A prospective study validated the AI-based POTTER calculator for predicting outcomes in emergency general surgery patients undergoing laparotomy. Involving 361 patients, it demonstrated high accuracy, with a c-statistic of 0.90 for 30-day postoperative mortality prediction and between 0.80 and 0.89 for other complications. The tool’s user-friendliness and interpretability enhance its value for preoperative counseling, establishing […]
Category: Digital Surgery and Telemedicine
Deep learning accurately classifies colorectal liver metastasis growth patterns
A new deep learning algorithm successfully distinguishes between desmoplastic and non-desmoplastic histopathological growth patterns in colorectal liver metastases, achieving high discriminatory power with area under the curve values of 0.93 and 0.95 during development and external validation, respectively. This automated classification parallels manual scoring regarding overall survival outcomes, ensuring its potential utility in routine histopathological […]
Ecart AI tool outperforms other warning scores for patient deterioration
In a cohort study of 362,926 hospital encounters, the Ecart early warning score demonstrated superior performance in identifying patient clinical deterioration, achieving an area under the receiver operating characteristic curve of 0.895. The study compared three artificial intelligence scores and three traditional scores, revealing that Ecart provided better predictive values and lead time for interventions. […]
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 […]
Machine learning predicts early recurrence after liver surgery.
A new machine learning model successfully predicted early extrahepatic recurrence (EEHR) following curative resection of colorectal liver metastases (CRLM). Among 1,410 patients, 131 (9.3%) experienced EEHR, with median overall survival significantly lower for affected patients (35.4 months) versus those without (120.5 months, p < 0.001). The model achieved a c-index of 0.77, highlighting primary tumor […]
Telemedicine is favored by older cancer patients for consultations
A significant majority (77%) of older cancer patients opted for telemedicine consultations over in-person visits at the Cancer and Aging Interdisciplinary Team clinic. Factors influencing in-person visit choices included older age, lower educational status, living in New York City, cognitive impairments, performance measure challenges, and social support issues. The findings highlight the potential of telemedicine […]
New machine learning model accurately predicts cancer treatment response
A novel machine learning model, utilizing systemic inflammation-nutritional index (SINI), successfully predicts pathological complete response (PCR) in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy (NCRT). The model achieved a mean area under the curve (AUC) of 0.877 during training and demonstrated consistent performance in both internal (AUC 0.86) and external validation sets (AUC […]
Risk-specific training enhances surgical complication prediction
Utilizing deep learning models on separately defined risk cohorts significantly improved predictive accuracy for postoperative complications. Training on high-risk patients yielded better area under the precision-recall curve for predicting in-hospital mortality, acute kidney injury, and prolonged ICU stays. This tailored approach notably enhanced F1 scores for various complications, suggesting that risk-specific training could address class […]
Machine learning enhances assessment of colorectal liver metastases response
A machine learning model utilizing CT-based radiomics significantly surpassed traditional radiologist assessments in estimating pathologic response of colorectal liver metastases after neoadjuvant therapy. In a study involving 85 patients, the model achieved an area under the curve (AUC) of 0.87, contrasting sharply with the AUCs of 0.53 for RECIST assessments and 0.56 for morphologic evaluation. […]
AI model predicts futility of surgery in cholangiocarcinoma patients
An artificial intelligence model effectively predicted “futile” surgeries in intrahepatic cholangiocarcinoma patients, utilizing ten preoperative factors. In a study of 827 cases, 378 (45.7%) experienced insufficient surgical outcomes, with notable rates of recurrence (78.6%) and mortality (21.4%) within 12 months. The model achieved high accuracy, with a training area under the curve of 0.830 and […]