A three-pronged approach is proposed to leverage artificial intelligence in improving surgical care access, particularly for underserved populations. By emphasizing data quality, continuous system evaluation, and ethical governance, researchers suggest that AI can reduce disparities in health outcomes. This approach addresses the risks associated with AI implementation while unlocking its potential to benefit rural and […]
Category: Digital Surgery and Telemedicine
Machine learning accurately measures tension in colorectal surgery
A novel machine learning algorithm based on long short-term memory neural networks successfully estimates mechanical tension in ex vivo porcine colons, achieving 88% accuracy and strong correlations in force measurement. This innovative approach provides a significant advance over traditional subjective tension assessment during robotic surgery, addressing the challenge of anastomotic leaks, which affect one in […]
Artificial intelligence improves safety in laparoscopic cholecystectomy
A systematic review of artificial intelligence (AI) applications in laparoscopic cholecystectomy reveals promising results. Analysis of 25 studies shows an average precision of 98% in identifying biliary structures, with the AI system altering annotations in 27% of cases. Notably, 70% of these adjustments were deemed safer, indicating AI’s potential to enhance surgical precision and mitigate […]
AI Advances Enhance Hernia Surgery Outcomes
Artificial intelligence has the potential to significantly improve hernia surgery by enhancing surgical risk assessment and outcomes. The review highlights advancements from machine learning, natural language processing, computer vision, and robotics over the past two decades. Machine learning aids in prediction model development, while natural language processing improves human-computer interactions. Computer vision is critical for […]
Multiservice machine learning models enhance surgical resource planning.
Multiservice machine learning models were developed to predict postsurgical length of stay (LOS) and discharge disposition at the time of case posting. An analysis of 63,574 patients showed the LOS model achieved an area under the curve (AUC) of 0.81. Incorporating relative value units and historical LOS improved prediction accuracy for both short and prolonged […]
Machine learning effectively predicts discharge against medical advice
A study evaluated machine learning algorithms to predict discharge against medical advice for 48,394 injured inpatients over five years. Among these individuals, 8.8% opted for discharge against medical advice. The light gradient boosting machine combined with edited nearest neighbors outperformed traditional logistic regression, achieving areas under the curve of 0.820 and 0.837 in internal and […]
Machine learning predicts surgical outcomes in cholangiocarcinoma patients
A machine learning model developed from 376 intrahepatic cholangiocarcinoma patients effectively predicts textbook outcomes. Key preoperative factors include Child-Pugh classification, ECOG score, hepatitis B status, and tumor size. The model achieved high accuracy during internal (AUC = 0.8825) and external validation (AUC = 0.8346). Survival analysis indicated that patients achieving textbook outcomes had disease-free survival […]
Deep learning model enhances bile duct identification during surgery
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 […]
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 […]