Virtual Indigo Carmine Chromoendoscopy (VICI) converted from peroral cholangioscopy images using artificial intelligence technology demonstrated superior visualization of surface structures and lesion margins compared to white light imaging and narrow-band imaging. VICI, in combination with white light imaging, significantly improved diagnostic accuracy for biliary strictures, showing potential as a supportive modality for peroral cholangioscopy. Journal […]
Category: Digital Surgery and Telemedicine
AI Imaging Diagnosis Improves Lymph Node Metastasis Detection in Low Rectal Cancer
High-precision AI imaging methods, combining super-resolution and 3D shape data, show promising outcomes for diagnosing lymph node metastasis in low rectal cancer. In a pilot study, these methods demonstrated superior sensitivity, negative predictive value, and accuracy, offering potential as a game changer in cancer diagnosis and treatment. Although limited by a small sample size and […]
Higher Adoption of Best Practices in Minimally Invasive Surgery with Surgical Intelligence
Surgical intelligence led to a significant increase in Critical View of Safety (CVS) adoption in laparoscopic cholecystectomy procedures, reaching 69.2% from 39.2% within six months. Visualization of the cystic duct and artery significantly improved adoption rates. Procedures with full CVS were shorter with fewer events, demonstrating improved efficiency. Surgical intelligence can uncover new insights, modify […]
Digitizing Surgical Data Delivery in Tanzanian Hospitals
Digitizing operating theater data at Tanzanian referral hospital showcased significant advancements in data management, with over 4449 procedures conducted, emphasizing orthopedics/trauma. Challenges included securing local support and technology integration, but lessons learned highlighted the importance of stakeholder communication and training. The success of the web-based platform suggests scalability potential, with recommendations for ongoing monitoring, local […]
Machine Learning Predicts Kidney Allograft Survival
Using machine learning, researchers identified serum creatinine levels, pre-transplant body weight, age of patients, and bkpyv infection as key predictors for early allograft loss in kidney transplant patients. The model achieved a high precision of 0.81, sensitivity of 0.61, specificity of 0.89, and auc value of 0.84. These findings highlight the potential of machine learning […]
AI Revolutionizes Liver Resection Management
Artificial intelligence in perioperative planning and management of liver resection holds immense promise for the future of liver cancer care. AI-driven advancements enable precise tumor detection, patient-specific treatment predictions, enhanced surgical precision, streamlined patient monitoring, and accelerated research development. The integration of AI revolutionizes liver cancer care, offering personalized, efficient, and effective solutions that improve […]
TraumaICDBERT Excels in Predicting Injury ICD-10 Codes
A natural language processing algorithm, traumaicdbert, demonstrated superior performance in predicting injury ICD-10 diagnosis codes compared to existing tools. Analyzing over 3000 tertiary survey notes, traumaicdbert outperformed Amazon Web Services Comprehend Medical, showing potential for improved real-time availability of diagnosis codes in clinical settings. Each survey note, on average, had 3.8 extracted injury ICD-10 codes, […]
Machine Learning Predictive Model for Distal Cholangiocarcinoma Recurrence
Integration of machine learning into prognostic modeling for distal cholangiocarcinoma yielded a robust predictive model for disease-free survival after pancreaticoduodenectomy. Significant predictors of recurrence included lnr15, neural invasion, n stage, surgical radicality, and differentiation grade. The model demonstrated strong discriminatory capacity with internal and external validations showing high accuracy levels. This tool can enhance tailored […]
Artificial intelligence predicts non-transplantable recurrence in hepatocellular carcinoma
Artificial intelligence models accurately predicted non-transplantable recurrence in hepatocellular carcinoma patients post-hepatic resection. An ensemble AI model showed high accuracy (AUC 0.751-0.858) with incorporation of postoperative factors. Radiologic tumor burden and microvascular invasion were key predictors. Patients with predicted recurrence had lower survival rates, highlighting the models’ prognostic value. The AI models could guide treatment […]
Automatic Tissue Recognition in Surgery with Hyperspectral Imaging
Hyperspectral imaging, combined with machine learning, enables automatic tissue recognition during surgery. A study utilizing convolutional neural networks achieved high true positive rates for skin and liver tissues. Misclassifications mainly occurred between tissues with similar origins. The study underscores the potential of hyperspectral imaging in surgical optomics, offering a new approach for extracting tissue features. […]