A Prediction Model Using Machine Learning Algorithm for Assessing Stone-Free Status after Single Session Shock Wave Lithotripsy to Treat Ureteral Stones.

PURPOSE: The aim of this study was to develop and validate a decision support model using a machine learning algorithm to predict treatment success after single session shock wave lithotripsy in ureteral stone cases. MATERIALS AND METHODS: Of the 1,803 patients treated with shock wave lithotripsy we selected those with ureteral stones who had preoperative computerized tomography available. Treatment success after single session shock wave lithotripsy was defined as freedom from stones or residual stone fragments less than 2 mm long on computerized tomography or plain x-ray of the kidneys, ureters and bladder 2 weeks later. Decision tree analysis was done using a machine learning algorithm to identify relevant parameters. A decision support model was developed to calculate the probability of treatment success. RESULTS: A total of 791 patients were enrolled in study. Mean ± SD stone length was 5.9 ± 2.3 mm and mean stone volume was 89.3 ± 140.0 mm3. The overall treatment success rate after SWL was 64.4% (509 cases). The rate for upper, middle and lower ureter stones was 59.8%, 65.5% and 69.6%, respectively. On decision tree analysis the top 3 performance criteria factors were volume, length and HU. Decision models were constructed with all possible combinations of factors. The model with 15 factors had greater than 92% accuracy and an average ROC AUC of 0.951. CONCLUSIONS: We applied a machine learning algorithm, a subfield of artificial intelligence, to predict the outcome after single session shock wave lithotripsy for ureteral stones. A 92.29% accurate decision model was developed with 15 factors and an average ROC AUC of 0.951.

Male-specific genetic effect on hypertension and metabolic disorders.

Genetic risk factors for hypertension may have age or gender specificity and pleiotropic effects. This study aims to measure the risk of genetic and non-genetic factors in the occurrence of hypertension and related diseases, with consideration of potential confounding factors and age-gender stratification. A discovery set of 352,228 genotyped plus 1.8 million imputed single-nucleotide polymorphisms were analyzed for 2,886 hypertensive cases and 3,440 healthy controls obtained from two community-based cohorts in Korea, and selected gene variants were replicated in the Health Examinee cohort (665 cases and 1,285 controls). Genome-wide association analyses were conducted in 12 groups stratified by age and gender after adjusting for potential covariates under three genetic models. Age, rural area residence, body mass index, family history of hypertension, male gender, current alcohol drinking status, and current smoking status were significantly associated with hypertension (P = 4 × 10(-151) to 0.011). Five gene variants, rs11066280 (C12orf51), rs12229654 and rs3782889 (MYL2), rs2072134 (OAS3), rs2093395 (TREML2), and rs17249754 (ATP2B1), were found to be associated with hypertension mostly in men (P = 4.76 × 10(-14) to 4.46 × 10(-7) in the joint analysis); three SNPs (rs11066280, rs12229654, and rs3782889) remained significant after Bonferroni correction in an independent population. Three gene variants, rs12229654, rs17249754, and rs11066280, were significantly associated with metabolic disorders such as hyperlipidemia and diabetes (P = 0.00071 to 0.0097, respectively). Careful consideration of the potential confounding effects in future genome-wide association studies is necessary to uncover the genetic underpinnings of complex diseases.

A Novel Deep Learning-based Artificial Intelligence System for Interpreting Urolithiasis in Computed Tomography.

BACKGROUND AND OBJECTIVE: Our aim was to develop an artificial intelligence (AI) system for detection of urolithiasis in computed tomography (CT) images using advanced deep learning capable of real-time calculation of stone parameters such as volume and density, which are essential for treatment decisions. The performance of the system was compared to that of urologists in emergency room (ER) scenarios. METHODS: Axial CT images for patients who underwent stone surgery between August 2022 and July 2023 comprised the data set, which was divided into 70% for training, 10% for internal validation, and 20% for testing. Two urologists and an AI specialist annotated stones using Labelimg for ground-truth data. The YOLOv4 architecture was used for training, with acceleration via an RTX 4900 graphics processing unit (GPU). External validation was performed using CT images for 100 patients with suspected urolithiasis. KEY FINDINGS AND LIMITATIONS: The AI system was trained on 39 433 CT images, of which 9.1% were positive. The system achieved accuracy of 95%, peaking with a 1:2 positive-to-negative sample ratio. In a validation set of 5736 images (482 positive), accuracy remained at 95%. Misses (2.6%) were mainly irregular stones. False positives (3.4%) were often due to artifacts or calcifications. External validation using 100 CT images from the ER revealed accuracy of 94%; cases that were missed were mostly ureterovesical junction stones, which were not included in the training set. The AI system surpassed human specialists in speed, analyzing 150 CT images in 13 s, versus 38.6 s for evaluation by urologists and 23 h for formal reading. The AI system calculated stone volume in 0.2 s, versus 77 s for calculation by urologists. CONCLUSIONS AND CLINICAL IMPLICATIONS: Our AI system, which uses advanced deep learning, assists in diagnosing urolithiasis with 94% accuracy in real clinical settings and has potential for rapid diagnosis using standard consumer-grade GPUs. PATIENT SUMMARY: We developed a new AI (artificial intelligence) system that can quickly and accurately detect kidney stones in CT (computed tomography) scans. Testing showed that this system is highly effective, with accuracy of 94% for real cases in the emergency department. It is much faster than traditional methods and provides rapid and reliable results to help doctors in making better treatment decisions for their patients.