AIMC Topic: Radiography

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[Promoting the application of federated learning in medical imaging artificial intelligence].

Zhonghua yi xue za zhi
Medical image-based artificial intelligence (AI) systems have shown great potential in assisting disease diagnosis and treatment. However, the challenges, such as data silos, privacy security and standardization, seriously impedes the application of ...

Estimation of patient's angle from skull radiographs using deep learning.

Journal of X-ray science and technology
BACKGROUND: Skull radiography, an assessment method for initial diagnosis and post-operative follow-up, requires substantial retaking of various types of radiographs. During retaking, a radiologic technologist estimates a patient's rotation angle fro...

Artificial Intelligence in Radiology: an introduction to the most important concepts.

Radiologia
The interpretation of medical imaging tests is one of the main tasks that radiologists do. For years, it has been a challenge to teach computers to do this kind of cognitive task; the main objective of the field of computer vision is to overcome this...

Optimized chest X-ray image semantic segmentation networks for COVID-19 early detection.

Journal of X-ray science and technology
BACKGROUND: Although detection of COVID-19 from chest X-ray radiography (CXR) images is faster than PCR sputum testing, the accuracy of detecting COVID-19 from CXR images is lacking in the existing deep learning models.

Use of deep learning methods for hand fracture detection from plain hand radiographs.

Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES
BACKGROUND: Patients with hand trauma are usually examined in emergency departments of hospitals. Hand fractures are frequently observed in patients with hand trauma. Here, we aim to develop a computer-aided diagnosis (CAD) method to assist physician...

[Evaluation of Radiograph Accuracy in Skull X-ray Images Using Deep Learning].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: Accurate positioning is essential for radiography, and it is especially important to maintain image reproducibility in follow-up observations. The decision on re-taking radiographs is entrusting to the individual radiological technologist. T...

Demographic Reporting in Publicly Available Chest Radiograph Data Sets: Opportunities for Mitigating Sex and Racial Disparities in Deep Learning Models.

Journal of the American College of Radiology : JACR
OBJECTIVE: Data sets with demographic imbalances can introduce bias in deep learning models and potentially amplify existing health disparities. We evaluated the reporting of demographics and potential biases in publicly available chest radiograph (C...

Detecting Racial/Ethnic Health Disparities Using Deep Learning From Frontal Chest Radiography.

Journal of the American College of Radiology : JACR
PURPOSE: The aim of this study was to assess racial/ethnic and socioeconomic disparities in the difference between atherosclerotic vascular disease prevalence measured by a multitask convolutional neural network (CNN) deep learning model using fronta...

Distinguishing Intramedullary Spinal Cord Neoplasms from Non-Neoplastic Conditions by Analyzing the Classic Signs on MRI in the Era of AI.

Current medical imaging
Intramedullary lesions can be challenging to diagnose, given the wide range of possible pathologies. Each lesion has unique clinical and imaging features, which are best evaluated using magnetic resonance imaging. Radiological imaging is unique with ...