Researchers developed and validated machine learning-based predictive models for lymph node metastasis (LNM) in intrahepatic cholangiocarcinoma (ICC) using clinicopathological data from 345 patients. The study used six machine learning algorithms, identifying the Extreme Gradient Boosting (XGB) model as the best, with an average area under the curve (AUC) of 0.908. Factors such as alcoholic liver […]
Category: Digital Surgery and Telemedicine
Artificial Intelligence for Predicting Lymph Node Metastases in Early-Stage Colorectal Cancer
Artificial intelligence models demonstrated high accuracy levels in predicting lymph node metastasis in early-stage colorectal cancers, outperforming traditional clinical guidelines. With models such as support vector machine and deep learning, there is potential for refining clinical decisions and improving outcomes in this critical area of cancer treatment. Journal Article by Thompson N, Morley-Bunker A (…) […]
Telemedicine Interventions Reduce Readmissions After Abdominal Surgery
A systematic review and meta-analysis examining the impact of telemedicine in perioperative care found that digital health interventions are associated with a lower risk of readmissions and emergency department (ED) visits in patients who have undergone abdominal surgery. Across 19 studies with 10,536 patients, the risk ratio for ED visits (0.78; 95% CI, 0.65-0.94) and […]
Accuracy and Clinical Correlation of AI-based Computer Vision in Laparoscopic Appendectomy
Implementation of an AI-based computer-vision model in laparoscopic appendectomy accurately assesses complexity grading and safety adherence, predicting operative time and intraoperative course. However, no clinical correlation is found regarding postoperative outcomes. Surgeons and inter-surgeons agreements were high for complexity grading and safety adherence. Further studies are needed to validate these findings. Journal Article by Dayan […]
Machine Learning Models to Predict Laparoscopic Surgery Difficulty in Rectal Cancer
Researchers developed and validated machine learning (ML) models to predict the difficulty of laparoscopic total mesorectal excision (LaTME) in rectal cancer. In a study with 186 patients, four ML models—logistic regression (LR), support vector machine (SVM), random forest (RF), and decision tree (DT)—were developed using preoperative and intraoperative factors. All models showed strong performance in […]
Machine Learning for Predicting Future Surgery in Ileal Crohn’s Disease Using CT-Enterography
Researchers used machine learning (ML) to quantify cumulative ileal injury on CT-enterography (CTE) scans for patients with ileal Crohn’s disease (CD) to predict future bowel surgery. Analysis of 229 CTE scans with 8,424 mini-segments revealed strong agreement between ML and radiologists in grading bowel injury (κ=0.80), similar to inter-radiologist agreement (κ=0.87). Cumulative injury scores were […]
Virtual Reality as an Effective Pain Management Tool After Surgery
Virtual reality (VR) is more effective than standard iPad use in decreasing pain after surgery in pediatric patients. A randomized controlled study found that patients using VR experienced lower pain scores and decreased opioid consumption compared to those using iPads. Younger patients benefited more from VR intervention during the immediate postoperative period. Journal Article by […]
Performance of AI Chatbots in Surgical Decision-making for Gastroesophageal Reflux Disease
Large language model-linked chatbots showed varying accuracy in providing surgical management recommendations for gastroesophageal reflux disease. Google Bard had the highest accuracy, while Copilot and Perplexity had lower performance. Additional training using evidence-based health information is needed to maximize the potential of chatbots in clinical practice. Journal Article by Huo B, Calabrese E (…) Vosburg […]
Robotic System Training Develops Tissue Handling Skills Faster Than Virtual Reality
Robotic novices showed comparable improvements in tissue handling skills after both virtual reality (VR) and robotic system training. However, participants required significantly fewer repetitions to reach proficiency in tasks when trained on the robotic system compared to the VR simulator. This suggests that while VR training is effective, training on a real robotic system may […]
Performance of AI Chatbots in Surgical Decision-making for Gastroesophageal Reflux Disease
Large language model-linked chatbots showed varying accuracy in providing surgical management recommendations for gastroesophageal reflux disease. Google Bard had the highest accuracy, while Copilot and Perplexity had lower performance. Additional training using evidence-based health information is needed to maximize the potential of chatbots in clinical practice. Journal Article by Huo B, Calabrese E (…) Vosburg […]