A deep learning approach to identify the fetal head position using transperineal ultrasound during labor
Year: 2024
Type: article
Abstract: Objectives To develop a deep learning (DL)-model using convolutional neural networks (CNN) to automatically identify the fetal head position at transperineal ultrasound in the second stage of labor. Material and methods Prospective, multicenter study including singleton, term, cephalic pregnancies in the second stage of labor. We assessed the fetal head position using transabdominal ultrasound and subsequently, obtained an image of the fetal head on the axial plane using transperineal ultrasound and labeled it according to the transabdominal ultrasound findings. The ultrasound images were randomly allocated into the three datasets containing a similar proportion of images of each subtype of fetal head position (occiput anterior, posterior, right and left transverse): the training dataset included 70 %, the validation dataset 15 %, and the testing dataset 15 % of the acquired images. The pre-trained ResNet18 model was employed as a foundational framework for feature extraction and classification. CNN 1 was trained to differentiate between occiput anterior (OA) and non-OA positions, CNN 2 classified fetal head malpositions into occiput posterior (OP) or occiput transverse (OT) position, and CNN 3 classified the remaining images as right or left OT. The DL-model was constructed using three convolutional neural networks (CNN) working simultaneously for the classification of fetal head positions. The performance of the algorithm was evaluated in terms of accuracy, sensitivity, specificity, F1-score and Cohen's kappa. Results Between February 2018 and May 2023, 2154 transperineal images were included from eligible participants across 16 collaborating centers. The overall performance of the model for the classification of the fetal head position in the axial plane at transperineal ultrasound was excellent, with an of 94.5 % (95 % CI 92.0––97.0), a sensitivity of 95.6 % (95 % CI 96.8–100.0), a specificity of 91.2 % (95 % CI 87.3–95.1), a F1-score of 0.92 and a Cohen's kappa of 0.90. The best performance was achieved by the CNN 1 – OA position vs fetal head malpositions – with an accuracy of 98.3 % (95 % CI 96.9–99.7), followed by CNN 2 – OP vs OT positions – with an accuracy of 93.9 % (95 % CI 89.6–98.2), and finally, CNN 3 – right vs left OT position – with an accuracy of 91.3 % (95 % CI 83.5–99.1). Conclusions We have developed a DL-model capable of assessing fetal head position using transperineal ultrasound during the second stage of labor with an excellent overall accuracy. Future studies should validate our DL model using larger datasets and real-time patients before introducing it into routine clinical practice.
Authors: Ruben Ramirez Zegarra, Francesco Conversano, Andrea Dall'Asta, Maria Giovanna Di Trani, Stefania Fieni, Rocco Morello, Chiara Melito, Paola Pisani, Enrico Iurlaro, Marta Tondo, Dominic Gabriel Iliescu, Rodica Nagy, Edvin Vaso, Michael Abou-Dakn, Gülhan Muslu, Wailam Lau, Catherine Hung, Angelo Sirico, Antonio Lanzone, Giuseppe Rizzo, Ilenia Mappa, Christoph Lees, Sana Usman, Alice Winkler, Christian Braun, Roni Levy, Edi Vaisbuch, Wassim A. Hassan, Sasha Taylor, Antonella Vimercati, Allegra Mazzeo, Torbjørn Moe Eggebø, Yaw Amo Wiafe, Tullio Ghi, Sergio Casciaro
Institutions: University of Parma, National Research Council, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Medicine and Pharmacy of Craiova, St. Joseph-Krankenhaus, Kwong Wah Hospital, Agostino Gemelli University Polyclinic, Istituti di Ricovero e Cura a Carattere Scientifico, University of Rome Tor Vergata, Policlinico Tor Vergata, Queen Charlotte's and Chelsea Hospital, Luzerner Kantonsspital, Hebrew University of Jerusalem, Kaplan Medical Center, East Suffolk and North Essex NHS Foundation Trust, Colchester Hospital, Azienda Universitaria Ospedaliera Consorziale - Policlinico Bari, Policlinico Umberto I, Sapienza University of Rome, St Olav's University Hospital, Kwame Nkrumah University of Science and Technology
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,220
OpenAlex ID: W4401467542