Artificial Intelligence Dystocia Algorithm (AIDA) as a Decision Support System in Transverse Fetal Head Position

Abstract

Transverse fetal head position during labor is associated with increased rates of operative deliveries and cesarean sections. Traditional assessment methods rely on digital examination, which can be inaccurate in cases of prolonged labor. Intrapartum ultrasound offers improved diagnostic capabilities, but standardized interpretation frameworks are needed. This study aimed to evaluate the significance of appropriate assessment and management of transverse fetal head position during labor, with particular emphasis on the correlation between geometric parameters and delivery outcomes. Additionally, the investigation analyzed the potential role of Artificial Intelligence Dystocia Algorithm (AIDA) as an innovative decision support system in standardizing diagnostic approaches and optimizing clinical decision-making in cases of fetal malposition. This investigation was conducted as a focused secondary analysis of data originally collected for the development and validation of the Artificial Intelligence Dystocia Algorithm (AIDA). The study examined 66 cases of transverse fetal head position from a cohort of 135 nulliparous women with prolonged second-stage labor across three Italian hospitals. Cases were stratified by Midline Angle (MLA) measurements into classic transverse (≥75°), near-transverse (70-74°), and transitional (60-69°) positions. Four geometric parameters (Angle of Progression, Head-Symphysis Distance, Midline Angle, and Asynclitism Degree) were evaluated using the AIDA classification system. The predictive capabilities of three machine learning algorithms (Support Vector Machine, Random Forest, and Multilayer Perceptron) were assessed, and delivery outcomes were analyzed. The AIDA system successfully categorized labor dystocia into five distinct classes, with strong predictive value for delivery outcomes. A clear gradient of cesarean delivery risk was observed across the spectrum of transverse positions (100%, 93.1%, and 85.7% for near-transverse, classic transverse, and transitional positions, respectively). All cases classified as AIDA Class 4 required cesarean delivery regardless of the specific MLA value. Machine learning algorithms demonstrated high predictive accuracy, with Random Forest achieving 95.5% overall accuracy across the study cohort. The presence of concurrent asynclitism with transverse position was associated with particularly high rates of cesarean delivery. Among the seven cases that achieved vaginal delivery despite transverse positioning, none belonged to the classic transverse positions group, and five (71.4%) exhibited at least one parameter classified as favorable. The integration of artificial intelligence through AIDA as a decision support system, combined with intrapartum ultrasound, offered a promising approach for objective assessment and management of transverse fetal head position. The AIDA classification system's integration of multiple geometric parameters, with particular emphasis on precise Midline Angle (MLA) measurement in degrees, provided superior predictive capability for delivery outcomes compared to qualitative position assessment alone. This multidimensional approach enabled more personalized and evidence-based management of malpositions during labor, potentially reducing unnecessary interventions while identifying cases where expectant management might be futile. Further prospective studies are needed to validate the predictive capability of this decision support system and its impact on clinical decision-making in real-time labor management.

Keywords: Artificial Intelligence Dystocia Algorithm (AIDA); cesarean delivery; decision support system; intrapartum ultrasound; labor dystocia; machine learning; midline angle; occiput transverse position; transverse fetal head position.

Figure 1

This figure illustrates how to perform a correct ultrasound (US) evaluation of the transverse fetal head during the second stage of labor. (A) Transabdominal transverse scan of Right Occiput Transverse (ROT) position; (B) translabial transverse scan of ROT position; (C) transabdominal transverse scan of Left Occiput Transverse (LOT) position; (D) translabial transverse scan of LOT position. Red lines indicate measurement reference points for geometric parameter assessment.

Figure 2

Schematic representation of the transverse position of the fetal head in the maternal pelvis. When the angle of rotation is between 75° and 105°, the position is called LOT (if the fetal occiput is on the maternal left side); when the angle of rotation is between 255° and 285°, the position is called ROT (if the occiput is on the maternal right side). Next to the degree measurements written in black are red notations of the positions according to the “clock face”.

Figure 3

(A) Angle of progression (AoP): the drawing on the right and the US photo on the left show the AoP with the fetal head in Right Occiput Transverse (ROT) position; (B) fetal head–symphysis distance (HSD): the drawing on the right and the US photo on the left show the HSD (red line) with the fetal head in ROT position; (C) midline angle (MLA): the drawing on the right and the US photo on the left shows the MLA with the fetal head in ROT position; (D) asynclitism degree (AD): the drawing on the right and the US photo on the left show the AD with the fetal head in ROT position.

Figure 4

AIDA classifier (N = 135) showing the four geometric parameters (HSD, AD, MLA, AoP) with their measurement ranges and color-coded classification system for predicting delivery outcomes. Green zones indicate favorable conditions for vaginal delivery, yellow zones represent borderline measurements, and red zones suggest high risk for cesarean delivery. Values and classification thresholds as described in Malvasi, Malgieri et al. [33].

Figure 5

Distribution of cases according to Midline Angle (MLA) values. (A) “Classic Transverse Positions” with MLA values ≥75° (n = 29, 43.9% of study subset). (B) “Near-Transverse Positions” with MLA values between 70° and 74° (n = 9, 13.6%). (C) “Transitional Positions” with MLA values between 60° and 69° (n = 28, 42.4%).

Figure 6

Asynclitism degree and midline angle values for the 66 cases included in the analysis with varying degrees of transverse fetal head position.

Figure 7

Angle of progression and fetal head–symphysis distance values for the 66 cases included in the analysis with varying degrees of transverse fetal head position.

Figure 8

Distribution of delivery outcomes across AIDA Classes 2, 3, and 4 and MLA values. Each panel displays delivery outcomes categorized as immediate intrapartum cesarean delivery (ICD, blue diamonds), cesarean delivery after failed intervention (ICD AFTER FAILURE, yellow crosses), and operative vaginal delivery (Operative VD, gray triangles).