Predicting kidney abnormalities in utero with deep learning OttawaHospital uOttawa kidney health deeplearning
By Dr. Liji Thomas, MDJul 17 2023Reviewed by Benedette Cuffari, M.Sc. In a recent study published in on the preprint server Research Square* while it is under review for publication in Scientific Reports, researchers examine the potential use of deep learning models to diagnose fetal kidney anomalies.
Introduction DL is a method of machine learning that uses artificial neural networks to rapidly and deeply analyze large datasets to identify key features that may contribute to a given outcome. The immense amounts of data processed using DL could provide new approaches that enhance or change the way healthcare is delivered.
Fetal ultrasound imaging is crucial for observing fetal anatomy and the presence of any malformations. Considering the high rate of congenital anomalies of the kidneys and urinary tract at one in 500 live births, the incorporation of DL into prenatal screening could allow clinicians to diagnose these conditions earlier to improve patient outcomes.
About 970 ultrasound images were used to train the current study, which consisted of about 650 controls and 320 images of anomalous kidney anatomy. More specifically, about 260 images exhibited unilateral dilation of the urinary tract , whereas 64 were images of unilateral multicystic dysplastic kidney .
DL was also used to discriminate normal and abnormal conditions in a two-class schema. The best model showed great promise in its generalizability to ultrasound imaging of CAKUT. The potential for misclassification also exists for various reasons. This emphasizes the need to advance the current understanding of DL models to improve their reliability in areas where wrong predictions could be disastrous for the patient.
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