resource settings while acknowledging inher- ent limitations. Results. AIDA uniquely categorized labor cases into five classes (0-4) by analyzing four key geometric parameters measured through intrapartum ultrasound: an- gle of progression (AoP), asynclitism degree (AD), head-symphysis distance (HSD), and midline angle (MLA). The analysis revealed that 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. The proposed integration created a combined classification (e.g., "Robson 2A/AIDA 3") that provided both population-level and individual geometric risk assessment. Implementation strategies were developed for high-, intermediate-, and low-resource settings, acknowledging that full implementation required ultrasound equipment and specialized training. Conclusions. The integration of AIDA with the Robson classification represented a potentially valuable advancement inthe assessment of labor progress and CS risk, combining population-level stratification with individual-level geometric assessment to enable more personalized obstetric care. This approach enhanced risk stratification beyond demographic and clinical factors, po- tentially facilitating more targeted interventions for specific geometric challenges. While implementation faced resource and training challenges, the potential impact on reducing unnecessary CSs was substantial, particularly for nulliparous women whose first delivery mode significantly influenced future birth outcomes. The authors concluded that future validation studies across diverse settings were needed to establish the clinical utility of this innovative approach to managing labor and reducing cesarean deliveries