Comprehensive machine learning facilitated the identification of 12 consensus predicted genes (CPGs) linked to non-alcoholic fatty liver disease (NAFLD) following metabolic and bariatric surgery (MBS). A mouse model confirmed the mRNA expression of six hub genes—PPARA, PLIN2, MED13, INSIG1, CPT1A, and ALOX5AP—showing strong correlations with data from three validation datasets. These findings highlight the potential […]
Category: Digital Surgery and Telemedicine
Machine learning effectively predicts early recurrence in gastric cancer.
A multicenter study identified early recurrence (ER) within two years post-surgery in 15% of 11,615 gastric cancer patients. Utilizing machine learning, researchers developed a stacking ensemble model that achieved an area under the receiver operating characteristic curve of 1.0 for training and 0.8 for testing, indicating robust predictive capability. Key predictors included tumor size, staging, […]
Surgical navigation enhances successful lymph node removal outcomes
Surgical navigation significantly improves successful retroperitoneal lymph-node dissection outcomes, achieving an 85% success rate compared to 50% in conventional methods. The trial, involving 69 participants, demonstrated that navigation aided surgeons in localizing targeted lymph nodes more effectively. Both complication rates were similar across both groups, and surgeons rated the navigation system favorably. These findings indicate […]
Machine learning predicts duodenal stump leakage risk in gastric cancer
A new predictive model utilizing machine learning has shown promise in forecasting duodenal stump leakage after laparoscopic gastrectomy for gastric cancer. Analyzing data from 4,070 patients, the support vector machine emerged as the most effective algorithm, achieving high sensitivity, accuracy, and specificity. Key factors influencing leakage risk include tumor location, tumor stage, operation time, preoperative […]
Telemedicine shows promise in improving surgical care in Africa
Telemedicine has emerged as a transformative force in African surgical care, particularly in areas with scarce access to quality treatment. Despite challenges such as infrastructure deficits and personnel training, the implementation of telemedicine has demonstrated favorable outcomes. Increased adoption is evident in postsurgical care and doctor-patient consultations since the pandemic. While the potential for telesurgery […]
Machine learning models effectively predict appendicitis in emergencies
This proof-of-concept study reveals that machine learning models can predict appendicitis in patients with acute abdominal pain more accurately than traditional methods. With AUROCs of 0.919 and 0.923 when including laboratory test results, the models outperformed the Alvarado scoring system (AUROC of 0.824) and matched or exceeded the performance of emergency department physicians. These findings […]
Deep learning model predicts early recurrence in gastric cancer
A deep learning model (DLRMLP) integrating clinical factors outperformed traditional methods in predicting early recurrence of locally advanced gastric cancer (LAGC) post-gastrectomy. In a study involving 620 patients, DLRMLP achieved an AUC of 0.891 compared to 0.797 with conventional models. This model effectively stratified early recurrence-free survival, disease-free survival, and overall survival (all p < […]
Deep-learning model surpasses traditional methods in predicting HCC recurrence
A novel deep-learning model, Recurr-Net, showed superior accuracy in predicting hepatocellular carcinoma (HCC) recurrence post-surgery compared to histological microvascular invasion (MVI) and conventional clinical prediction scores. In a study of 1,231 patients, Recurr-Net achieved AUROC scores between 0.770 and 0.857 for internal validation, significantly better than MVI and various clinical risk scores. The model effectively […]
Digital reservation path enables efficient specialist appointments
A newly implemented structured reservation pathway (PRP) at Renji Hospital in Shanghai has improved access to specialist appointments for patients. Analysis of 58,271 applicants over two years revealed an overall pass rate of 34.8%, with significant demographic influences on outcomes. Age emerged as the primary predictor for approval through a random forest model with 92.31% […]
Fusion model predicts KRAS mutations in rectal cancer effectively
A study evaluated deep learning, radiomics, and fusion models to predict KRAS mutations in rectal cancer using endorectal ultrasound images from 304 patients. Among the models, the feature-based fusion model (dlrexpand10_fb) achieved the highest area under the receiver operating characteristic curve (AUC) of 0.896, indicating superior predictive performance. Additionally, incorporating peritumoral regions significantly improved the […]