Automated surgical step recognition in transurethral bladder tumor resection using artificial intelligence: transfer learning across surgical modalities

ObjectiveAutomated surgical step recognition (SSR) using AI has been a catalyst in the “digitization” of surgery. However, progress has been limited to laparoscopy, with relatively few SSR tools in endoscopic surgery. This study aimed to create a SSR model for transurethral resection of bladder tumors (TURBT), leveraging a novel application of transfer learning to reduce video dataset requirements.Materials and methodsRetrospective surgical videos of TURBT were manually annotated with the following steps of surgery: primary endoscopic evaluation, resection of bladder tumor, and surface coagulation. Manually annotated videos were then utilized to train a novel AI computer vision algorithm to perform automated video annotation of TURBT surgical video, utilizing a transfer-learning technique to pre-train on laparoscopic procedures. Accuracy of AI SSR was determined by comparison to human annotations as the reference standard.ResultsA total of 300 full-length TURBT videos (median 23.96 min; IQR 14.13–41.31 min) were manually annotated with sequential steps of surgery. One hundred and seventy-nine videos served as a training dataset for algorithm development, 44 for internal validation, and 77 as a separate test cohort for evaluating algorithm accuracy. Overall accuracy of AI video analysis was 89.6%. Model accuracy was highest for the primary endoscopic evaluation step (98.2%) and lowest for the surface coagulation step (82.7%).ConclusionWe developed a fully automated computer vision algorithm for high-accuracy annotation of TURBT surgical videos. This represents the first application of transfer-learning from laparoscopy-based computer vision models into surgical endoscopy, demonstrating the promise of this approach in adapting to new procedure types

Five-Year Mortality Rates Following Elective Shoulder Arthroplasty and Shoulder Arthroplasty for Fracture in Patients Over Age 65

Background:. To effectively counsel patients prior to shoulder arthroplasty, surgeons should understand the overall life trajectory and life expectancy of patients in the context of the patient’s shoulder pathology and medical comorbidities. Such an understanding can influence both operative and nonoperative decision-making and implant choices. This study evaluated 5-year mortality following shoulder arthroplasty in patients ≥65 years old and identified associated risk factors. Methods:. We utilized Centers for Medicare & Medicaid Services Fee-for-Service inpatient and outpatient claims data to investigate the 5-year mortality rate following shoulder arthroplasty procedures performed from 2014 to 2016. The impact of patient demographics, including fracture diagnosis, year fixed effects, and state fixed effects; patient comorbidities; and hospital-level characteristics on 5-year mortality rates were assessed with use of a Cox proportional hazards regression model. A p value of <0.05 was considered significant. Results:. A total of 108,667 shoulder arthroplasty cases (96,104 nonfracture and 12,563 fracture) were examined. The cohort was 62.7% female and 5.8% non-White and had a mean age at surgery of 74.3 years. The mean 5-year mortality rate was 16.6% across all shoulder arthroplasty cases, 14.9% for nonfracture cases, and 29.9% for fracture cases. The trend toward higher mortality in the fracture group compared with the nonfracture group was sustained throughout the 5-year postoperative period, with a fracture diagnosis being associated with a hazard ratio of 1.63 for mortality (p < 0.001). Medical comorbidities were associated with an increased risk of mortality, with liver disease bearing the highest hazard ratio (3.07; p < 0.001), followed by chronic kidney disease (2.59; p < 0.001), chronic obstructive pulmonary disease (1.92; p < 0.001), and congestive heart failure (1.90; p < 0.001). Conclusions:. The mean 5-year mortality following shoulder arthroplasty was 16.6%. Patients with a fracture diagnosis had a significantly higher 5-year mortality risk (29.9%) than those with a nonfracture diagnosis (14.9%). Medical comorbidities had the greatest impact on mortality risk, with chronic liver and kidney disease being the most noteworthy. This novel longer-term data can help with patient education and risk stratification prior to undergoing shoulder replacement. Level of Evidence:. Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence