AIMC Topic: Follow-Up Studies

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Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine-Learning and CT Features.

Clinical transplantation
INTRODUCTION: Primary graft dysfunction (PGD) is a significant barrier to survival in lung transplant (LTx) recipients. PGD in patients with systemic sclerosis (SSc) remains especially underrepresented in research.

Prediction of post stroke depression with machine learning: A national multicenter cohort study.

Journal of psychiatric research
OBJECTIVE: Post-stroke depression (PSD) is a common psychiatric complication following stroke, with low clinical detection rates and delayed diagnosis. Most existing PSD prediction models suffer from incomplete data inclusion, which limits their clin...

Machine Learning Model for Predicting Pheochromocytomas/Paragangliomas Surgery Difficulty: A Retrospective Cohort Study.

Annals of surgical oncology
OBJECTIVE: We aimed to develop a machine learning (ML) model to preoperatively predict surgical difficulty for pheochromocytomas and paragangliomas (PPGLs) using clinical and radiomic features.

Evaluating natural language processing derived linguistic features associated with current suicidal ideation, past attempts, and future suicidal behavior.

Journal of psychiatric research
BACKGROUND: People with psychosis have a higher suicide risk than the general population. Natural language processing (NLP) has been used to understand communication in psychosis and suicide risk prediction, but not to predict future suicidal behavio...

Derivation and validation of an artificial intelligence-based plaque burden safety cut-off for long-term acute coronary syndrome from coronary computed tomography angiography.

European heart journal. Cardiovascular Imaging
AIMS: Artificial intelligence (AI) has enabled accurate and fast plaque quantification from coronary computed tomography angiography (CCTA). However, AI detects any coronary plaque in up to 97% of patients. To avoid overdiagnosis, a plaque burden saf...

Comparison of Sarcopenia Assessment in Liver Transplant Recipients by Computed Tomography Freehand Region-of-Interest versus an Automated Deep Learning System.

Clinical transplantation
INTRODUCTION: Sarcopenia, or the loss of muscle quality and quantity, has been associated with poor clinical outcomes in liver transplantation such as infection, increased length of stay, and increased patient mortality. Abdominal computed tomography...

Integrating Machine Learning and Dynamic Digital Follow-up for Enhanced Prediction of Postoperative Complications in Bariatric Surgery.

Obesity surgery
BACKGROUND: Traditional risk models, such as POSSUM and OS-MS, have limited accuracy in predicting complications after bariatric surgery. Machine learning (ML) offers new opportunities for personalized risk assessment by incorporating artificial inte...

Machine learning in colorectal polyp surveillance: A paradigm shift in post-endoscopic mucosal resection follow-up.

World journal of gastroenterology
Colorectal cancer remains a major health concern, with colorectal polyps as key precursors. Endoscopic mucosal resection (EMR) is a common treatment, but recurrence rates remain high. Traditional surveillance strategies rely on polyp characteristics ...