Fetal biometry and amniotic fluid volume assessment end-to-end automation using Deep Learning.

ABSTRACT: Fetal biometry and amniotic fluid volume assessments are two essential yet repetitive tasks in fetal ultrasound screening scans, aiding in the detection of potentially life-threatening conditions. However, these assessment methods can occasionally yield unreliable results. Advances in deep learning have opened up new avenues for automated measurements in fetal ultrasound, demonstrating human-level performance in various fetal ultrasound tasks. Nevertheless, the majority of these studies are retrospective in silico studies, with a limited number including African patients in their datasets. In this study we developed and prospectively assessed the performance of deep learning models for end-to-end automation of fetal biometry and amniotic fluid volume measurements. These models were trained

SUBMITTER: Slimani S 

PROVIDER: S-EPMC10624828 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature