AIMC Topic: Radiography
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A deep learning-based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: development/external validation.
European radiology
Oct 13, 2021
OBJECTIVES: Cardiovascular border (CB) analysis is the primary method for detecting and quantifying the severity of cardiovascular disease using posterior-anterior chest radiographs (CXRs). This study aimed to develop and validate a deep learning-bas...
Deep learning for automated detection and numbering of permanent teeth on panoramic images.
Dento maxillo facial radiology
Oct 13, 2021
OBJECTIVE: This study aimed to evaluate an automated detection system to detect and classify permanent teeth on orthopantomogram (OPG) images using convolutional neural networks (CNNs).
Unsupervised Learning with Generative Adversarial Network for Automatic Tire Defect Detection from X-ray Images.
Sensors (Basel, Switzerland)
Oct 12, 2021
Automatic defect detection of tire has become an essential issue in the tire industry. However, it is challenging to inspect the inner structure of tire by surface detection. Therefore, an X-ray image sensor is used for tire defect inspection. At pre...
Application of deep learning neural network in predicting bone mineral density from plain X-ray radiography.
Archives of osteoporosis
Oct 9, 2021
UNLABELLED: DeepDXA is a deep learning model designed to infer bone mineral density data from plain pelvis X-ray, and it can achieve good predicted value for clinical use.
Learn Fine-Grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images.
IEEE journal of biomedical and health informatics
Oct 5, 2021
Automatic and accurate detection of anatomical landmarks is an essential operation in medical image analysis with a multitude of applications. Recent deep learning methods have improved results by directly encoding the appearance of the captured anat...
The Trials and Tribulations of Assembling Large Medical Imaging Datasets for Machine Learning Applications.
Journal of digital imaging
Oct 4, 2021
With vast interest in machine learning applications, more investigators are proposing to assemble large datasets for machine learning applications. We aim to delineate multiple possible roadblocks to exam retrieval that may present themselves and lea...
[Structured reporting and artificial intelligence].
Der Radiologe
Oct 4, 2021
BACKGROUND: There are a multitude of application possibilities of artificial intelligence (AI) and structured reporting (SR) in radiology. The number of scientific publications have continuously increased for many years. There is an extensive portfol...
The reporting quality of natural language processing studies: systematic review of studies of radiology reports.
BMC medical imaging
Oct 2, 2021
BACKGROUND: Automated language analysis of radiology reports using natural language processing (NLP) can provide valuable information on patients' health and disease. With its rapid development, NLP studies should have transparent methodology to allo...
Clinical Validation of a Deep Learning-Based Hybrid (Greulich-Pyle and Modified Tanner-Whitehouse) Method for Bone Age Assessment.
Korean journal of radiology
Oct 1, 2021
OBJECTIVE: To evaluate the accuracy and clinical efficacy of a hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse (TW) artificial intelligence (AI) model for bone age assessment.
Evaluation and Real-World Performance Monitoring of Artificial Intelligence Models in Clinical Practice: Try It, Buy It, Check It.
Journal of the American College of Radiology : JACR
Sep 30, 2021
The pace of regulatory clearance of artificial intelligence (AI) algorithms for radiology continues to accelerate, and numerous algorithms are becoming available for use in clinical practice. End users of AI in radiology should be aware that AI algor...
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