AIMC Topic: Deep Learning

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Development and validation of an offline deep learning algorithm to detect vitreoretinal abnormalities on ocular ultrasound.

Indian journal of ophthalmology
PURPOSE: We describe our offline deep learning algorithm (DLA) and validation of its diagnostic ability to identify vitreoretinal abnormalities (VRA) on ocular ultrasound (OUS).

Utilizing human intelligence in artificial intelligence for detecting glaucomatous fundus images using human-in-the-loop machine learning.

Indian journal of ophthalmology
PURPOSE: For diagnosing glaucomatous damage, we have employed a novel convolutional neural network (CNN) from TrueColor confocal fundus images to conquer the black box dilemma in artificial intelligence (AI). This neural network with CNN architecture...

Detecting Glaucoma in the Ocular Hypertension Study Using Deep Learning.

JAMA ophthalmology
IMPORTANCE: Automated deep learning (DL) analyses of fundus photographs potentially can reduce the cost and improve the efficiency of reading center assessment of end points in clinical trials.

High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy With Cardiovascular Deep Learning.

JAMA cardiology
IMPORTANCE: Early detection and characterization of increased left ventricular (LV) wall thickness can markedly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating ...

Radiomics and deep learning approach to the differential diagnosis of parotid gland tumors.

Current opinion in otolaryngology & head and neck surgery
PURPOSE OF REVIEW: Advances in computer technology and growing expectations from computer-aided systems have led to the evolution of artificial intelligence into subsets, such as deep learning and radiomics, and the use of these systems is revolution...

Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging.

Neuro-oncology
BACKGROUND: Diagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-codeletion status. Because genetic testing requires an invasive process, an alternative noninvasive appr...

Development and Validation of an Image-based Deep Learning Algorithm for Detection of Synchronous Peritoneal Carcinomatosis in Colorectal Cancer.

Annals of surgery
OBJECTIVE: The aim of this study was to build a SVM classifier using ResNet-3D algorithm by artificial intelligence for prediction of synchronous PC.

Utilizing Deep Learning to Analyze Whole Slide Images of Colonic Biopsies for Associations Between Eosinophil Density and Clinicopathologic Features in Active Ulcerative Colitis.

Inflammatory bowel diseases
BACKGROUND: Eosinophils have been implicated in the pathogenesis of ulcerative colitis and have been associated with disease course and therapeutic response. However, associations between eosinophil density, histologic activity, and clinical features...