From Retrospective to Prospective: The Contribution of Artificial Intelligence Dystocia Algorithm in Understanding and Preventing Dystocia in Intrapartum Cesarean Delivery

The management of dystocia represents one of the most significant challenges in modern obstetrics; however, paradoxically, the World Health Organization (WHO) had not yet provided a unified definition of this condition.1 Dystocia is responsible for a substantial proportion of cesarean deliveries worldwide. The journey from this definitional confusion to a potentially preventive framework illustrated how sequential innovations in classification and measurement could transform obstetric practice. Robson’s Ten Group Classification System (TGCS)2 first illuminated the magnitude of the problem through epidemiological clarity. By stratifying cesarean sections into mutually exclusive categories, the TGCS revealed significant variations in cesarean rates across different obstetric populations, with particularly high rates observed in specific groups such as Group 2A (nulliparous women with induced labor). Although this stratification highlighted the cases in which cesareans are more frequently used, it did not explain the reasons. While the TGCS quantified and categorized the problem, it did not identify the specific factors leading to cesareans, with a particular focus on dystocia. The existing literature reflects a conceptual confusion through the use of descriptive adjectives associated with dystocia, such as “prolonged”, “arrested”, “protracted”, “failed”, and “abnormal” populated obstetric texts.3 This linguistic inconsistency represents the fundamental lack of mechanistic understanding of labor dysfunction. The subsequent development of the Intrapartum Cesarean Delivery Classification System (ICDCS) by Robson et al.4 marked a crucial step toward standardization. Through retrospective analysis of > 150,000 cesarean deliveries across 18 years, the ICDCS identified seven distinct patterns of labor dysfunction, providing for the first time a unified nomenclature for post-facto classification. These categories offered a systematic approach: inadequate contractions with and without oxytocin, hypercontractile patterns, cervical arrest despite adequate contractions, and various failure patterns in descent and rotation. This classification transformed dystocia from an amorphous concept into discrete patterns. However, the ICDCS is inherently limited by its retrospective and descriptive nature, functioning as an autopsy rather than a diagnostic tool. Figure 1 illustrates the conceptual evolution in this field, demonstrating the paradigm shift from retrospective classification systems to prospective management approaches. The upper section shows the historical progression from undefined dystocia by the WHO through TGCS quantification to ICDCS post-facto classification, while the lower section presents the prospective Artificial Intelligence Dystocia Algorithm (AIDA) framework with its real-time risk stratification capabilities.Figure 1: From retrospective to prospective: AIDA’s contribution. Conceptual framework illustrating the evolution from retrospective to prospective management of dystocia in intrapartum cesarean delivery. The upper retrospective section demonstrates the historical progression from undefined dystocia (WHO) through epidemiological quantification (TGCS) to post-facto classification (ICDCS with 7 categories). The paradigm shift (dashed line) separates retrospective from prospective approaches. The lower prospective section presents the AIDA system that enables real-time risk stratification (classes 0-4) through four ultrasonographic parameters (AoP, HSD, MLA, AD) and targeted intervention across five physiological domains: Neurogenic (stress response), metabolic (energy production), tissue (biomechanical properties), contractile (coordination patterns), and geometric (spatial relationships). This paradigm shift transforms obstetric practice from reactive crisis management to proactive prevention through continuous monitoring and domain-specific interventions. AD: Attitude distance; AIDA: Artificial Intelligence Dystocia Algorithm; AoP: Angle of progression; HSD: Head-symphysis distance; ICDCS: Intrapartum Cesarean Delivery Classification System; MLA: Midline angle; TGCS: Robson’s Ten Group Classification System; WHO: World Health Organization.The AIDA has represented this paradigm shift from retrospective classification to prospective risk stratification.5–8 Using standardized ultrasonographic parameters—angle of progression, head-symphysis distance, midline angle, and attitude distance—AIDA stratified patients into cesarean risk classes in real time: from class 0 (no cesarean risk) to class 4 (very high cesarean risk). This real-time assessment transformed dystocia management from reactive to potentially preventive. The framework enabled a feedback mechanism for interventions targeting five distinct physiological domains underlying dystocia. The neurogenic domain showed the mechanism underlying the function of catecholaminergic neurofibers in the gravid cervix as myometrial pacemakers,9 providing the mechanistic link between psychological stress and dystocic patterns. The metabolic domain emphasized labor as a process requiring high levels of energy. The tissue domain focused on biomechanical properties, while the contractile domain addressed coordination beyond simple force generation. The geometric domain served as an intervention target and as a “thermometer” for the function of the entire system. The practical implementation of this integrated framework followed a systematic iterative process, as depicted in Figure 2. The AIDA feedback loop shows the methodology used by clinicians to measure the four parameters through standardized ultrasound, obtaining objective risk classification. Based on correlations of risk class and clinical patterns with ICDCS categories, they selected