AIMC Topic: Radiography

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A deep learning model based on fusion images of chest radiography and X-ray sponge images supports human visual characteristics of retained surgical items detection.

International journal of computer assisted radiology and surgery
PURPOSE: Although a novel deep learning software was proposed using post-processed images obtained by the fusion between X-ray images of normal post-operative radiography and surgical sponge, the association of the retained surgical item detectabilit...

Detection of pulpal calcifications on bite-wing radiographs using deep learning.

Clinical oral investigations
OBJECTIVES: Pulpal calcifications are discrete hard calcified masses of varying sizes in the dental pulp cavity. This study is aimed at measuring the performance of the YOLOv4 deep learning algorithm to automatically determine whether there is calcif...

Machine Learning Model for Chest Radiographs: Using Local Data to Enhance Performance.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
PURPOSE: To develop and assess the performance of a machine learning model which screens chest radiographs for 14 labels, and to determine whether fine-tuning the model on local data improves its performance. Generalizability at different institution...

Deep Learning for Breast MRI Style Transfer with Limited Training Data.

Journal of digital imaging
In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data. This is made possible by training our model on unlimited possibilities of sim...

Detecting Distal Radius Fractures Using a Segmentation-Based Deep Learning Model.

Journal of digital imaging
Deep learning algorithms can be used to classify medical images. In distal radius fracture treatment, fracture detection and radiographic assessment of fracture displacement are critical steps. The aim of this study was to use pixel-level annotations...

Can artificial intelligence pass the Fellowship of the Royal College of Radiologists examination? Multi-reader diagnostic accuracy study.

BMJ (Clinical research ed.)
OBJECTIVE: To determine whether an artificial intelligence candidate could pass the rapid (radiographic) reporting component of the Fellowship of the Royal College of Radiologists (FRCR) examination.

Knowledge, perceptions, and expectations of Artificial intelligence in radiography practice: A global radiography workforce survey.

Journal of medical imaging and radiation sciences
BACKGROUND: Artificial Intelligence (AI) technologies have already started impacting clinical practice across various settings worldwide, including the radiography profession. This study is aimed at exploring a world-wide view on AI technologies in r...

Transparency in Artificial Intelligence Research: a Systematic Review of Availability Items Related to Open Science in Radiology and Nuclear Medicine.

Academic radiology
RATIONALE AND OBJECTIVES: Reproducibility of artificial intelligence (AI) research has become a growing concern. One of the fundamental reasons is the lack of transparency in data, code, and model. In this work, we aimed to systematically review the ...

Automatic assessment of knee osteoarthritis severity in portable devices based on deep learning.

Journal of orthopaedic surgery and research
BACKGROUND: For knee osteoarthritis, the commonly used radiology severity criteria Kellgren-Lawrence lead to variability among surgeons. Most existing diagnosis models require preprocessed radiographs and specific equipment.