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Cited by 32Artificial Intelligence in Healthcare and EducationMaternal and Perinatal Health InterventionsArtificial Intelligence in Healthcare

Novel artificial intelligence approach for automatic differentiation of fetal occiput anterior and non-occiput anterior positions during labor

Ghi, T., Conversano, F., Ramirez Zegarra, R., Pisani, P., Dall'Asta, A., Lanzone, A., Lau, W., Vimercati, A. +5 more·2022·Ultrasound in Obstetrics & Gynecology
DOI 10.1002/uog.23739
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Abstract

ABSTRACT Objectives To describe a newly developed machine‐learning (ML) algorithm for the automatic recognition of fetal head position using transperineal ultrasound (TPU) during the second stage of labor and to describe its performance in differentiating between occiput anterior (OA) and non‐OA positions. Methods This was a prospective cohort study including singleton term (> 37 weeks of gestation) pregnancies in the second stage of labor, with a non‐anomalous fetus in cephalic presentation. Transabdominal ultrasound was performed to determine whether the fetal head position was OA or non‐OA. For each case, one sonographic image of the fetal head was then acquired in an axial plane using TPU and saved for later offline analysis. Using the transabdominal sonographic diagnosis as the gold standard, a ML algorithm based on a pattern‐recognition feed‐forward neural network was trained on the TPU images to discriminate between OA and non‐OA positions. In the training phase, the model tuned its parameters to approximate the training data (i.e. the training dataset) such that it would identify correctly the fetal head position, by exploiting geometric, morphological and intensity‐based features of the images. In the testing phase, the algorithm was blinded to the occiput position as determined by transabdominal ultrasound. Using the test dataset, the ability of the ML algorithm to differentiate OA from non‐OA fetal positions was assessed in terms of diagnostic accuracy. The F1‐score and precision‐recall area under the curve (PR‐AUC) were calculated to assess the algorithm's performance. Cohen's kappa (κ) was calculated to evaluate the agreement between the algorithm and the gold standard. Results Over a period of 24 months (February 2018 to January 2020), at 15 maternity hospitals affiliated to the International Study group on Labor ANd Delivery Sonography (ISLANDS), we enrolled into the study 1219 women in the second stage of labor. On the basis of transabdominal ultrasound, they were classified as OA (n = 801 (65.7%)) or non‐OA (n = 418 (34.3%)). From the entire cohort (OA and non‐OA), approximately 70% (n = 824) of the patients were assigned randomly to the training dataset and the rest (n = 395) were used as the test dataset. The ML‐based algorithm correctly classified the fetal occiput position in 90.4% (357/395) of the test dataset, including 224/246 with OA (91.1%) and 133/149 with non‐OA (89.3%) fetal head position. Evaluation of the algorithm's performance gave an F1‐score of 88.7% and a PR‐AUC of 85.4%. The algorithm showed a balanced performance in the recognition of both OA and non‐OA positions. The robustness of the algorithm was confirmed by high agreement with the gold standard (κ = 0.81; P < 0.0001). Conclusions This newly developed ML‐based algorithm for the automatic assessment of fetal head position using TPU can differentiate accurately, in most cases, between OA and non‐OA positions in the second stage of labor. This algorithm has the potential to support not only obstetricians but also midwives and accoucheurs in the clinical use of TPU to determine fetal occiput position in the labor ward. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

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Authors
Ghi, T., Conversano, F., Ramirez Zegarra, R., Pisani, P., Dall'Asta, A., Lanzone, A., Lau, W., Vimercati, A., Iliescu, D. G., Mappa, I., Rizzo, G., Casciaro, S., & the International Study group on Labor ANd Delivery Sonography (ISLANDS)
Affiliations
  • University of Parma (IT)
  • National Research Council (IT)
  • Department of Medicine and Surgery, Obstetrics and Gynecology Unit University of Parma Parma Italy
  • Università Cattolica del Sacro Cuore (IT)
  • Kwong Wah Hospital (CN)
  • Azienda Universitaria Ospedaliera Consorziale - Policlinico Bari (IT)
  • University of Medicine and Pharmacy of Craiova (RO)
  • University of Rome Tor Vergata (IT)
Corresponding author
Ghi, T.
ORCID
Venue
Ultrasound in Obstetrics & Gynecology
Publisher
Wiley
Published
2022-01
Volume
59
Issue
1
Pages
93-99
ISSN
  • 0960-7692 (ISSN-L)
  • 1469-0705
DOI
10.1002/uog.23739
OpenAlex
W3183526564
Cited by
32
References
46
Citations by year
2026: 2 · 2025: 8 · 2024: 13 · 2023: 5 · 2022: 4
FWCI
1.59
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 4 d)
Locations
  • Ultrasound in Obstetrics and Gynecology (publishedVersion)
  • PubMed (publishedVersion)
  • Cineca Institutional Research Information System (Tor Vergata University) (submittedVersion)
  • Archivio Istituzionale della Ricerca - Università degli Studi della Campania "Luigi Vanvitelli" (submittedVersion)
  • IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
  • CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) (submittedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Obstetrics and Gynecology › Maternal and Perinatal Health Interventions
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
fetal head position, second stage of labor, transperineal ultrasound, machine learning, feedforward neural networks, automated image classification, cephalic presentation, term singleton pregnancy, diagnostic accuracy
MeSH terms
Adult, Artificial Intelligence, Female, Fetus, Head, Humans, Obstetric Labor Complications, Labor Presentation, Labor Stage, Second, Pregnancy, Prospective Studies, Ultrasonography, Prenatal, Area Under Curve

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