Renal abnormalities are important findings in pediatric medicine. It is well accepted that “silent” renal abnormalities can be effectively detected through ultrasound (US) screening, which makes both early diagnoses and intervention possible [1,2]. US is a safe, relatively cheap, and convenient medical modality. Portable ultrasonic probes and internet connections have largely developed in recent years, even extending the coverage of pediatric renal US screening throughout the world. However, current methods remain limited due to the lack of automated processes that accurately classify diseased and normal kidneys [3].

Common renal abnormalities identified in US images in a series of more than 1 million school children included hydronephrosis (39.6%), unilateral small kidney (19.8%), unilateral agenesis (15.9%), cystic disease (13.9%), abnormal shapes—ectopic, horseshoe, and duplication of kidney (8%)—as well as others, that is, stones, tumors, and parenchymal diseases (1.5%) [1].

Thus far, publications regarding computer-aided US image interpretation have been much fewer than those based on computerized tomography or magnetic resonance imaging [4,5]. The use of US presents unique challenges, such as different angles of image sampling, low image quality caused by noise and artifacts, high dependence on an abundance of operators, and high inter- and intra-observer variability across different institutes and manufacturers’ US systems [6]. From the review about medical US published in 2021 [7], there were only 3 studies involving deep learning for renal US image classification [5,8,9].

This study was performed to select normal pediatric renal US images, as well as different types of renal abnormalities previously mentioned, for purposes of machine learning. Through the pretreatment of original images, adequate grouping of images, and deep neural network training, we hope that renal images can be correctly classified as either normal or abnormal. The aim of this study is to establish an artificial intelligence (AI) model for screening renal abnormalities to enhance the well-being of children even in areas where there is no pediatric nephrologist.

After 30 epochs for these 1599 pediatric renal US images, we obtained satisfactory results. The performance metrics in the test part of the data set are shown in Table 2. The accuracy in different abnormalities ranged from 95% to 100%.

The accuracies of each abnormality ranged from 95.2% to 100%, with an overall accuracy as 98.4%. The area under curves (AUCs) were from 0.935 to 0.998. The AUC for overall performance was 0.961. There was no difference between these 10 random tests (P>.05). We repeated the 10 experiments using different randomizations involving 80%/20% training/test images to check the consistency of the machine learning performance. The accuracies ranged from 95.2% to 98.4%. There was no difference between these 10 tests (P>.05). We performed a 5-fold cross test, and the results are shown in Table 3.

We validated the 94 MB model through machine learning with another 327 pediatric renal US images. The classifications included 66 (20.2%) normal, 37 (11.3%) hydronephrosis, 53 (16.2%) cyst, 95 (29.1%) stone, 53 (16.2%) hyperechogenicity, and 26 (7.9%) space-occupying US images. The performances based on each single image are summarized in Table 4. Accuracy in the different abnormalities ranged from 89.9% to 94.1%, with an average of 92.3%. AUC was from 0.934 to 0.996 (Figure 3). The overall performance in AUC was 0.959. The macro F₁ was 0.924.

The main finding of this study is a useful AI model for screening abnormal pediatric renal US images. The average accuracy can be 92.9%. The results can fulfill the main purpose of this study—to develop a useful computer-aided diagnosis model for screening various pediatric renal US abnormal patterns automatically. In this study, the machine learning methods were based upon convolutional neural network and fine-tuning, along with our unique methods for image preprocessing, as well as strategies for classification, which achieved a feasible model for clinical purposes. We constructed the stable classifier that combined both the transfer learning and training from scratch, balancing the training of a medical data set taken from an adequate sample size.

Clinical applications of AI in nephrology are versatile, but the use of renal US in this field is still in its infancy [13,14]. The reports derived from renal US images alone have been relatively limited up until now, with the major reports involving acute and chronic injuries [15-17]. Most renal image studies for AI used magnetic resonance imaging, computerized tomography, and patient histology for tumors, stones, nephropathy, transplantation, and other conditions [18-21]. The key challenges associated with deep learning involving US include reliability, generalizability, and bias [22]. The basic studies for enhancing AI performance in renal US have begun and remain undergoing [23-25].

