A comprehensive framework developed by the Society of Robotic Surgery outlines best practices for effective and ethical telesurgery. This initiative drew on extensive literature reviews and expert consensus from various disciplines. Key considerations encompass technological requirements, safety standards, training protocols, and legal issues. By promoting scalable implementation and addressing regional regulations, the framework aims to […]
Category: Digital Surgery and Telemedicine
Machine learning model significantly improves surgical time predictions
A study utilizing electronic health record data from 16,159 patients has demonstrated that an artificial neural network (ANN) model significantly outperforms traditional scheduling methods for predicting surgical procedure durations. The ANN achieved a root mean squared error of 49.7 minutes and a mean absolute error of 31.8 minutes, compared to an 18.52-minute discrepancy in surgeon […]
Advanced deep learning framework significantly improves surgical instrument tracking
A novel deep learning framework has achieved a mean average precision of 98.4% for detecting and tracking surgical instruments in laparoscopic surgery. Utilizing the yolov9n model combined with advanced tracking algorithms, it delivers real-time precision, even in challenging conditions such as rapid movements and occlusions. This innovation enhances surgical workflows, reduces the cognitive burden on […]
Technical guidelines established for remote robotic-assisted surgery
Remote robotic-assisted surgery has the potential to enhance healthcare access, improve patient outcomes, and optimize efficiency. By addressing geographic barriers and reducing travel burdens, these procedures can significantly expand access to quality care. Key to their effectiveness is robust networks, reliable connectivity, and strict cybersecurity measures to protect patient data. The established technical guidelines outline […]
AI model enhances diagnostic accuracy in colorectal cancer
A novel AI model named Coffee was developed to classify histopathological growth patterns in colorectal liver metastases, achieving impressive predictive performance with AUC values up to 1.000 in prospective cohorts. Desmoplastic tumors were linked to improved overall and progression-free survival compared to non-desmoplastic tumors. Additionally, AI-assisted pathology increased diagnostic accuracy to 94.7% for junior pathologists […]
AI model excels in early detection of post-hepatectomy liver failure
A cutting-edge AI model demonstrated significant accuracy in detecting post-hepatectomy liver failure (PHLF) within the first 24 hours post-surgery. The model achieved an AUC of 0.952 in internal validation and 0.884 in external validation among 1,832 patients across six Chinese hospitals. Moreover, it outperformed existing algorithms in challenging cases, showing an AUC of 0.654 within […]
New machine learning models improve diagnosis of obstructive jaundice
A comprehensive study analyzing 5,726 patients with obstructive jaundice has identified key etiologies, revealing cholangiocarcinoma, pancreatic adenocarcinoma, and common bile duct stones as predominant causes. Despite traditional markers lacking accuracy, two machine learning models were successfully developed, achieving an area under the receiver operating characteristic curve (AUROC) of 0.862 for differentiating benign and malignant causes. […]
Augmented reality eliminates conversion rates in liver surgeries.
A retrospective analysis reveals augmented reality (AR) enhances tumour localisation in minimally invasive liver surgery (MILS), achieving a zero conversion rate for the AR group, compared to six conversions in the non-AR group. While AR increased operative times by 10%, it did not adversely affect resection margins or postoperative complications. The findings suggest AR’s potential […]
Machine learning predicts prolonged surgery in laparoscopic cholecystectomy
A recent study revealed that machine learning can effectively predict prolonged operative times in fluorescent laparoscopic cholecystectomy, identifying 29% of patients at risk. Key predictive factors included type of cholecystitis, puncture ports, gallbladder adhesion, pre-surgery antibiotics, and gallbladder thickness. Using the light gradient boosting machine (LightGBM) model, researchers achieved an impressive AUC of 0.876, indicating […]
AI Pipeline Enhances Detection of Surgical Site Infections
An AI-based system was developed to assess patient-submitted postoperative wound images, aiming to streamline the detection of surgical site infections (SSI). Among 6,060 patients studied, the model achieved an impressive incision detection accuracy of 94% and 73% accuracy for SSI detection. This technology demonstrated robust performance in image quality assessment and showed comparable efficacy across […]