Automatic Screening of Pediatric Renal Ultrasound Abnormalities: Deep Learning and Transfer Learning Approach

Abstract

Background In recent years, the progress and generalization surrounding portable ultrasonic probes has made ultrasound (US) a useful tool for physicians when making a diagnosis. With the advent of machine learning and deep learning, the development of a computer-aided diagnostic system for screening renal US abnormalities can assist general practitioners in the early detection of pediatric kidney diseases. Objective In this paper, we sought to evaluate the diagnostic performance of deep learning techniques to classify kidney images as normal and abnormal. Methods We chose 330 normal and 1269 abnormal pediatric renal US images for establishing a model for artificial intelligence. The abnormal images involved stones, cysts, hyperechogenicity, space-occupying lesions, and hydronephrosis. We performed preprocessing of the original images for subsequent deep learning. We redefined the final connecting layers for classification of the extracted features as abnormal or normal from the ResNet-50 pretrained model. The performances of the model were tested by a validation data set using area under the receiver operating characteristic curve, accuracy, specificity, and sensitivity. Results The deep learning model, 94 MB parameters in size, based on ResNet-50, was built for classifying normal and abnormal images. The accuracy, (%)/area under curve, of the validated images of stone, cyst, hyperechogenicity, space-occupying lesions, and hydronephrosis were 93.2/0.973, 91.6/0.940, 89.9/0.940, 91.3/0.934, and 94.1/0.996, respectively. The accuracy of normal image classification in the validation data set was 90.1%. Overall accuracy of (%)/area under curve was 92.9/0.959.. Conclusions We established a useful, computer-aided model for automatic classification of pediatric renal US images in terms of normal and abnormal categories.

Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

“…The final predictions were generated using the majority-vote technique to achieve a maximum accuracy of 95.58%. While considering stones, cysts, hyper-echogenicity, space-occupying lesions, and hydronephrosis as abnormalities, Tsai et al [48] collected 330 normal and 1269 abnormal pediatric renal images from a U.S. database.After performing the pre-processing tasks, the final linking layer of ResNet50 was redefined, and an accuracy of 92.9% was achieved. Though several studies have employed different DL approaches for better accuracy, they have primarily used DL models that consisted of more parameters, and they have not focused on improving the clarity and transparency of the result from DL models.…”

Pilot study of machine learning in the task of distinguishing high and low-grade pediatric hydronephrosis on ultrasound

“…There are various studies in the diagnosis of hydronephrosis using artificial intelligence (AI)/machine learning (ML). Both deep learning and conventional radiomics analysis have been employed in these studies to detect renal abnormalities and predict hydronephrosis grade and severity [ 11 12 13 14 15 16 17 ]. Cerrolaza et al [ 11 ] performed quantitative imaging analysis on US in predicting obstructive severity in children with hydronephrosis.…”