Predicting Urinary Stone Composition in Single-Use Flexible Ureteroscopic Images with a Convolutional Neural Network.

Background and Objectives: Analysis of urine stone composition is one of the most important factors in urolithiasis treatment. This study investigated whether a convolutional neural network (CNN) can show decent results in predicting urinary stone composition even in single-use flexible ureterorenoscopic (fURS) images with relatively low resolution. Materials and Methods: This study retrospectively used surgical images from fURS lithotripsy performed by a single surgeon between January 2018 and December 2021. The ureterorenoscope was a single-use flexible ureteroscope (LithoVue, Boston Scientific). Among the images taken during surgery, a single image satisfying the inclusion and exclusion criteria was selected for each stone. Cases were divided into two groups according to whether they contained any calcium oxalate (the Calcium group) or none (the Non-calcium group). From 506 total cases, 207 stone surface images were finally included in the study. In the CNN model, the transfer learning method using Resnet-18 as a pre-trained model was used, and only endoscopic digital images and stone classification data were input to achieve minimally supervised learning. Results: There were 175 cases in the Calcium group and 32 in the Non-calcium group. After training and validation, the model was tested using the test set, and the total accuracy was 81.8%. Recall and precision of the test results were 88.2% and 88.2% in the Calcium group and 60.0% and 60.0% in the Non-calcium group, respectively. The area under the receiver operating characteristic curve of the model, which represents its classification performance, was 0.82. Conclusions: Single-use flexible ureteroscopes have financial benefits but low vision quality compared with reusable digital flexible ureteroscopes. As far as we know, this is the first artificial intelligence study using single-use fURS images. It is meaningful that the CNN performed well even under these difficult conditions because these results can further expand the possibilities of its use.

Comparison of prostate cancer detection rates of various prostate biopsy methods for patients with prostate-specific antigen levels of <10.0 ng/mL in real-world practice.

Purpose: Several strategies of prostate biopsy (PBx) have been introduced to improve prostate cancer (PCa) detection rates. However, studies comparing cancer detection rates (CDRs) according to biopsy methods in real-world practice are scarce. This study aimed to investigate CDRs according to the biopsy methods for patients with prostate-specific antigen (PSA) <10.0 ng/mL. Materials and Methods: From 2006 to 2015, patients who underwent PBx were initially selected. All patients were categorized according to the biopsy methods performed (magnetic resonance imaging targeted biopsy [MR-TBx], 12+2 hypoechoic lesion target biopsy, saturation biopsy [sPBx], extended biopsy, and 12-core PBx). The CDR of MR-TBx was compared to that of sPBx and other protocols. Volume per core (VPC) was defined as prostate volume divided by the number of biopsy cores. Patients previously diagnosed with PCa were excluded. Results: Of the 1,598 patients (median PSA, 5.41 ng/mL), 401 (25.1%) were diagnosed with PCa. Among the biopsy methods, MR-TBx has the highest CDR and proportion of Gleason score ≥7 (3+4). Biopsy methods, VPC, age, prostate volume, and PSA were associated with PCa detection. In the sub-analysis for initial biopsy, MR-TBx had no significant difference with sPBx, but had higher CDR than the other biopsy protocols. For repeat biopsy, VPC, rather than the biopsy method, was associated with CDR. Conclusions: This study reaffirmed the efficacy of MR-TBx on CDR in real-world practice. In cases with barriers to performing magnetic resonance imaging, VPC might be useful for adjusting the optimal number of biopsy cores in repeat biopsy.