ArticleJournal of imaging2024
Artificial Intelligence, Intrapartum Ultrasound and Dystocic Delivery: AIDA (Artificial Intelligence Dystocia Algorithm), a Promising Helping Decision Support System.
Article in Journal of imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06664112 (Study of Delivery Outcomes After AIDA), which is not on this map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Study of Delivery Outcomes After AIDA (Artificial Intelligence Dystocia Algorithm) Analysis
Who cites it
6 citing papers in PubMed.
- Artificial Intelligence Dystocia Algorithm (AIDA) for Risk Stratification of Occiput Posterior Fetal Head Position.Journal of imaging · 2026Article
- From Retrospective to Prospective: The Contribution of Artificial Intelligence Dystocia Algorithm in Understanding and Preventing Dystocia in Intrapartum Cesarean Delivery.Maternal-fetal medicine (Wolters Kluwer Health, Inc.) · 2026Article
- The Contribution of AIDA (Artificial Intelligence Dystocia Algorithm) to Cesarean Section Within Robson Classification Group.Journal of imaging · 2025Article
- Artificial Intelligence Dystocia Algorithm (AIDA) as a Decision Support System in Transverse Fetal Head Position.Journal of imaging · 2025Article
- Dystocia, Delivery, and Artificial Intelligence in Labor Management: Perspectives and Future Directions.Journal of clinical medicine · 2024Article
- AIDA (Artificial Intelligence Dystocia Algorithm) in Prolonged Dystocic Labor: Focus on Asynclitism Degree.Journal of imaging · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.