AIMC Topic: Cephalopelvic Disproportion

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A machine learning model for prenatal risk prediction of cephalopelvic disproportion-related dystocia: A retrospective study.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics Apr 2, 2025
OBJECTIVE: To develop a prenatal risk prediction model for cephalopelvic disproportion (CPD)-related dystocia. This model aims to complement obstetricians' empirical judgments by identifying high-risk CPD-related dystocia cases within populations dee...
Humans Machine Learning Retrospective Studies Pregnancy Female Cesarean Section Risk Factors Adult Risk Assessment Delivery, Obstetric Cephalopelvic Disproportion Dystocia
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[Development of CT Pelvimetry Using Deep Learning Based Reconstruction].

Nihon Hoshasen Gijutsu Gakkai zasshi Jan 1, 2020
PURPOSE: X-ray pelvimetry is typically performed for the diagnosis of the cephalopelvic disproportion (CPD). The purpose of this study was to assess the utility of new computed tomography (CT) reconstruction "deep learning based reconstruction (DLR) ...
Humans Tomography, X-Ray Computed Pregnancy Female Deep Learning Japan Radiation Dosage Cephalopelvic Disproportion Pelvimetry
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