A retrospective study evaluated the concordance between therapeutic recommendations made by multidisciplinary teams and those generated by ChatGPT for colorectal cancer. Of 100 patients, pre-therapeutic discussions showed a 72.5% complete concordance, while post-therapeutic discussions revealed an increase to 82.8%. Discordance was notably higher among patients over 77 years old and with higher ASA classifications. These […]
Category: Digital Surgery and Telemedicine
Multimodal ML Model Improves Delirium Detection Rates
A novel multimodal machine learning model significantly enhanced delirium risk stratification in hospitalized older adults. Validation outcomes revealed an impressive area under the curve of 0.94, with monthly delirium detection rates soaring from 4.42% to 17.17% following model deployment. Moreover, the post-deployment cohort experienced reduced daily doses of benzodiazepines and olanzapine, indicating potential improvements in […]
Machine learning model predicts anastomotic strictures post-surgery
A novel machine learning model demonstrates the potential to predict anastomotic strictures in patients following esophageal cancer surgery. Analyzing data from 1,549 patients, the gradient boosting machine achieved an area under the curve (AUC) of 0.886 in the training set and 0.872 in the validation set. Key predictive variables included anastomotic leakage, suture method, and […]
Deep learning model achieves high accuracy in ureter identification
A deep learning computer vision model demonstrated precise real-time identification of the left ureter during laparoscopic sigmoidectomy, achieving a mean average precision of 0.92. Key evaluation metrics, including precision, recall, and dice coefficient, reached impressive values of 0.94, 0.88, and 0.90, respectively. Operating at 32 frames per second, the model significantly aids surgical navigation. Despite […]
ChatGPT-4o is superior in aiding gastric cancer decisions
ChatGPT-4o significantly outperformed Gemini Advanced in generating treatment recommendations for advanced gastric cancer, as evidenced by a structured evaluation of responses to ten clinical questions. It provided superior recommendations in surgical suggestions and chemotherapy options during multidisciplinary team discussions. Additionally, it excelled in analyzing rare cases from PubMed, demonstrating increased accuracy and consistent evaluator agreement. […]
Deep learning effectively identifies pathologic complete response in esophageal cancer.
A deep neural network-based endoscopic evaluation accurately detected pathologic complete response (PCR) in esophageal cancer patients after neoadjuvant chemotherapy, achieving median sensitivity, specificity, and accuracy rates of 80%, 90%, and 85%, respectively. Among 1041 patients, 354 demonstrated PCR, correlating with significantly improved overall and recurrence-free survival. The findings suggest the potential for this AI technology […]
AI Advances in Laparoscopic Skill Assessment Show High Accuracy
AI applications in laparoscopic surgery simulation demonstrate significant promise, achieving concordance rates of 42%-100% with human assessments. The study tested AI trained on multiple procedures, revealing 100% accuracy for the appendectomy in the five-class scoring system and also high accuracy in the two-class system across three different procedures. These results suggest that AI can reliably […]
AI system enhances polyp detection in colonoscopy
The developed AI-assisted system significantly improves the detection and localization of colorectal polyps during colonoscopy. Out of five CNN models tested, EfficientNetV2 excelled, achieving an accuracy of 93.3% and a specificity of 94.6% on the test set. The system’s performance metrics, including a high AUC of 0.983, suggest its potential for clinical applications, as illustrated […]
A novel MRI-based model predicts perineural invasion in cholangiocarcinoma
An innovative MRI-based fusion model effectively predicts perineural invasion (PNI) status in intrahepatic cholangiocarcinoma (ICC) patients. The model, incorporating deep learning, radiomics, and clinical features, was developed from a study involving 192 patients. In the training phase, it achieved an AUC of 0.905 and 82.3% accuracy, while the external test set yielded an AUC of […]
AI model predicts in-hospital mortality for elderly surgical patients
An AI-driven Geriatric Emergency Perioperative Risk Index (GEPR) effectively predicts postoperative in-hospital mortality for elderly patients undergoing emergency general surgery. Utilizing data from 1,500 patients, the Random Forest Classifier algorithm excelled in accuracy, achieving a c-statistic of 0.872. The probability of in-hospital mortality varied significantly, increasing from 0% to 100% as GEPR scores ranged from […]