A new deep learning model utilizing multiphase CT images effectively predicts early recurrence in patients with locally advanced gastric cancer (LAGC). The model, integrated with clinical factors, demonstrated superior performance (AUC: 0.891) compared to previous models. Significant associations were found between higher prediction scores and tumor proliferation pathways, such as WNT and MYC signaling, as […]
Category: Digital Surgery and Telemedicine
AI-based scoring system enhances prognosis prediction for HCC
A novel AI-driven system effectively quantifies CD8+ tumor-infiltrating lymphocytes (TILs) in hepatocellular carcinoma (HCC) patients post-liver resection. In a study involving 514 patients, the automated CD8+ TIL scoring system (ATLS-8) revealed five-year overall survival rates of 34.05% for low-score and 65.03% for high-score cohorts. This research highlights the independent prognostic value of CD8+ TIL density, […]
Telemedicine significantly reduces outpatient waiting times.
A systematic review revealed telemedicine’s effectiveness in decreasing outpatient wait times, showcasing a substantial weighted mean reduction of 25.4 days across 270,388 patients. Notably, for clinical specialties, the reduction reached 34.7 days, and for surgical patients, it was 17.3 days. Most studies demonstrated low bias risk, underscoring telemedicine’s potential for enhancing equitable access to healthcare […]
Explainable machine learning model predicts mortality in infected pancreatic necrosis
An explainable machine learning model for predicting 90-day mortality in patients with infected pancreatic necrosis (IPN) has shown promising results. The final model, developed from a cohort of 344 patients, achieved a c-index of 0.863 in internal validation and 0.857 in external validation, outperforming nine other models. Key mortality predictors included multiple organ failure and […]
AI framework quantifies clinical influences on posthepatectomy length of stay
An innovative artificial intelligence framework was developed to quantify the impact of clinical factors on posthepatectomy length of stay (LOS), explaining 75% of its variability. The study analyzed 21,039 patients, revealing that clinical influences are significant while nonclinical factors account for the remaining 25%. Notably, major resections had a longer mean LOS of 6.9 days […]
Clinical factors explain less than 55% of postoperative stay variability
A machine-learning framework quantified the impact of clinical versus nonclinical influences on postoperative length of stay after colectomy. Analysis of 96,081 patients revealed that clinical factors accounted for only 29-54% of length of stay variability. Despite optimizing these variables, significant unexplained variance remained, indicating the influence of nonclinical factors. This study is pioneering in highlighting […]
Machine learning effectively predicts gastroparesis risk in surgery patients
Advanced machine learning techniques, particularly the xgboost algorithm, have shown exceptional predictive accuracy for identifying the risk of postoperative gastroparesis in colon cancer patients after complete mesocolic excision. From a cohort of 1,097 patients, featuring 87 gastroparesis cases, xgboost achieved an area under the curve of 0.939 for training and 0.876 for validation. This predictive […]
Deep learning algorithm excels in kidney trauma detection
A deep learning model, RenotrNet, demonstrated high accuracy in detecting kidney trauma on CT scans, achieving 0.88 accuracy in internal testing and 0.93 in external validation with RSNA data. The model showed robust performance metrics, including 0.75 sensitivity and 0.95 specificity internally, and 0.73 sensitivity and 0.94 specificity externally. Its high negative predictive value of […]
Predictive model identifies factors for ERCP post-cholecystectomy
A machine learning-based predictive model has been developed to estimate the incidence of endoscopic retrograde cholangiopancreatography (ERCP) after emergency laparoscopic cholecystectomy. Analyzing data from 8,854 patients, the model revealed a postoperative ERCP incidence of 5.7% in training and 6.4% in testing datasets. The gradient-boosting decision tree model excelled, with common bile duct dilatation, serum albumin, […]
3D surgical videos enhance student preparedness despite lower knowledge retention.
A randomized controlled trial with 231 medical students found that while 3D immersive videos led to lower immediate knowledge retention compared to 2D videos, they significantly improved expected preparedness, satisfaction, and self-confidence after one month. Participants using 3D video reported feeling more engaged, better able to identify anatomical structures, and less overwhelmed. These findings suggest […]