Evidence map›Paper›PMID 39194983›Full record

ArticleJournal of imaging2024

AIDA (Artificial Intelligence Dystocia Algorithm) in Prolonged Dystocic Labor: Focus on Asynclitism Degree.

Antonio Malvasi, Lorenzo E Malgieri, Ettore Cicinelli, Antonella Vimercati, Reuven Achiron, Radmila Sparić, Antonio D'Amato, Giorgio Maria Baldini, Miriam Dellino, Giuseppe Trojano and 3 more

Registry-linked trialAbstract read
In one paragraph

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 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

NCT06664112 recruitingnot on this map

Study of Delivery Outcomes After AIDA (Artificial Intelligence Dystocia Algorithm) Analysis

Typeobservational_patient_registrySponsorCentro di Ricerca Clinica SalentinoRan2024 to 2026Enrolled1,000ConditionsDelivery Complication, Delivery Problem, Delivery, InjuryArmsAIDA Analysis
3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Antonio MalvasiDepartment of Interdisciplinary Medicine (DIM), Unit of Obstetrics and Gynecology, University of Bari "Aldo Moro", Policlinico of Bari, Piazza Giulio Cesare 11, 70124 Bari, Italy.
Lorenzo E MalgieriChief Innovation Officer in CLE, 70126 Bari, Italy.ORCID 0000-0003-0606-6425
Ettore CicinelliDepartment of Interdisciplinary Medicine (DIM), Unit of Obstetrics and Gynecology, University of Bari "Aldo Moro", Policlinico of Bari, Piazza Giulio Cesare 11, 70124 Bari, Italy.ORCID 0000-0003-2390-4582
Antonella VimercatiDepartment of Precision and Regenerative Medicine and Jonic Area, University of Bari "Aldo Moro", 70121 Bari, Italy.ORCID 0000-0002-9862-1619
Reuven AchironSackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 6997801, Israel.
Radmila SparićClinic for Gynecology and Obstetrics, University Clinical Centre of Serbia, 11000 Belgrade, Serbia.ORCID 0000-0003-0515-1951
Antonio D'AmatoDepartment of Interdisciplinary Medicine (DIM), Unit of Obstetrics and Gynecology, University of Bari "Aldo Moro", Policlinico of Bari, Piazza Giulio Cesare 11, 70124 Bari, Italy.ORCID 0000-0001-6286-761X
Giorgio Maria BaldiniDepartment of Interdisciplinary Medicine (DIM), Unit of Obstetrics and Gynecology, University of Bari "Aldo Moro", Policlinico of Bari, Piazza Giulio Cesare 11, 70124 Bari, Italy.ORCID 0000-0002-5061-8007
Miriam DellinoDepartment of Interdisciplinary Medicine (DIM), Unit of Obstetrics and Gynecology, University of Bari "Aldo Moro", Policlinico of Bari, Piazza Giulio Cesare 11, 70124 Bari, Italy.
Giuseppe TrojanoDepartment of Maternal and Child Gynecologic Oncology Unit, "Madonna delle Grazie" Hospital ASM, 75100 Matera, Italy.ORCID 0000-0002-8737-9943
Renata BeckDepartment of Medical and Surgical Sciences, Anesthesia and Intensive Care Unit, Policlinico Riuniti Foggia, University of Foggia, 71122 Foggia, Italy.ORCID 0000-0001-8352-7219
Tommaso DifonzoDepartment of Interdisciplinary Medicine (DIM), Unit of Obstetrics and Gynecology, University of Bari "Aldo Moro", Policlinico of Bari, Piazza Giulio Cesare 11, 70124 Bari, Italy.ORCID 0009-0004-9711-3497
Andrea TinelliDepartment of Obstetrics and Gynecology and CERICSAL (CEntro di RIcerca Clinico SALentino), Veris delli Ponti Hospital Scorrano, 73020 Lecce, Italy.ORCID 0000-0001-8426-8490

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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,

Indexed as

artificial intelligenceasynclitismcesarean sectiondystociaintrapartum ultrasoundlabormalpositionmalrotationvaginal operative delivery

Identifiers

PMID39194983
PMCPMC11355300

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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.