targeted interventions addressing the suspected dominant physiological domain. For instance, a class 3 AIDA with hypercontractile patterns suggested neurogenic dysfunction, prompting interventions for stress reduction. After appropriate intervals—from 30 min for neurogenic interventions to 90 min for metabolic corrections—clinicians re-measured the parameters. The comparison between initial and subsequent classifications provided objective feedback on intervention efficacy, creating a dynamic approach that offers immediate measurement of the effectiveness of each intervention.Figure 2: The AIDA feedback loop: Operational mechanism for real-time dystocia prevention. Schematic representation of the AIDA feedback loop operational mechanism. The system enables real-time monitoring and intervention during labor through iterative cycles of measurement, domain identification, targeted intervention, and re-assessment. The central hub shows AIDA risk classification (classes 0-4, from green/zero risk to dark red/very high risk). Step 1 (blue box): Initial measurement of four ultrasonographic parameters (AoP, HSD, MLA, AD). Step 2 (purple box): Identification of dominant physiological domain based on ICDCS pattern recognition. Step 3 (red box): Domain-specific interventions tailored to identified dysfunction. Step 4 (green box): Re-measurement after appropriate time intervals (30-120 minutes depending on domain). Dashed arrows indicate workflow progression; The central ellipse represents continuous iteration throughout labor. Time intervals for re-measurement vary by physiological domain: Neurogenic (30-45 min), metabolic (60-90 min), tissue (90-120 min), and contractile (45-60 min). The continuous comparison of risk classifications provides objective validation of intervention efficacy, enabling evidence-based clinical decision-making throughout labor progression. AD: Attitude distance; AIDA: Artificial Intelligence Dystocia Algorithm; AoP: Angle of progression; HSD: Head-symphysis distance; ICDCS: Intrapartum Cesarean Delivery Classification System; MLA: Midline angle; TGCS: Robson’s Ten Group Classification System.This integrated framework suggested markedly different prevention potentials across ICDCS categories. Category 3, being primarily neurogenic, offered high prevention potential through stress reduction, with AIDA measurements confirming success within 30–45 min. Categories involving metabolic and mechanical compromise presented moderate prevention opportunities with longer response times. Although categories representing structural limitations showed limited prevention potential, they could still benefit from optimized management. The ability to measure the effectiveness of interventions in real time fundamentally changed decision-making in obstetrics. This approach enables clinicians to assess intervention success within defined timeframes and modify strategies as needed. Moreover, it creates a learning loop for each labor, where clinical decisions are continuously refined based on measurable outcomes. The framework highlights the need for urgent research focusing on measurable parameters within each domain, incorporating temporal response patterns. For instance, cortisol levels could quantify neurogenic dysfunction, lactate accumulation could indicate metabolic compromise, tissue elastography could assess biomechanical properties, tocodynamometry patterns could evaluate contractile coordination, and AIDA measurements could provide geometric feedback. Research protocols should incorporate intervention efficacy and response timing, creating temporal maps of domain-specific intervention kinetics. This approach suggests a revolutionary experimental paradigm where researchers could develop domain-specific interventions based on mechanistic evidence, use AIDA as a surrogate outcome to validate efficacy in real time, and establish temporal response curves for different intervention-domain combinations. The transformation from observational to interventional science with continuous feedback could revolutionize obstetric practice. The journey from definitional confusion to mechanistic clarity with operational feedback represented more than academic progress; It offered promise for improving outcomes through precision medicine. The prevention of cesarean sections through targeted domain intervention may lead to reduced maternal morbidity, improved neonatal outcomes, and enhanced maternal empowerment through physiological birth. The scientific community is currently facing an ethical imperative to validate this framework prospectively, develop domain-specific biomarkers with defined response times, and create standardized yet customizable protocols. The integration of artificial intelligence in obstetrics10 through AIDA represents technological advancement and a fundamental reconceptualization of labor management. Through such concerted effort, it may be possible to determine the pathophysiology of dystocia and identify approaches for its prevention with measurable, real-time validation of preventive efforts. For this purpose, the tools were available, the framework was established, and the operational mechanism was defined. What remained was the collective will to implement this paradigm shift and realize its potential for improving birth outcomes globally. Funding This research received no external funding. Author Contributions Concept or design: Tinelli, Malgieri. Acquisition of data: Tinelli, Malgieri. Analysis or interpretation of data: Stark, Pecorella, Malvasi. Drafting of the article: Tinelli, Malgieri. Critical revision for important intellectual content: Stark, Pecorella, Malvasi. Conflicts of Interest The authors declare that they have no conflicts of interest and nothing to disclose. All authors had full access to the data, contributed to the study, approved the final version for publication, and take responsibility for its accuracy and integrity.