There have been 4 reports from studies involving clinical AI applications in pediatric renal US abnormalities [3, 5,8,9]. Zheng et al [3] found that the deep transfer learning method offers satisfactory accuracy in identifying congenital anomalies in the kidney and urinary tract, even when the data set is as small as only having 50 children with congenital anomalies in the kidney and urinary tract and 50 children as the control. Yin et al [5] performed a similar study to detect posterior urethral valves. Sudarharson et al [8] used 3 variant data sets for identifying renal cysts, stones, and tumors, with an accuracy rate of 96.54% in images of quality and 95.58% in images of noise. Smail et al [9] attempted to use AI for grading hydronephrosis involving the 5-point scoring system from the Society of Fetal Urology (SFU). The best recorded performance was a 78% accuracy rate by dividing hydronephrosis into mild and severe. However, the accuracy rate was only 51% when using the 5-point system. In our study, we established a single 94 MB model to classify normal versus abnormal pediatric renal US images. The items seen in the abnormalities included renal cysts, stones, and tumors, as reported by Sudarharson et al [8]. In addition, the model was able to identify images of hydronephrosis and hyperechogenicity. Comparing the results from the study performed by Smail et al [9], our results showed a better classification accuracy for hydronephrosis. The 37 validated images were moderate or severe hydronephrosis, that is, the SFU class II, III, and IV. Our model can achieve 100% sensitivity, comparing the sensitivity of 46%-54%, as previously reported [26].

In terms of SFU class I, our model had an accuracy of 71.7% (119/166). Up until now, grading of hydronephrosis has been an ongoing challenge [27]. Extremely early intervention for treatment of mild hydronephrosis remains inadequate. If a child with mild hydronephrosis is also experiencing other renal abnormalities, such as stones, cysts, or hyperechogenicity, it is highly possible our model would be capable of providing any alarming information surrounding these conditions.

The unique pretreatment of images for machine learning performed in this study was performed to provide a comparison of liver echogenicity in the simultaneous study. This step is necessary for identifying hyperechogenicity. Other abnormalities, such as hydronephrosis, cysts, stones, and tumors, showed no difference in classification, regardless of whether we inputted the images with the addition of the square containing liver echogenicity and the gray scale gradient in the left part of the image shown in Figure 1. As demonstrated in Table 4, the accuracy and sensitivity for hyperechogenicity identification was lower than it was with other abnormalities. Increased echogenicity is an important finding in evaluating muscle, thyroid, vascular, and renal diseases [28]. The gray scale US presents a general sensitivity rate of 62% to 77%, a specificity of 58% to 73%, and a positive predictive value of 92% for detecting microscopically confirmed renal parenchymal diseases. The above results reveal that the echogenicity change was not sensitive enough for detecting renal disease. Abnormalities in renal echogenicity include increased echogenicity, poor differentiation of the cortex or medulla, and inversed echogenicity of the renal cortex and medulla [29]. In practice, it is quite often that we cannot obtain a square containing homogenous liver echogenicity for purposes of machine learning. When the classification is compared by a pediatric nephrologist, the results are acceptable. It is also difficult for the naked eye to discriminate between the not-so-significant gray scale differences. Currently, the so called “radiomics” information, which can aid US imaging in AI, is emerging [30], with a more precise assessment of US pixels possibly enhancing the utility of hyperechogenicity.

A limitation of this study is the single medical center image source. More images from different hospitals, areas, ethnicities, and US companies need to be used. We conducted a small-scale external validation using US images from different companies, including General Electric, Siemens, and Toshiba. After image pretreatment, the results could be 100% sensitivity, 80% specificity, and 90% accuracy. Another limitation is the moderate image number of images contributing to the data set. We did not divide images from right or left kidney for training, though the results can be acceptable. We will further validate our method based on larger data sets.

In conclusion, this study proposed the use of an automatic model for purposes of screening various abnormalities in pediatric renal US images. We will continue to enhance the model’s performance as we conduct additional evaluation studies surrounding its future clinical applications, including being an auxiliary software for screening children’s renal abnormalities in remote areas.