Artificial Intelligence Dystocia Algorithm (AIDA) as a Decision Support System in Transverse Fetal Head Position
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 capabilitie...
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2025-07-01
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| author | Antonio Malvasi Lorenzo E. Malgieri Tommaso Difonzo Reuven Achiron Andrea Tinelli Giorgio Maria Baldini Lorenzo Vasciaveo Renata Beck Ilenia Mappa Giuseppe Rizzo |
| author_facet | Antonio Malvasi Lorenzo E. Malgieri Tommaso Difonzo Reuven Achiron Andrea Tinelli Giorgio Maria Baldini Lorenzo Vasciaveo Renata Beck Ilenia Mappa Giuseppe Rizzo |
| author_sort | Antonio Malvasi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-933229d9aca045fbb044283bdc57f486 |
| institution | DOAJ |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-07-01 |
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| series | Journal of Imaging |
| spelling | doaj-art-933229d9aca045fbb044283bdc57f4862025-08-20T02:45:39ZengMDPI AGJournal of Imaging2313-433X2025-07-0111722310.3390/jimaging11070223Artificial Intelligence Dystocia Algorithm (AIDA) as a Decision Support System in Transverse Fetal Head PositionAntonio Malvasi0Lorenzo E. Malgieri1Tommaso Difonzo2Reuven Achiron3Andrea Tinelli4Giorgio Maria Baldini5Lorenzo Vasciaveo6Renata Beck7Ilenia Mappa8Giuseppe Rizzo9Unit of Obstetrics and Gynecology, Department of Interdisciplinary Medicine (DIM), University of Bari “Aldo Moro”, Policlinic of Bari, Piazza Giulio Cesare 11, 70124 Bari, ItalyThe New European Surgical Academy (NESA), 10117 Berlin, GermanyUnit of Obstetrics and Gynecology, Department of Interdisciplinary Medicine (DIM), University of Bari “Aldo Moro”, Policlinic of Bari, Piazza Giulio Cesare 11, 70124 Bari, ItalySackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 6997801, IsraelDepartment of Obstetrics and Gynecology and CERICSAL (CEntro di RIcerca Clinico SALentino), Veris delli Ponti Hospital Scorrano, 73020 Lecce, ItalyUnit of Obstetrics and Gynecology, Department of Interdisciplinary Medicine (DIM), University of Bari “Aldo Moro”, Policlinic of Bari, Piazza Giulio Cesare 11, 70124 Bari, ItalyDepartment of Obstetrics and Gynecology, Center of Maternal Fetal Medicine, Universitary Hospital, University of Foggia, 71122 Foggia, ItalyAnesthesia and Intensive Care Unit, Department of Medical and Surgical Sciences, Policlinico Riuniti Foggia, University of Foggia, 71122 Foggia, ItalyDepartment of Maternal and Child Health and Urological Sciences, University of Roma Sapienza, 00185 Rome, ItalyDepartment of Maternal and Child Health and Urological Sciences, University of Roma Sapienza, 00185 Rome, ItalyTransverse 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.https://www.mdpi.com/2313-433X/11/7/223Artificial Intelligence Dystocia Algorithm (AIDA)transverse fetal head positionmachine learningintrapartum ultrasoundlabor dystociadecision support system |
| spellingShingle | Antonio Malvasi Lorenzo E. Malgieri Tommaso Difonzo Reuven Achiron Andrea Tinelli Giorgio Maria Baldini Lorenzo Vasciaveo Renata Beck Ilenia Mappa Giuseppe Rizzo Artificial Intelligence Dystocia Algorithm (AIDA) as a Decision Support System in Transverse Fetal Head Position Journal of Imaging Artificial Intelligence Dystocia Algorithm (AIDA) transverse fetal head position machine learning intrapartum ultrasound labor dystocia decision support system |
| title | Artificial Intelligence Dystocia Algorithm (AIDA) as a Decision Support System in Transverse Fetal Head Position |
| title_full | Artificial Intelligence Dystocia Algorithm (AIDA) as a Decision Support System in Transverse Fetal Head Position |
| title_fullStr | Artificial Intelligence Dystocia Algorithm (AIDA) as a Decision Support System in Transverse Fetal Head Position |
| title_full_unstemmed | Artificial Intelligence Dystocia Algorithm (AIDA) as a Decision Support System in Transverse Fetal Head Position |
| title_short | Artificial Intelligence Dystocia Algorithm (AIDA) as a Decision Support System in Transverse Fetal Head Position |
| title_sort | artificial intelligence dystocia algorithm aida as a decision support system in transverse fetal head position |
| topic | Artificial Intelligence Dystocia Algorithm (AIDA) transverse fetal head position machine learning intrapartum ultrasound labor dystocia decision support system |
| url | https://www.mdpi.com/2313-433X/11/7/223 |
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