Intra-hemorrhoidal coagulation with 980-nm diode laser reduces postoperative pain, intra-operative bleeding, and administered analgesics with a comparable resolution rate of hemorrhoid symptoms. However, for the patients who experience complications, such as hemorrhoidal thrombosis, the overall pain may be equivalent to or even worse than conventional hemorrhoidectomy.

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To compare the perioperative outcomes of intracorporeal (ICUD) vs extracorporeal urinary diversion (ECUD) after robotassisted radical cystectomy (RARC). Patients and MethodsWe retrospectively reviewed the prospectively maintained International Robotic Cystectomy Consortium (IRCC) database. A total of 972 patients from 28 institutions who underwent RARC were included. Propensity score matching was used to match patients based on age, gender, body mass index (BMI), American Society of Anesthesiologists Score (ASA) score, Charlson Comorbidity Index (CCI) score, prior radiation and abdominal surgery, receipt of neoadjuvant chemotherapy, and clinical staging. Matched cohorts were compared. Multivariate stepwise logistic and linear regression models were fit to evaluate variables associated with receiving ICUD, operating time, 90-day high-grade complications (Clavien-Dindo Classification Grade ≥III), and 90-day readmissions after RARC. ResultsUtilisation of ICUD increased from 0% in 2005 to 95% in 2018. The ICUD patients had more overall complications (66% vs 58%, P = 0.01) and readmissions (27% vs 17%, P = 0.01), but not high-grade complications (21% vs 24%, P = 0.22). A more recent RC era and ileal conduit diversion were associated with receiving an ICUD. Higher BMI, ASA score ≥3, and

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Objectives To develop and evaluate the feasibility of an objective method using artificial intelligence (AI) and image processing in a semi‐automated fashion for tumour‐to‐cortex peak early‐phase enhancement ratio (PEER) in order to differentiate CD117(+) oncocytoma from the chromophobe subtype of renal cell carcinoma (ChRCC) using convolutional neural networks (CNNs) on computed tomography imaging. Methods The CNN was trained and validated to identify the kidney + tumour areas in images from 192 patients. The tumour type was differentiated through automated measurement of PEER after manual segmentation of tumours. The performance of this diagnostic model was compared with that of manual expert identification and tumour pathology with regard to accuracy, sensitivity and specificity, along with the root‐mean‐square error (RMSE), for the remaining 20 patients with CD117(+) oncocytoma or ChRCC. Results The mean ± sd Dice similarity score for segmentation was 0.66 ± 0.14 for the CNN model to identify the kidney + tumour areas. PEER evaluation achieved accuracy of 95% in tumour type classification (100% sensitivity and 89% specificity) compared with the final pathology results (RMSE of 0.15 for PEER ratio). Conclusions We have shown that deep learning could help to produce reliable discrimination of CD117(+) benign oncocytoma and malignant ChRCC through PEER measurements obtained by computer vision.