Predicting stone composition via machine-learning models trained on intra-operative endoscopic digital images.

OBJECTIVES: The aim of this study was to use deep learning (DL) of intraoperative images of urinary stones to predict the composition of urinary stones. In this way, the laser frequency and intensity can be adjusted in real time to reduce operation time and surgical trauma. MATERIALS AND METHODS: A total of 490 patients who underwent holmium laser surgery during the two-year period from March 2021 to March 2023 and had stone analysis results were collected by the stone laboratory. A total of 1658 intraoperative stone images were obtained. The eight stone categories with the highest number of stones were selected by sorting. Single component stones include calcium oxalate monohydrate (W1), calcium oxalate dihydrate (W2), magnesium ammonium phosphate hexahydrate, apatite carbonate (CH) and anhydrous uric acid (U). Mixed stones include W2 + U, W1 + W2 and W1 + CH. All stones have intraoperative videos. More than 20 intraoperative high-resolution images of the stones, including the surface and core of the stones, were available for each patient via FFmpeg command screenshots. The deep convolutional neural network (CNN) ResNet-101 (ResNet, Microsoft) was applied to each image as a multiclass classification model. RESULTS: The composition prediction rates for each component were as follows: calcium oxalate monohydrate 99% (n = 142), calcium oxalate dihydrate 100% (n = 29), apatite carbonate 100% (n = 131), anhydrous uric acid 98% (n = 57), W1 + W2 100% (n = 82), W1 + CH 100% ( n = 20) and W2 + U 100% (n = 24). The overall weighted recall of the cellular neural network component analysis for the entire cohort was 99%. CONCLUSION: This preliminary study suggests that DL is a promising method for identifying urinary stone components from intraoperative endoscopic images. Compared to intraoperative identification of stone components by the human eye, DL can discriminate single and mixed stone components more accurately and quickly. At the same time, based on the training of stone images in vitro, it is closer to the clinical application of stone images in vivo. This technology can be used to identify the composition of stones in real time and to adjust the frequency and energy intensity of the holmium laser in time. The prediction of stone composition can significantly shorten the operation time, improve the efficiency of stone surgery and prevent the risk of postoperative infection.

U-Net-Based Assistive Identification of Bladder Cancer: A Promising Approach for Improved Diagnosis.

INTRODUCTION: Bladder cancer (BC) is a major health concern that poses a significant threat to the population, with an increasing incidence rate and a high risk of recurrence and progression. The primary clinical method for diagnosing BC is cystoscopy, but due to the limitations of traditional white light cystoscopy and inadequate clinical experience among junior physicians, its detection rate for bladder tumor, especially small and flat lesions, is relatively low. However, recent years have seen remarkable advancements in the application of artificial intelligence (AI) technology in the field of medicine. This has led to the development of numerous AI algorithms that have been successfully integrated into medical practices, providing valuable assistance to clinicians. The purpose of this study is to develop a cystoscopy algorithm that is real time, cost effective, high performing, and accurate, with the aim of enhancing the detection rate of bladder tumors during cystoscopy. MATERIALS AND METHODS: For this study, a dataset of 3,500 cystoscopic images obtained from 100 patients diagnosed with BC was collected, and a deep learning model was developed utilizing the U-Net algorithm within a convolutional neural network for training purposes. RESULTS: This study randomly divided 3,500 images from 100 BC patients into training and validation groups, and each patient's pathology result was confirmed. In the validation group, the accuracy of tumor recognition by the U-Net algorithm reached 98% compared to primary urologists, with greater accuracy and faster detection speed. CONCLUSION: This study highlights the potential of U-Net-based deep learning techniques in the detection of bladder tumors. The establishment and optimization of the U-Net model is a significant breakthrough and it provides a valuable reference for future research in the field of medical image processing.

Effect of Flexible Ureteroscopic Lithotripsy on Surgical Outcomes, Renal Function and Quality of Life of Patients with 2-3 cm Renal Calculi.

AIM: This retrospective study aims to analyse the effect of flexible ureteroscopic lithotripsy (FURSL) on the surgical outcome, renal function (RF) and quality of life (QoL) of patients with 2-3 cm renal calculi. METHODS: A total of 111 patients with renal calculi (2-3 cm) admitted from January 2019 to May 2022 were selected. Among them, 55 patients who underwent minimally invasive percutaneous nephrolithotomy (PCNL) were set as the control group and 56 patients treated with FURSL served as the research group. The control group consisted of 29 males and 26 females aged (43.31 ± 6.49) years on average. The research group consisted of 31 males and 25 females, with a mean age of (42.46 ± 7.44) years. Parameters such as surgical outcomes (stone clearance rate, bleeding volume, operation time and postoperative recovery time), incidence of adverse reactions (ARs: Gross hematuria, fever, urinary tract infection (UTI) and urinary tract injury), RF (blood urea nitrogen (BUN) and serum creatinine (Scr)), pain degree and QoL were compared. RESULTS: No significant difference in the stone clearance rate was found between the groups. Compared with the control group, the research group had statistically longer operation time, less bleeding, postoperative recovery time, and incidence of ARs and pain and obviously higher QoL. BUN and Scr differed insignificantly between the groups before and after surgery. CONCLUSIONS: FURSL can accelerate postoperative recovery in patients with 2-3 cm renal calculi, lower the risk of postoperative ARs, mitigate pain and improve QoL without significantly affecting RF.

Determinants of anxiety and depression among pheochromocytoma patients: A case-control study.

ABSTRACT: Phaeochromocytomas are catecholamine-producing neuroendocrine tumors that may manifest in many ways, specifically as sustained or paroxysmal hypertension. Data, including data from mental status screening, were prospectively collected from suspected patients. The Hospital Anxiety and Depression Scale was used as a screening tool to identify abnormal mental status. Results showed phaeochromocytoma patients were more likely to experience anxiety and depression. For future phaeochromocytoma treatment, early screening for anxiety and depression should be recommended.