The occiput posterior (OP) fetal head position is the most common malposition during labor and is associated with prolonged labor, operative delivery, and cesarean section. Conventional assessment often relies on digital examination, and the clinical significance of OP may lie along a spectrum rather than as a binary diagnosis. The Artificial Intelligence Dystocia Algorithm (AIDA) integrates four objective intrapartum ultrasound parameters (Angle of Progression [AoP], Head–Symphysis Distance [HSD], Midline Angle [MLA], and Asynclitism Degree [AD]) into a five-class ordinal classification (Classes 0–4). This investigation is a focused secondary subgroup analysis of 79 OP cases drawn from a single-cohort dataset of 135 nulliparous women with prolonged second-stage labor originally collected for the development of the AIDA. Only Branch 1 of the AIDA (the deterministic threshold-based classification, with cut-offs originally derived via Decision Tree on the parent cohort, N = 135) was applied; Branch 2 (the case-level machine-learning predictors) was not used, and no predictive model was trained or validated in this study. Cesarean delivery rates rose monotonically across AIDA classes, from no cesareans in Class 0 to all cases delivering by cesarean in Class 4, with a clear gradient across intermediate classes; full numerical results, confidence intervals, and effect sizes are reported in the Results section. Because the AIDA thresholds were derived from the same parent cohort, the analysis is best interpreted as a within-cohort subgroup evaluation rather than as independent validation. The observed class-graded outcome distribution is consistent with the hypothesis that in OP labors, the AIDA class assignment itself may carry clinically relevant information on the risk of intrapartum cesarean delivery; this remains hypothesis generating, and confirmation in independent prospective cohorts is required before AIDA-class assignment can be regarded as an established risk-stratification descriptor in OP labors.

Artificial Intelligence Dystocia Algorithm (AIDA) for Risk Stratification of Occiput Posterior Fetal Head Position / Malvasi, Antonio; Baldini, Giorgio Maria; Difonzo, Tommaso; Cara, Iris; Cerbone, Marco; Dellino, Miriam; Vimercati, Antonella; Mappa, Ilenia; Rizzo, Giuseppe; Tinelli, Andrea; Cicinelli, Ettore; Naro, Edoardo Di; Malgieri, Lorenzo E.. - In: JOURNAL OF IMAGING. - ISSN 2313-433X. - 12:6(2026). [10.3390/jimaging12060230]

Artificial Intelligence Dystocia Algorithm (AIDA) for Risk Stratification of Occiput Posterior Fetal Head Position

Mappa, Ilenia;Rizzo, Giuseppe;
2026

Abstract

The occiput posterior (OP) fetal head position is the most common malposition during labor and is associated with prolonged labor, operative delivery, and cesarean section. Conventional assessment often relies on digital examination, and the clinical significance of OP may lie along a spectrum rather than as a binary diagnosis. The Artificial Intelligence Dystocia Algorithm (AIDA) integrates four objective intrapartum ultrasound parameters (Angle of Progression [AoP], Head–Symphysis Distance [HSD], Midline Angle [MLA], and Asynclitism Degree [AD]) into a five-class ordinal classification (Classes 0–4). This investigation is a focused secondary subgroup analysis of 79 OP cases drawn from a single-cohort dataset of 135 nulliparous women with prolonged second-stage labor originally collected for the development of the AIDA. Only Branch 1 of the AIDA (the deterministic threshold-based classification, with cut-offs originally derived via Decision Tree on the parent cohort, N = 135) was applied; Branch 2 (the case-level machine-learning predictors) was not used, and no predictive model was trained or validated in this study. Cesarean delivery rates rose monotonically across AIDA classes, from no cesareans in Class 0 to all cases delivering by cesarean in Class 4, with a clear gradient across intermediate classes; full numerical results, confidence intervals, and effect sizes are reported in the Results section. Because the AIDA thresholds were derived from the same parent cohort, the analysis is best interpreted as a within-cohort subgroup evaluation rather than as independent validation. The observed class-graded outcome distribution is consistent with the hypothesis that in OP labors, the AIDA class assignment itself may carry clinically relevant information on the risk of intrapartum cesarean delivery; this remains hypothesis generating, and confirmation in independent prospective cohorts is required before AIDA-class assignment can be regarded as an established risk-stratification descriptor in OP labors.
2026
: Artificial Intelligence Dystocia Algorithm (AIDA); occiput posterior position; machine learning; intrapartum ultrasound; labor dystocia; persistent occiput posterior; risk stratification
01 Pubblicazione su rivista::01a Articolo in rivista
Artificial Intelligence Dystocia Algorithm (AIDA) for Risk Stratification of Occiput Posterior Fetal Head Position / Malvasi, Antonio; Baldini, Giorgio Maria; Difonzo, Tommaso; Cara, Iris; Cerbone, Marco; Dellino, Miriam; Vimercati, Antonella; Mappa, Ilenia; Rizzo, Giuseppe; Tinelli, Andrea; Cicinelli, Ettore; Naro, Edoardo Di; Malgieri, Lorenzo E.. - In: JOURNAL OF IMAGING. - ISSN 2313-433X. - 12:6(2026). [10.3390/jimaging12060230]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1768972
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