AIMC Topic: Image Interpretation, Computer-Assisted
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Synthetic data in machine learning for medicine and healthcare.
Nature biomedical engineering
Jun 1, 2021
The proliferation of synthetic data in artificial intelligence for medicine and healthcare raises concerns about the vulnerabilities of the software and the challenges of current policy.
Automated Detection of Crohn's Disease Intestinal Strictures on Capsule Endoscopy Images Using Deep Neural Networks.
Journal of Crohn's & colitis
May 4, 2021
BACKGROUND AND AIMS: Passable intestinal strictures are frequently detected on capsule endoscopy [CE]. Such strictures are a major component of inflammatory scores. Deep neural network technology for CE is emerging. However, the ability of deep neura...
Current trends in the use of machine learning for diagnostics and/or risk stratification in cardiovascular disease.
Cardiovascular research
Apr 23, 2021
Artificial Intelligence Improves the Accuracy in Histologic Classification of Breast Lesions.
American journal of clinical pathology
Mar 15, 2021
OBJECTIVES: This study evaluated the usefulness of artificial intelligence (AI) algorithms as tools in improving the accuracy of histologic classification of breast tissue.
Detection of glaucoma using retinal fundus images: A comprehensive review.
Mathematical biosciences and engineering : MBE
Mar 2, 2021
Content-based image analysis and computer vision techniques are used in various health-care systems to detect the diseases. The abnormalities in a human eye are detected through fundus images captured through a fundus camera. Among eye diseases, glau...
The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World.
American journal of clinical dermatology
Mar 1, 2021
Artificial intelligence (AI) algorithms have been shown to diagnose skin lesions with impressive accuracy in experimental settings. The majority of the literature to date has compared AI and dermatologists as opponents in skin cancer diagnosis. Howev...
Unenhanced CT texture analysis with machine learning for differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma.
Nagoya journal of medical science
Feb 1, 2021
Differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma (ML) remains challenging on cross-sectional images. The aim of this study is to investigate the usefulness of texture features on unenhanced CT for differentiating be...
Deep Learning Detection of Sea Fan Neovascularization From Ultra-Widefield Color Fundus Photographs of Patients With Sickle Cell Hemoglobinopathy.
JAMA ophthalmology
Feb 1, 2021
IMPORTANCE: Adherence to screening for vision-threatening proliferative sickle cell retinopathy is limited among patients with sickle cell hemoglobinopathy despite guidelines recommending dilated fundus examinations beginning in childhood. An automat...
Retinal Neovascularization
Retinal Vessels
Pattern Recognition, Automated
Fluorescein Angiography
Photography
Cross-Sectional Studies
Male
Middle Aged
Deep Learning
Predictive Value of Tests
Anemia, Sickle Cell
Image Interpretation, Computer-Assisted
Observer Variation
Female
Reproducibility of Results
Young Adult
Adult
Humans
Retrospective Studies
Development of rapid and highly accurate method to measure concentration of fibers in atmosphere using artificial intelligence and scanning electron microscopy.
Journal of occupational health
Jan 1, 2021
AIM: We aimed to develop a measurement method that can count fibers rapidly by scanning electron microscopy equipped with an artificial intelligence image recognition system (AI-SEM), detecting thin fibers which cannot be observed by a conventional p...
Differentiating Small-Cell Lung Cancer From Non-Small-Cell Lung Cancer Brain Metastases Based on MRI Using Efficientnet and Transfer Learning Approach.
Technology in cancer research & treatment
Jan 1, 2021
Differentiation between small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC) brain metastases is crucial due to the different clinical behaviors of the two tumor types. We propose the use of a deep learning and transfer learning appr...
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