Using Deep Learning Algorithms to Grade Hydronephrosis Severity: Toward a Clinical Adjunct

Grading hydronephrosis severity relies on subjective interpretation of renal ultrasound images. Deep learning algorithms classify images into categories using data-driven methods, thus presenting a promising option for grading hydronephrosis. The current study explored the potential of convolutional neural networks (CNN), a type of deep learning algorithm, to grade hydronephrosis ultrasound images according to the 5-point Society for Fetal Urology (SFU) classification system, and discusses its potential applications in developing decision and teaching aids for clinical practice. We developed a five-layer CNN to grade 2420 sagittal hydronephrosis ultrasound images (191 SFU 0 8%, 407 SFU I 17%, 666 SFU II 28%, 833 SFU III 34%, and 323 SFU IV 13%), from 673 patients ranging from 0 to 116.29 months old (Mage=16.53, SD=17.80). Five-way (all grades) and two-way classification problems (i.e. II vs. III, and low 0-II vs. high III-IV) were explored. The CNN classified 94% (95% CI, 93%-95%) of the images correctly or within one grade of the provided label in the five-way classification problem. 51% of these images (95% CI, 49%-53%) were correctly predicted, with an average weighted F1 score of 0.49 (95% CI, 0.47-0.51). The CNN achieved an average accuracy of 78% (95% CI, 75%-82%) with an average weighted F1 of 0.78 (95% CI, 0.74-0.82) when classifying low vs. high grades, and an average accuracy of 71% (95% CI, 68%-74%) with an average weighted F1 score of 0.71 (95% CI, 0.68-0.75) when discriminating between grades II vs. III. Our model performs well above chance level, and classifies almost all images either correctly or within one grade of the provided label. We have demonstrated the applicability of a CNN approach to hydronephrosis ultrasound image classification, and further investigation into a deep learning-based clinical adjunct for hydronephrosis is warranted.