A new deep learning model demonstrated strong predictive capabilities for mismatch repair deficiency in colorectal cancer. An area under the receiver operating characteristic (AUROC) score of 0.948 was achieved during testing, while independent validation yielded an AUROC of 0.807. Notably, the model achieved a negative predictive value of 94.2%. In a prospective trial, it accurately […]
Category: Digital Surgery and Telemedicine
AI system significantly improves surgical case length predictions
An artificial intelligence system was developed for predicting surgical case lengths, significantly outperforming current electronic health record estimates. Analyzed data from 125,493 inpatient elective surgical cases revealed the categoricalboost regressor using clinical text embeddings achieved a mean absolute error of 46.4 minutes, compared to 120.0 minutes for existing records (p < 0.001). The AI model […]
AI surpasses human endoscopists in detecting colorectal lesions
A deep learning algorithm based on the yolov7 model demonstrated exceptional performance in identifying laterally spreading tumors (LSTs) with an accuracy of 99.34%. Sensitivity was recorded at 96.88%, while specificity reached 99.8%. This algorithm outperformed both novice and senior endoscopists, achieving diagnostic capabilities comparable to expert professionals. The study utilized a substantial dataset of 7985 […]
Decentralized Knowledge Graph Improves Medical Data Organization
A decentralized knowledge graph (dkg) was developed to enhance the organization of medical and surgical data, improving the quality of health data collection. By utilizing the cyb.ai protocol within the cosmos network, this system leverages emergency surgery publications to create a structured bibliographic resource. The dkg not only facilitates a better understanding of medical relationships […]
New segmentation method significantly improves liver cancer imaging
The DSA-Former network introduces a hybrid approach for segmenting liver and liver tumours, effectively integrating convolutional kernels and attention mechanisms. Achieving dice coefficients of 96.8% for liver segmentation and 72.2% for tumour segmentation, this method outperforms existing techniques in key metrics such as IoU and HD95. Enhanced segmentation precision promises to bolster the accuracy of […]
Postoperative complications in colorectal cancer surgery lack uniform guidelines
A systematic review of 135 articles on postoperative complications from colorectal cancer surgery underscores significant disparities in research focus and treatment guidelines. Notably, anastomotic leakage and postoperative infections accounted for 35% and 25% of studies, while port-site metastases and cardiovascular dysfunctions received limited attention. The prevalence rates of complications varied widely, from 3% to 20%, […]
New AI model enhances detection of colorectal liver metastases
An innovative AI-based recognition model for detecting colorectal liver metastases during contrast-enhanced intraoperative ultrasonography was developed. By integrating the basic recognition model and a subtraction model, a combination model demonstrated superior accuracy, reaching 96.5%. Overall, the combination model achieved an exceptional area under the curve (AUC) of 0.99, significantly surpassing individual models, which achieved 89.4% […]
Telesurgery shows promise for equitable surgical care access
Telesurgery is emerging as a vital solution for delivering expert surgical care to underserved regions, addressing global healthcare disparities. While it holds transformative potential, significant challenges remain, including data transmission issues, latency, and the necessity for advanced robotic platforms. The introduction of 5G networks offers a hopeful technological backdrop, yet disparities in coverage persist. Ethical […]
H. pylori infection identified as key factor in gastric cancer recurrence
A machine learning study involving 1,234 gastric cancer patients revealed that H. pylori infection is the predominant high-risk factor for cancer recurrence post-gastrectomy. Utilizing four algorithms, the XGBoost model outperformed others in accuracy, identifying critical risk elements such as tumor invasion depth and lymph node metastasis. The findings suggest that machine learning can aid clinicians […]
Machine Learning Model Accurately Predicts Early Recurrence in PCCA Patients
A multicenter study developed a machine learning model to predict early recurrence in patients with perihilar cholangiocarcinoma (PCCA) after curative surgery. The model, leveraging five key factors, including carbohydrate antigen 19-9 and tumor size, achieved superior predictive performance, particularly with the random forest algorithm (AUC: 0.983) compared to others. High-risk patients showed significantly different recurrence-free […]