RESUMEN
Asynclitism, a misalignment of the fetal head with respect to the plane of passage through the birth canal, represents a significant obstetric challenge. High degrees of asynclitism are associated with labor dystocia, difficult operative delivery, and cesarean delivery. Despite its clinical relevance, the diagnosis of asynclitism and its influence on the outcome of labor remain matters of debate. This study analyzes the role of the degree of asynclitism (AD) in assessing labor progress and predicting labor outcome, focusing on its ability to predict intrapartum cesarean delivery (ICD) versus non-cesarean delivery. The study also aims to assess the performance of the AIDA (Artificial Intelligence Dystocia Algorithm) algorithm in integrating AD with other ultrasound parameters for predicting labor outcome. This retrospective study involved 135 full-term nulliparous patients with singleton fetuses in cephalic presentation undergoing neuraxial analgesia. Data were collected at three Italian hospitals between January 2014 and December 2020. In addition to routine digital vaginal examination, all patients underwent intrapartum ultrasound (IU) during protracted second stage of labor (greater than three hours). Four geometric parameters were measured using standard 3.5 MHz transabdominal ultrasound probes: head-to-symphysis distance (HSD), degree of asynclitism (AD), angle of progression (AoP), and midline angle (MLA). The AIDA algorithm, a machine learning-based decision support system, was used to classify patients into five classes (from 0 to 4) based on the values of the four geometric parameters and to predict labor outcome (ICD or non-ICD). Six machine learning algorithms were used: MLP (multi-layer perceptron), RF (random forest), SVM (support vector machine), XGBoost, LR (logistic regression), and DT (decision tree). Pearson's correlation was used to investigate the relationship between AD and the other parameters. A degree of asynclitism greater than 70 mm was found to be significantly associated with an increased rate of cesarean deliveries. Pearson's correlation analysis showed a weak to very weak correlation between AD and AoP (PC = 0.36, p < 0.001), AD and HSD (PC = 0.18, p < 0.05), and AD and MLA (PC = 0.14). The AIDA algorithm demonstrated high accuracy in predicting labor outcome, particularly for AIDA classes 0 and 4, with 100% agreement with physician-practiced labor outcome in two cases (RF and SVM algorithms) and slightly lower agreement with MLP. For AIDA class 3, the RF algorithm performed best, with an accuracy of 92%. AD, in combination with HSD, MLA, and AoP, plays a significant role in predicting labor dystocia and labor outcome. The AIDA algorithm, based on these four geometric parameters, has proven to be a promising decision support tool for predicting labor outcome and may help reduce the need for unnecessary cesarean deliveries, while improving maternal-fetal outcomes. Future studies with larger cohorts are needed to further validate these findings and refine the cut-off thresholds for AD and other parameters in the AIDA algorithm.
RESUMEN
The position of the fetal head during engagement and progression in the birth canal is the primary cause of dystocic labor and arrest of progression, often due to malposition and malrotation. The authors performed an investigation on pregnant women in labor, who all underwent vaginal digital examination by obstetricians and midwives as well as intrapartum ultrasonography to collect four "geometric parameters", measured in all the women. All parameters were measured using 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.
RESUMEN
Malacoplakia is an uncommon disease characterized by chronic and granulomatous inflammation, which rarely involves the female genital tract. We describe the ecographic and histological evolution of the first case of a patient developing endometrial malacoplakia as a complication after a cesarean section. The patient, a 43-year-old woman, presented with pelvic pain one month after delivering by cesarean section and the initial suspicion was of retention of placental rests. We discuss the diagnostic challenges for this rare disease, highlighting the importance of considering endometrial malacoplakia as a possible diagnosis in patients with similar clinical presentations and the important role of 2D and 3D ultrasound in the diagnostic pathway. In literature, ultrasound findings in cases of endometrial malacoplakia are represented by hypoechoic thickening of the endometrial lining; hyperechoic thickening of the myometrium, and the presence of masses, nodules, cystic areas, or anechoic fluid within the endometrium. For the first time, we describe the evolution of endometrial malacoplakia through both ultrasound, 2D and 3D, and histopathological findings, from the acute to chronic stage of the disease.
RESUMEN
Background: Hysteroscopy currently represents the gold standard for the diagnosis and treatment of intrauterine pathologies. Recent technological progress has enabled the integration of diagnostic and operative time, leading to the "see and treat" approach. Diode laser technology is emerging as one of the most innovative and intriguing techniques in this context. Methods: A comprehensive search of the literature was carried out on the main databases. Only original studies reporting the treatment of intrauterine pathologies using diode laser were deemed eligible for inclusion in this systematic review (PROSPERO ID: CRD42023485452). Results: Eight studies were included in the qualitative analysis for a total of 474 patients undergoing laser hysteroscopic surgery. Eighty-three patients had female genital tract abnormalities, 63 had submucosal leiomyomas, 327 had endometrial polyps, and one patient had a scar pregnancy. Except for leiomyomas, whose technique already included two surgical times at the beginning, only seven patients required a second surgical step. Cumulative rates of intraoperative and postoperative complications of 2.7% and 0.6%, respectively, were reported. Conclusions: Diode laser through "see and treat" hysteroscopy appears to be a safe and effective method. However, additional studies with larger sample sizes and improved designs are needed to consolidate the evidence currently available in the literature.