AIMC Topic: Radiography

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Deep learning assistance for tuberculosis diagnosis with chest radiography in low-resource settings.

Journal of X-ray science and technology
Tuberculosis (TB) is a major health issue with high mortality rates worldwide. Recently, tremendous researches of artificial intelligence (AI) have been conducted targeting at TB to reduce the diagnostic burden. However, most researches are conducted...

[Artificial intelligence in oncological radiology : A (p)review].

Der Radiologe
BACKGROUND: Artificial intelligence (AI) has the potential to fundamentally change medicine within the coming decades. Radiological imaging is one of the primary fields of its clinical application.

Validation of cervical vertebral maturation stages: Artificial intelligence vs human observer visual analysis.

American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
INTRODUCTION: This study aimed to develop an artificial neural network (ANN) model for cervical vertebral maturation (CVM) analysis and validate the model's output with the results of human observers.

How much deep learning is enough for automatic identification to be reliable?

The Angle orthodontist
OBJECTIVES: To determine the optimal quantity of learning data needed to develop artificial intelligence (AI) that can automatically identify cephalometric landmarks.

Clinician and computer: a study on patient perceptions of artificial intelligence in skeletal radiography.

BMJ health & care informatics
BACKGROUND: Up to half of all musculoskeletal injuries are investigated with plain radiographs. However, high rates of image interpretation error mean that novel solutions such as artificial intelligence (AI) are being explored.

Current Clinical Applications of Artificial Intelligence in Radiology and Their Best Supporting Evidence.

Journal of the American College of Radiology : JACR
PURPOSE: Despite tremendous gains from deep learning and the promise of artificial intelligence (AI) in medicine to improve diagnosis and save costs, there exists a large translational gap to implement and use AI products in real-world clinical situa...

Workflow Applications of Artificial Intelligence in Radiology and an Overview of Available Tools.

Journal of the American College of Radiology : JACR
In the past decade, there has been tremendous interest in applying artificial intelligence (AI) to improve the field of radiology. Currently, numerous AI applications are in development, with potential benefits spanning all steps of the imaging chain...

Is Deep Learning On Par with Human Observers for Detection of Radiographically Visible and Occult Fractures of the Scaphoid?

Clinical orthopaedics and related research
BACKGROUND: Preliminary experience suggests that deep learning algorithms are nearly as good as humans in detecting common, displaced, and relatively obvious fractures (such as, distal radius or hip fractures). However, it is not known whether this a...

Advanced Deep Learning Techniques Applied to Automated Femoral Neck Fracture Detection and Classification.

Journal of digital imaging
To use deep learning with advanced data augmentation to accurately diagnose and classify femoral neck fractures. A retrospective study of patients with femoral neck fractures was performed. One thousand sixty-three AP hip radiographs were obtained fr...

Artificial Intelligence and Machine Learning in Radiology: Current State and Considerations for Routine Clinical Implementation.

Investigative radiology
Although artificial intelligence (AI) has been a focus of medical research for decades, in the last decade, the field of radiology has seen tremendous innovation and also public focus due to development and application of machine-learning techniques ...