A newly developed deep learning system effectively identifies extrahepatic bile ducts in real-time during laparoscopic cholecystectomy. Trained on 3,993 images, the YoloV7 model achieved a mean average precision of 0.846 overall, with specific accuracies of 94.39% for the common bile duct and 84.97% for the cystic duct during video clip validations. By optimizing these crucial […]
Category: Digital Surgery and Telemedicine
Automated models enhance competency assessment in laparoscopic surgery
The study demonstrates the development of automated surgical action recognition models, achieving significant accuracy in competency prediction during laparoscopic cholecystectomy. Analysis of the cholec80 dataset revealed that high-competency groups exhibited shorter dissection durations and higher scores on established evaluation metrics. A random forest model achieved 93% accuracy in predicting surgical competency, while a video-masked autoencoder […]
Prognostic model predicts recurrence and treatment response in HCC
A novel prognostic model utilizing pathological signatures effectively predicts postoperative recurrence risk and sorafenib response in hepatocellular carcinoma (HCC) patients. Analyzed across 287 non-treated patients, the model achieved AUROC values of 0.818 and 0.811 for predicting one and two-year recurrence respectively. Validation with an external cohort confirmed its predictive power. Additionally, it successfully stratified sorafenib-treated […]
Large language models can improve surgical patient education
A multicenter analysis revealed that ChatGPT-4o effectively enhances patient education before and after surgery. Evaluating audio responses to frequently asked questions, the model achieved an average accuracy score of 4.12/5 and a relevance score of 4.46/5. Postoperative responses were notably more accurate and less harmful than preoperative ones. Results suggest that integrating ChatGPT-4o in clinical […]
Machine learning effectively predicts postpancreatectomy acute pancreatitis
A novel machine learning model successfully predicts postpancreatectomy acute pancreatitis (PPAP) in patients following pancreaticoduodenectomy. In a cohort of 381 patients, 88 (23.09%) developed PPAP, which was notably associated with a higher occurrence of postoperative pancreatic fistulas (55.68%). Various algorithms, including logistic regression and gradient boosting, were tested, with recursive feature elimination optimizing variable selection. […]
Improved visualization of surgical needle tips enhances accuracy
A novel model was developed to visualize hidden surgical needle tips within organs, addressing challenges in laparoscopic procedures that could lead to complications like anastomotic leakage. Testing revealed real-time image inference at 33.4 frames per second and a mean needle misalignment of just 1.03 mm, well below the 1.8 mm threshold. These findings suggest that […]
AI has transformative potential for surgical education.
Artificial intelligence could revolutionize surgical education by providing personalized feedback, improved competency evaluations, and enhanced candidate selection. AI-driven simulations foster adaptive learning among trainees, while intraoperative tools may assist surgeons in complex procedures. However, challenges such as data quality, ethical concerns, and the risk of overskilling need to be addressed. Developing regulatory frameworks emphasizing transparency […]
Machine learning accurately predicts temporary stoma formation in CD patients
A machine learning model effectively predicts temporary stoma formation following intestinal resection in Crohn’s disease patients. An analysis of 252 patient records identified eight key predictors, achieving an area under the curve (AUC) between 0.886 and 0.998. The random forest algorithm showed the highest predictive accuracy, leading to potential improvements in surgical decision-making and personalized […]
Intraoperative surgeon metrics predict robotic herniorrhaphy outcomes
Machine learning analysis on robotic ventral hernia repair data revealed that intraoperative surgeon performance indicators significantly influence postoperative complications. Evaluating 520 patients, the study achieved high predictive accuracy (0.95) for complications using various algorithms. Notably, 6.3% experienced complications at discharge, emphasizing the interplay between surgical techniques and patient-specific factors. The findings pave the way for […]
Machine learning model effectively predicts postoperative recurrence in duodenal adenocarcinoma
A machine learning-based model has been developed to predict postoperative recurrence in patients with duodenal adenocarcinoma, utilizing data from 1,830 patients who underwent radical surgery. Among 100 predictive models tested, the penalized regression + accelerated oblique random survival forest model (pam) delivered the highest performance with a concordance index (c-index) of 0.882 in the training […]