Description

One of the hardest processes to overcome during labor is the positioning of the fetal head during engagement and progression in the birth canal. These malpositions and malrotations are also common causes of dystocic labor and arrest of progression, which necessitates a surgical delivery. Traditionally, a vaginal digital examination is used to assess the fetal head position and engagement. The degree of fetal engagement and progression is delineated by different planes of the pelvis, which have values ranging from -5 (5 cm above the ischial spines) to +5 (5 cm below the ischial spines with the fetal head visible at the introitus). The head is considered engaged when the leading point of the skull touches the ischial spine plane; this is referred to as station 0. Moreover, when the parietal bone is the presenting feature, asynclitism is identified. When the front parietal bone manifests, the condition is identified as anterior asynclitism; when the posterior parietal bone presents, the condition is described as posterior asynclitism. The fetal head's minor "tilted" posture in the birth canal, however, is a result of the fetal head's physiological adjustment to the mother's pelvis throughout labor. Malposition and malrotation of the fetal head in the birth canal, characterized by significant asynclitism, might result in dystocia that necessitates surgical delivery. The 15% of people have asynclitism, with anterior asynclitism being more common than posterior asynclitism. The traditional classification of degree of fetal engagement and progression has been criticized as inaccurate and poorly reproducible. Furthermore, the failure of instrumental delivery, which may reach up to 10%. Errors in the diagnosis of the fetal head station can have major implications during labor and adverse perinatal outcomes, such as acidemia, fetal traumas, intracranial hemorrhages, and low Apgar scores after an emergency cesarean section for failed operative vaginal deliveries. Many researchers have proposed many ultrasonographic parameters, collected during labor and delivery, to evaluate the engagement, descent, and internal rotation of the fetal head in the birth canal. Thus, we used intrapartum ultrasound parameters, measured in all the women during labor and recorded to be measured by artificial intelligence and machine learning algorithms, called AIDA (Artificial Intelligence Dystocia Algorithm), which incorporates a human-in-the-loop approach, that is, to use AI (artificial intelligence) algorithms that prioritize the physician's decision and explainable artificial intelligence (XAI). The AIDA was structured into five classes. After a number of "geometric parameters" were collected, the data obtained from the AIDA analysis were entered into a red, yellow, or green zone, linked to the analysis of the progress of labor. Using the AIDA analysis, we were able to identify five reference classes for patients in labor, each of which had a certain sort of birth outcome. A 100% cesarean birth prediction was made in two of these five classes. The use of artificial intelligence, through the evaluation of certain obstetric parameters in specific decision-making algorithms, allows physicians to systematically understand how the results of the algorithms can be explained. This approach can be useful in evaluating the progress of labor and predicting the labor outcome, including spontaneous, whether operative VD (vaginal delivery) should be attempted, or if ICD (intrapartum cesarean delivery) is preferable or necessary. In this investigation, we will seek, with a critical eye, to evaluate the implications of some geometric parameters measured using an intrapartum ultrasound trying to highlight all the possible complications and problems of a dystocic and eutocic labor, with an evaluation of a big volume of women in pregnancy and during labor.

Currently, the Artificial Intelligence Dystocia Algorithm (AIDA) represents a significant advancement in the application of artificial intelligence to intrapartum care. Developed through two key studies, AIDA 1 and AIDA 2, this innovative novel approach combines and integrates multiple geometric parameters measured through intrapartum ultrasonography with machine learning techniques and algorithms to assess labor progress and predict delivery outcomes. The selection of algorithms, methodologies for performance evaluation, and the metrics employed were meticulously delineated in the respective publications on AIDA 1 and AIDA 2. These seminal papers provide comprehensive elucidations of the machine learning techniques utilized, the rationale underpinning their selection, and the rigorous performance metrics applied to assess their efficacy in predicting labor outcomes. The high values obtained demonstrate the robust discriminative capability of the algorithms chosen in distinguishing between different delivery outcomes, underscoring the potential clinical utility of the AIDA approach in obstetric decision-making.

