Artificial intelligence is revolutionizing surgical education by enhancing training through advanced digital tools. A review of thirteen studies highlights AI’s role in creating immersive environments via virtual and augmented reality, significantly improving residents’ technical skills and hand-eye coordination. Personalized feedback systems also support effective learning and assessment, bridging the gap between theoretical knowledge and practice. […]
Category: Digital Surgery and Telemedicine
New machine learning tool enhances obstructive jaundice diagnosis
A novel machine learning-based diagnostic tool for obstructive jaundice has shown promising results. Researchers analyzed data from over 6,000 patients and identified key causes including pancreatic adenocarcinoma and biliary cholangiocarcinoma. Traditional markers performed poorly on their own. Two machine learning models were developed, achieving an area under the receiver operating characteristic of 0.862 for identifying […]
Integrated care pathways enhance sarcoma treatment efficiency
A digital platform, ShapeHub, is proposed to streamline sarcoma care by integrating patient data across multiple healthcare providers. The initiative aims to address care fragmentation that often leads to delays and redundant testing. In a case study within the Swiss sarcoma network, ShapeHub demonstrated significant improvements in diagnostic pathways, decreased unplanned surgeries, and optimized radiotherapy […]
New model predicts risk of intestinal resection in hernia patients
Advanced machine-learning techniques successfully identified key predictors of intestinal resection in incarcerated inguinal hernia patients, including peritonitis, intestinal obstruction, neutrophil count, C-reactive protein, and preoperative total protein. The constructed model, validated externally, demonstrated strong predictive performance with an area under the curve exceeding 0.8 for all ten algorithms tested. Notably, the k-nearest neighbor algorithm showed […]
Machine learning predicts duodenal stump leakage in gastric cancer.
A machine learning model was developed to predict duodenal stump leakage (DSL) in 1,107 gastric cancer patients post-gastrectomy. The model utilized 189 clinical features and evaluated six algorithms, with extreme gradient boosting (XGB) achieving the highest area under the receiver operating characteristic curve (AUROC) score of 0.880. Random forest followed with a score of 0.858. […]
Gradient boosting model predicts liver metastasis in pancreatic neuroendocrine tumors.
A study utilizing data from 7,463 pancreatic neuroendocrine tumor (PanET) patients developed a gradient boosting machine (GBM) model, marking it as the most effective tool for predicting liver metastasis. The model achieved an AUC of 0.937 and an accuracy of 0.87, highlighting independent risk factors like T-stage, N-stage, and previous treatments. It culminated in a […]
Multimodal system enhances diagnostic accuracy for hepatic conditions
A deep learning diagnostic system combining CT and ultrasound achieved superior accuracy in detecting hepatic echinococcosis and other liver conditions compared to individual modalities and physician evaluations. The integrated model demonstrated an impressive accuracy of 81.6% in internal tests and 84.9% in external assessments, with high sensitivity and specificity. This innovation significantly reduces misdiagnosis rates, […]
AI aligns closely with colorectal cancer treatment decisions
A retrospective study evaluated the concordance between therapeutic recommendations made by multidisciplinary teams and those generated by ChatGPT for colorectal cancer. Of 100 patients, pre-therapeutic discussions showed a 72.5% complete concordance, while post-therapeutic discussions revealed an increase to 82.8%. Discordance was notably higher among patients over 77 years old and with higher ASA classifications. These […]
Multimodal ML Model Improves Delirium Detection Rates
A novel multimodal machine learning model significantly enhanced delirium risk stratification in hospitalized older adults. Validation outcomes revealed an impressive area under the curve of 0.94, with monthly delirium detection rates soaring from 4.42% to 17.17% following model deployment. Moreover, the post-deployment cohort experienced reduced daily doses of benzodiazepines and olanzapine, indicating potential improvements in […]
Machine learning model predicts anastomotic strictures post-surgery
A novel machine learning model demonstrates the potential to predict anastomotic strictures in patients following esophageal cancer surgery. Analyzing data from 1,549 patients, the gradient boosting machine achieved an area under the curve (AUC) of 0.886 in the training set and 0.872 in the validation set. Key predictive variables included anastomotic leakage, suture method, and […]