Deep learning model-assisted detection of kidney stones on computed tomography

Abstract

Introduction: The aim of this study was to investigate the success of a deep learning model in detecting kidney stones in different planes according to stone size on unenhanced computed tomography (CT) images. Materials and Methods: This retrospective study included 455 patients who underwent CT scanning for kidney stones between January 2016 and January 2020; of them, 405 were diagnosed with kidney stones and 50 were not. Patients with renal stones of 0-1 cm, 1-2 cm, and >2 cm in size were classified into groups 1, 2, and 3, respectively. Two radiologists reviewed 2,959 CT images of 455 patients in three planes. Subsequently, these CT images were evaluated using a deep learning model. The accuracy rate, sensitivity, specificity, and positive and negative predictive values of the deep learning model were determined. Results: The training group accuracy rates of the deep learning model were 98.2%, 99.1%, and 97.3% in the axial plane; 99.1%, 98.2%, and 97.3% in the coronal plane; and 98.2%, 98.2%, and 98.2% in the sagittal plane, respectively. The testing group accuracy rates of the deep learning model were 78%, 68% and 70% in the axial plane; 63%, 72%, and 64% in the coronal plane; and 85%, 89%, and 93% in the sagittal plane, respectively. Conclusions: The use of deep learning algorithms for the detection of kidney stones is reliable and effective. Additionally, these algorithms can reduce the reporting time and cost of CT-dependent urolithiasis detection, leading to early diagnosis and management.

Efficient and Accurate Computed Tomography–Based Stone Volume Determination: Development of an Automated Artificial Intelligence Algorithm

“…9,[18][19][20] As such, there is a growing appeal to the application of automated CT-based stone volume determination algorithms. 18,[21][22][23][24][25][26][27] To this end, AI offers clinical utility, as it precludes the need for manual measurements while increasing the accuracy of the determination by eliminating the high interobserver variability seen with linear measurements and the inaccuracies of employing the ellipsoid formula. 9 Prior studies investigated the utility of AI in urolithiasis diagnostics, but they primarily focused on detecting the presence of stones rather than assessing stone volume.…”

“…9 Prior studies investigated the utility of AI in urolithiasis diagnostics, but they primarily focused on detecting the presence of stones rather than assessing stone volume. 21,22,24,25,27 While emergency room physicians only need the former, urologists require the latter as determination of both presence of the stone and its volume are essential for management. Only a few studies have trained AI to focus on the more complex matter of volumetric stone burden assessment.…”

“…9,18-20 As such, there is a growing appeal to the application of automated CT-based stone volume determination algorithms. 18,21-27 To this end, AI offers clinical utility, as it precludes the need for manual measurements while increasing the accuracy of the determination by eliminating the high interobserver variability seen with linear measurements and the inaccuracies of employing the ellipsoid formula. 9…”

Theranostic roles of machine learning in clinical management of kidney stone disease

“…Both deep learning techniques (VGG16, Inceptionv3 and Resnet50) and Visual Transformer variants (EANet, CCT and Swin transformer algorithms) can be applied to differentiate KSD from renal cysts and tumors with 99.30 % accuracy achieved by Swin transformer-based model [50] . Caglayan et al [51] have examined the efficacy of a deep learning model for identifying kidney stones in unenhanced CT images in various planes based on stone size. The sagittal plane has provided the best sensitivity and specificity as compared with other planes [51] .…”

“…Caglayan et al [51] have examined the efficacy of a deep learning model for identifying kidney stones in unenhanced CT images in various planes based on stone size. The sagittal plane has provided the best sensitivity and specificity as compared with other planes [51] . Längkvist et al [52] have created a computer-aided detection (CAD) algorithm that can detect a ureteral stone in a CT scan.…”