AIMC Topic: Radiography

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Radiomics in Oncology: A Practical Guide.

Radiographics : a review publication of the Radiological Society of North America, Inc
Radiomics refers to the extraction of mineable data from medical imaging and has been applied within oncology to improve diagnosis, prognostication, and clinical decision support, with the goal of delivering precision medicine. The authors provide a ...

Application of machine learning in CT images and X-rays of COVID-19 pneumonia.

Medicine
Coronavirus disease (COVID-19) has spread worldwide. X-ray and computed tomography (CT) are 2 technologies widely used in image acquisition, segmentation, diagnosis, and evaluation. Artificial intelligence can accurately segment infected parts in X-r...

Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms.

The Lancet. Digital health
BACKGROUND: Artificial intelligence (AI) algorithms can be trained to recognise tuberculosis-related abnormalities on chest radiographs. Various AI algorithms are available commercially, yet there is little impartial evidence on how their performance...

Can a Deep-learning Model for the Automated Detection of Vertebral Fractures Approach the Performance Level of Human Subspecialists?

Clinical orthopaedics and related research
BACKGROUND: Vertebral fractures are the most common osteoporotic fractures in older individuals. Recent studies suggest that the performance of artificial intelligence is equal to humans in detecting osteoporotic fractures, such as fractures of the h...

A deep learning approach to dental restoration classification from bitewing and periapical radiographs.

Quintessence international (Berlin, Germany : 1985)
OBJECTIVE: The aim of this study was to examine the success of deep learning-based convolutional neural networks (CNN) in the detection and differentiation of amalgam, composite resin, and metal-ceramic restorations from bitewing and periapical radio...

Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study.

The Lancet. Digital health
BACKGROUND: The tumour stroma microenvironment plays an important part in disease progression and its composition can influence treatment response and outcomes. Histological evaluation of tumour stroma is limited by access to tissue, spatial heteroge...

Performance of SURF and SIFT Keypoints for the Automated Differentiation of Abnormality in Chest Radiographs.

Studies in health technology and informatics
In this work, automated abnormality detection using keypoint information from Speeded-Up Robust feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors in chest Radiographic (CR) images is investigated and compared. Computerized image...

Evaluation of automated cephalometric analysis based on the latest deep learning method.

The Angle orthodontist
OBJECTIVES: To compare an automated cephalometric analysis based on the latest deep learning method of automatically identifying cephalometric landmarks (AI) with previously published AI according to the test style of the worldwide AI challenges at t...