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

Frontiers in Pediatrics

article

Lauren C. Smail¹  Kiret Dhindsa³  Luis H. Braga⁶  Suzanna Becker¹  Ranil R. Sonnadara¹ 

[1] Department of Psychology, McMaster University;Office of Education Science, McMaster University;Department of Surgery, McMaster University;Research and High Performance Computing, McMaster University;Vector Institute for Artificial Intelligence;Division of Urology, Department of Surgery, McMaster University;Division of Urology, Department of Surgery, McMaster Children's Hospital;McMaster Pediatric Surgery Research Collaborative, McMaster University;Centre for Advanced Research in Experimental and Applied Linguistics, McMaster University

关键词: hydronephrosis;    machine learning;    deep learning;    ultrasound;    diagnostic imaging;    grading;    diagnostic aid;    teaching aid;   

DOI  :  10.3389/fped.2020.00001

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Grading hydronephrosis severity relies on subjective interpretation of renal ultrasound images. Deep learning is a data-driven algorithmic approach to classifying data, including images, presenting a promising option for grading hydronephrosis. The current study explored the potential of deep 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 2,420 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 ( M age = 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. Fifty-one percent 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. Further investigation into a deep learning-based clinical adjunct for hydronephrosis is warranted.

【 授权许可】

CC BY