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Prospective Studies

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Evaluating Automated Tools for Lesion Detection on F Fluoroestradiol PET/CT Images and Assessment of Concordance with Standard-of-Care Imaging in Metastatic Breast Cancer.

Radiology. Imaging cancer
Purpose To evaluate two automated tools for detecting lesions on fluorine 18 (F) fluoroestradiol (FES) PET/CT images and assess concordance of F-FES PET/CT with standard diagnostic CT and/or F fluorodeoxyglucose (FDG) PET/CT in patients with breast c...

Critical view of safety assessment in sentinel node dissection for endometrial and cervical cancer: artificial intelligence to enhance surgical safety and lymph node detection (LYSE study).

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society
OBJECTIVE: This study aims to evaluate the feasibility of video-based assessment rate of Critical Views of Safety criteria for sentinel lymph node dissection in endometrial and cervical cancer. Goal of these Critical Views of Safety is to help standa...

Patient Perception of Artificial Intelligence Use in Interpretation of Screening Mammograms: A Survey Study.

Radiology. Imaging cancer
Purpose To assess patient perceptions of artificial intelligence (AI) use in the interpretation of screening mammograms. Materials and Methods In a prospective, institutional review board-approved study, all patients undergoing mammography screening ...

Using Machine Learning to Predict Cognitive Decline in Older Adults From the Chinese Longitudinal Healthy Longevity Survey: Model Development and Validation Study.

JMIR aging
BACKGROUND: Cognitive impairment, indicative of Alzheimer disease and other forms of dementia, significantly deteriorates the quality of life of older adult populations and imposes considerable burdens on families and health care systems worldwide. T...

AI-generated questions for urological competency assessment: a prospective educational study.

BMC medical education
BACKGROUND: The integration of artificial intelligence (AI) in medical education assessment remains largely unexplored, particularly in specialty-specific evaluations during clinical rotations. Traditional question development methods are time-intens...

Deep Learning-Driven Abbreviated Shoulder MRI Protocols: Diagnostic Accuracy in Clinical Practice.

Tomography (Ann Arbor, Mich.)
BACKGROUND: Deep learning (DL) reconstruction techniques have shown promise in reducing MRI acquisition times while maintaining image quality. However, the impact of different acceleration factors on diagnostic accuracy in shoulder MRI remains unexpl...

Machine learning-based analysis on factors influencing blood heavy metal concentrations in the Korean CHildren's ENvironmental health Study (Ko-CHENS).

The Science of the total environment
Heavy metal concentration in pregnant women affects neurocognitive and behavioral development of their infants and children. The majority of existing research focusing on pregnant women's heavy metal concentration has considered individual environmen...

Artificial intelligence-aided optical biopsy improves the diagnosis of esophageal squamous neoplasm.

World journal of gastroenterology
BACKGROUND: Early detection of esophageal squamous neoplasms (ESN) is essential for improving patient prognosis. Optical diagnosis of ESN remains challenging. Probe-based confocal laser endomicroscopy (pCLE) enables accurate histological observation...

Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma.

Scientific reports
Post-hepatectomy liver failure (PHLF) is a severe complication following liver surgery. We aimed to develop a novel, interpretable machine learning (ML) model to predict PHLF. We enrolled 312 hepatocellular carcinoma (HCC) patients who underwent hepa...