AIMC Topic: Deep Learning

Clear Filters Showing 26321 to 26330 of 28423 articles

[Virtual reconstruction and clinical verification of maxillary defect based on deep learning].

Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology
To construct a virtual reconstruction method including midspan maxillary defects and provide clinical reference by training a generative adversarial network (GAN) model. The CT data of middle-aged Han patients with oral diseases who visited the Dep...

A smart, practical, deep learning-based clinical decision support tool for patients in the prostate-specific antigen gray zone: model development and validation.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Despite efforts to improve screening and early detection of prostate cancer (PC), no available biomarker has shown acceptable performance in patients with prostate-specific antigen (PSA) gray zones. We aimed to develop a deep learning-base...

In Reply to Performance of Deep Learning in the Interpretation of Serum Protein Electrophoresis.

Clinical chemistry
We thank He et al. for their comments on our article (1), which gives us the opportunity to clarify some methodological points. 1. Detection of abnormal patterns: mechanics.

Deep Learning vs Traditional Breast Cancer Risk Models to Support Risk-Based Mammography Screening.

Journal of the National Cancer Institute
BACKGROUND: Deep learning breast cancer risk models demonstrate improved accuracy compared with traditional risk models but have not been prospectively tested. We compared the accuracy of a deep learning risk score derived from the patient's prior ma...

Deep learning algorithms for magnetic resonance imaging of inflammatory sacroiliitis in axial spondyloarthritis.

Rheumatology (Oxford, England)
OBJECTIVE: The aim of this study was to develop a deep learning algorithm for detection of active inflammatory sacroiliitis in short tau inversion recovery (STIR) sequence MRI.

Deep Learning-Based Modeling of the Dark Adaptation Curve for Robust Parameter Estimation.

Translational vision science & technology
PURPOSE: This study investigates deep-learning (DL) sequence modeling techniques to reliably fit dark adaptation (DA) curves and estimate their key parameters in patients with age-related macular degeneration (AMD) to improve robustness and curve pre...