AIMC Topic: Prospective Studies

Clear Filters Showing 2151 to 2160 of 2510 articles

Combination of white-light imaging-based and narrow-band imaging-based artificial intelligence models during colonoscopy in patients with ulcerative colitis.

Journal of Crohn's & colitis
BACKGROUND AND AIMS: The long-term treat-to-target (T2T) approach in ulcerative colitis (UC) aims for endoscopic remission, but variability among endoscopists and a lack of precision in relapse prediction both limit its clinical usefulness. A recentl...

Artificial Intelligence-assisted Video Colonoscopy for Disease Monitoring of Ulcerative Colitis: A Prospective Study.

Journal of Crohn's & colitis
BACKGROUNDS AND AIMS: The Mayo endoscopic subscore [MES] is the most popular endoscopic disease activity measure of ulcerative colitis [UC]. Artificial intelligence [AI]-assisted colonoscopy is expected to reduce diagnostic variability among endoscop...

Automated Breast Density Assessment for Full-Field Digital Mammography and Digital Breast Tomosynthesis.

Cancer prevention research (Philadelphia, Pa.)
Mammographic density is a strong risk factor for breast cancer and is reported clinically as part of Breast Imaging Reporting and Data System (BI-RADS) results issued by radiologists. Automated assessment of density is needed that can be used for bot...

Intelligent diagnosis of thyroid nodules with AI ultrasound assistance and cytology classification.

Frontiers in endocrinology
OBJECTIVE: Accurate evaluation of thyroid nodules is crucial for effective management; however, methods such as ultrasonography and Fine Needle Aspiration Cytology (FNAC) can be subjective and operator-dependent. Indeterminate thyroid nodules (ITNs) ...

Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model.

PeerJ
BACKGROUND: Tuberculous pleural effusion (TPE) is a prevalent tuberculosis complication, with diagnosis presenting considerable challenges. Timely and precise identification of TPE is vital for effective patient management and prognosis, yet existing...

Comprehensive Machine Learning-Based Prediction Model for Delirium Risk in Older Patients with Dementia: Risk Factors Identification.

Clinical interventions in aging
BACKGROUND: Delirium superimposed on dementia (DSD) is a severe complication in older adults with dementia, marked by fluctuating cognition, inattention, and altered consciousness. Detection is challenging due to symptom overlap, yet it contributes t...

SMART (artificial intelligence enabled) DROP (diabetic retinopathy outcomes and pathways): Study protocol for diabetic retinopathy management.

PloS one
INTRODUCTION: Delayed diagnosis of diabetic retinopathy (DR) remains a significant challenge, often leading to preventable blindness and visual impairment. Given that physicians are frequently the first point of contact for people with diabetes, ther...

Developing a machine learning algorithm to predict psychotropic drugs-induced weight gain and the effectiveness of anti-obesity drugs in patients with severe mental illness: Protocol for a prospective cohort study.

PloS one
Obesity is a global public health concern, often co-occurring in patients with severe mental illnesses. The impact of psychotropic drugs-induced weight gain is augmenting the disease burden and healthcare expenditure. However, predictors of psychotro...

[Primary angle closure suspects: application of machine learning method for substantiation of close monitoring].

Vestnik oftalmologii
UNLABELLED: One of the priority areas in healthcare is the concept of predictive, preventive and personalized medicine, which is based on an individualized approach to the patient, including before the onset of diseases such as glaucoma.