AIMC Topic: Follow-Up Studies

Clear Filters Showing 641 to 650 of 788 articles

Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study.

Neuro-oncology
BACKGROUND: Glioblastoma (GBM) is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification.

Personalized surveillance in colorectal cancer: Integrating circulating tumor DNA and artificial intelligence into post-treatment follow-up.

World journal of gastroenterology
Given the growing burden of colorectal cancer (CRC) as a global health challenge, it becomes imperative to focus on strategies that can mitigate its impact. Post-treatment surveillance has emerged as essential for early detection of recurrence, signi...

Machine learning prediction model of prolonged delay to loop ileostomy closure after rectal cancer surgery: a retrospective study.

World journal of surgical oncology
BACKGROUND: Delayed closure of a temporary ileostomy in patients with rectal cancer may cause psychological, physiological, and socioeconomic burdens to patients.

Prognostic value of circadian rhythm-associated genes in breast cancer.

World journal of surgical oncology
OBJECTIVE: Breast cancer (BC) remains the most prevalent malignancy among women. Clinical evidence indicates that genetic variations related to circadian rhythms, as well as the timing of therapeutic interventions, influence the response to radiation...

Artificial intelligence-based automated matching of pulmonary nodules on follow-up chest CT.

European radiology experimental
BACKGROUND: The growing demand for follow-up imaging highlights the need for tools supporting the assessment of pulmonary nodules over time. We evaluated the performance of an artificial intelligence (AI)-based system for automated nodule matching.

Utilizing Machine Learning to Predict Liver Allograft Fibrosis by Leveraging Clinical and Imaging Data.

Clinical transplantation
BACKGROUND AND AIM: Liver transplant (LT) recipients may succumb to graft-related pathologies, contributing to graft fibrosis (GF). Current methods to diagnose GF are limited, ranging from procedural-related complications to low accuracy. With recent...

[Research progress in application of intelligent remote follow-up mode in hip and knee arthroplasty].

Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery
OBJECTIVE: To review the research progress of intelligent remote follow-up modes in the application after hip and knee arthroplasty.

Eye Movement Characteristics for Predicting a Transition to Psychosis: Longitudinal Changes and Implications.

Schizophrenia bulletin
BACKGROUND AND HYPOTHESIS: Substantive inquiry into the predictive power of eye movement (EM) features for clinical high-risk (CHR) conversion and their longitudinal trajectories is currently sparse. This study aimed to investigate the efficiency of ...

Automated proximal coronary artery calcium identification using artificial intelligence: advancing cardiovascular risk assessment.

European heart journal. Cardiovascular Imaging
AIMS: Identification of proximal coronary artery calcium (CAC) may improve prediction of major adverse cardiac events (MACE) beyond the CAC score, particularly in patients with low CAC burden. We investigated whether the proximal CAC can be detected ...

An artificial intelligence model for Lhermitte's sign in patients with pediatric-onset multiple sclerosis: A follow-up study.

Advances in clinical and experimental medicine : official organ Wroclaw Medical University
BACKGROUND: Lhermitte's sign (LS) is an important clinical marker for patients with multiple sclerosis (MS). Research on pediatric-onset MS (POMS) and LS is limited. To date, there has been no research conducted on the clinical and artificial intelli...