A deep learning approach to identify the fetal head position using transperineal ultrasound during labor

Ramirez Zegarra, Ruben; Conversano, Francesco; Dall'Asta, Andrea; Giovanna Di Trani, Maria; Fieni, Stefania; Morello, Rocco; Melito, Chiara; Pisani, Paola; Iurlaro, Enrico; Tondo, Marta; Gabriel Iliescu, Dominic; Nagy, Rodica; Vaso, Edvin; Abou-Dakn, Michael; Muslu, Gülhan; Lau, Wailam; Hung, Catherine; Sirico, Angelo; Lanzone, Antonio; Rizzo, Giuseppe; Mappa, Ilenia; Lees, Christoph; Usman, Sana; Winkler, Alice; Braun, Christian; Levy, Roni; Vaisbuch, Edi; Hassan, Wassim A.; Taylor, Sasha; Vimercati, Antonella; Mazzeo, Allegra; Moe Eggebø, Torbjørn; Amo Wiafe, Yaw; Ghi, Tullio; Casciaro, Sergio

doi:10.1016/j.ejogrb.2024.08.012

European Journal of Obstetrics & Gynecology and Reproductiv

2024

DOI: 10.1016/j.ejogrb.2024.08.012

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A deep learning approach to identify the fetal head position using transperineal ultrasound during labor

Ruben Ramirez Zegarra,

Francesco Conversano,

Andrea Dall'Asta

et al.

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Cited by 2 publications

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PSFHS challenge report: Pubic symphysis and fetal head segmentation from intrapartum ultrasound images

Bai,

Zhou,

et al. 2025

Medical Image Analysis

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PSFHS challenge report: Pubic symphysis and fetal head segmentation from intrapartum ultrasound images

Bai,

Zhou,

et al. 2025

Medical Image Analysis

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Comparing machine learning techniques for neonatal mortality prediction: insights from a modeling competition

Sullivan,

Moreira,

McAdams

et al. 2024

Pediatr Res

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Background Predicting mortality risk in neonatal intensive care units (NICUs) is challenging due to complex, variable clinical and physiological data. Machine learning (ML) offers potential for more accurate risk stratification. Objective To compare the performance of various ML models in predicting NICU mortality using a team-based modeling competition. Methods We conducted a modeling competition with five neonatologist-led teams applying ML techniques—logistic regression, CatBoost, neural networks, random forest, and XGBoost—to a shared dataset from over 6,000 NICU admissions. The dataset included static demographic and clinical variables, alongside daily samples of heart rate and oxygen saturation. Each team developed models to predict mortality risk at baseline and within 7 days. Models were evaluated using the area under the receiver operator characteristic curve (AUC). Results were presented at a national meeting, where an audience poll ranked models before AUC results were revealed. Results The audience favored the most complex model (CNN) for real-world application, though logistic regression achieved the highest AUC on test data. Teams employed varied feature selection, tuning, and evaluation strategies. Conclusions Logistic regression outperformed more complex models, highlighting the importance of selecting modeling methods based on data characteristics, interpretability, and expertise rather than model complexity alone. Impact By demonstrating that model complexity does not necessarily equate to better predictive performance, this research encourages the careful selection of modeling approaches.

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A deep learning approach to identify the fetal head position using transperineal ultrasound during labor

Cited by 2 publications

References 30 publications

PSFHS challenge report: Pubic symphysis and fetal head segmentation from intrapartum ultrasound images

PSFHS challenge report: Pubic symphysis and fetal head segmentation from intrapartum ultrasound images

Comparing machine learning techniques for neonatal mortality prediction: insights from a modeling competition

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