The AIDA Methodology A two-step methodology was applied to the data sample. The initial step, the correlation analysis, employed Pearson's correlation coefficient to ascertain that the four geometric parameters exhibited negligible or statistically insignificant correlations, ensuring each parameter contributed unique information regarding labor progression. The subsequent step, the machine learning algorithm selection, involved applying diverse supervised machine learning algorithms to the four geometric parameters in conjunction with physician-determined delivery outcomes. The predictive performance of each algorithm was quantified to identify the most efficacious models.

The AIDA Classification System The classification system is predicated upon the identification of cut-off values for each geometric parameter associated with intrapartum cesarean delivery (ICD) and non-ICD outcomes. A decision tree algorithm was utilized to establish these cut-offs: for each parameter, values strongly associated with non-ICD outcomes were designated as "green", those highly correlated with ICD were classified as "red", and in cases where the data sample revealed intermediate ranges of uncertainty, a "yellow" designation was applied.

Having assigned a color to each individual value for the four geometric parameters for every parturition, the AIDA classification system employs a structured approach to categorizing each labor event into one of five distinct classes. This color-coded stratification of the geometric parameters facilitates a nuanced assessment of labor progression, enabling a more refined classification of each case. AIDA class 0 denotes all four parameters being within the green zone, indicating a high probability of non-ICD outcomes. AIDA class 1 indicates one parameter being in the red or yellow zone and three being in the green zone. AIDA class 2 signifies two parameters being in the red or yellow zone and two being in the green zone. AIDA class 3 represents three parameters being in the red or yellow zone and one being in the green zone. AIDA class 4 denotes all four parameters being within the red or yellow zone, suggesting a heightened likelihood of ICD.

The AIDA's Prediction Performance The integration of the delivery predictions obtained from the three best performing algorithms with the AIDA classification system yielded significantly improved results. A particularly salient finding was the algorithm's high predictive accuracy for delivery outcomes, notably in AIDA classes 0 and 4. In AIDA class 0, characterized by all geometric parameters being within the green zone, the consistent prediction of non-ICD outcomes suggests its potential utility in identifying cases where intervention may be safely deferred. Conversely, the accurate prediction of ICD in AIDA class 4 cases could expedite decision-making for cesarean delivery, potentially optimizing maternal and fetal out-comes by mitigating the duration of prolonged, unproductive labor. The final methodological step entails employing the most effective machine learning algorithms for predicting delivery outcomes based on the four geometric parameters' values with consideration of the relevant AIDA class. This approach enables clinicians to evaluate a pre-diction's clinical reliability.

The remarkable predictive accuracy, particularly at the extremes of the AIDA classification spectrum, underscores the potential of this algorithmic approach to enhance clinical decision-making in labor management.

Potential Advantages of the AIDA in Dystocic Labor Management Dystocic labor, also known as dystocia, broadly defined as difficult or obstructed labor, encompasses a range of conditions that impede the normal progression of labor and delivery as forms of difficult labor characterized by abnormally slow progress.

This condition can arise due to inefficient uterine contractions, abnormal fetal presentation, or other complications that impede the normal process of childbirth. Dystocia can lead to obstructed labor, where despite strong uterine contractions, the fetus cannot descend through the birth canal due to an insurmountable barrier, often occurring at the pelvic brim.

Several types of dystocia have been recognized in obstetrics, each with characteristics and management implications, and the classification may vary slightly depending on the medical literature or clinical approach: geometric dystocia, mechanical dystocia, dynamic dystocia, fetal dystocia, uterine dystocia, functional dystocia, soft tissue dystocia, compound presentations, maternal exhaustion dystocia, and labor dystocia.

Through its simultaneous measurement of four geometric parameters-the angle of progression, asynclitism degree, head-symphysis distance, and midline angle-the AIDA offers a comprehensive view of the spatial relationships between the fetus and the maternal pelvis, provides an objective assessment of the fetal position, and holds potential as a comprehensive tool for evaluating, directly or indirectly, various types of dystocia documented in the medical literature.

This approach provides a more detailed and accurate picture of the progress of labor or labor obstruction that might be missed by traditional assessment methods. By quantifying parameters like the degree of asynclitism, the AIDA may enable earlier detection of fetal malpositions that could lead to dystocia.

The AIDA classification system offers a nuanced approach to risk assessment, helping clinicians tailor their management strategies.