AIMC Topic: Radiography

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Transfer-learning is a key ingredient to fast deep learning-based 4D liver MRI reconstruction.

Scientific reports Jul 11, 2023
Time-resolved volumetric magnetic resonance imaging (4D MRI) could be used to address organ motion in image-guided interventions like tumor ablation. Current 4D reconstruction techniques are unsuitable for most interventional settings because they ar...
Radiography Radionuclide Imaging Humans Pharmaceutical Vehicles Magnetic Resonance Imaging Liver Deep Learning
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Deep learning-based age estimation from chest CT scans.

International journal of computer assisted radiology and surgery Jul 7, 2023
PURPOSE: Medical imaging can be used to estimate a patient's biological age, which may provide complementary information to clinicians compared to chronological age. In this study, we aimed to develop a method to estimate a patient's age based on the...
Lung Lung Neoplasms Humans Deep Learning Tomography, X-Ray Computed Radiography
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Harnessing the Potential of Artificial Intelligence for Quality Assurance in Radiology Practice.

Journal of the American College of Radiology : JACR Jul 7, 2023
Radiologists Radiography Radiology Artificial Intelligence
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Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs.

Nature communications Jul 7, 2023
Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by ...
Humans Diabetes Mellitus, Type 2 Radiography Prospective Studies Deep Learning Radiography, Thoracic
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Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review.

Journal of medical Internet research Jul 3, 2023
BACKGROUND: Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. The conventional experts rea...
COVID-19 Reproducibility of Results Tuberculosis Humans X-Rays Artificial Intelligence Deep Learning Radiography
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Detection of incomplete atypical femoral fracture on anteroposterior radiographs via explainable artificial intelligence.

Scientific reports Jun 27, 2023
One of the key aspects of the diagnosis and treatment of atypical femoral fractures is the early detection of incomplete fractures and the prevention of their progression to complete fractures. However, an incomplete atypical femoral fracture can be ...
Femoral Fractures Humans Area Under Curve Artificial Intelligence Radiography
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Initial study on an expert system for spine diseases screening using inertial measurement unit.

Scientific reports Jun 27, 2023
In recent times, widely understood spine diseases have advanced to one of the most urgetn problems where quick diagnosis and treatment are needed. To diagnose its specifics (e.g. to decide whether this is a scoliosis or sagittal imbalance) and assess...
Magnetic Resonance Imaging Radiography Humans Scoliosis X-Rays Spine Expert Systems
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Improving Medical Imaging Order Entry With Artificial Intelligence Tools: Insights and Action Items.

Journal of the American College of Radiology : JACR Jun 26, 2023
Artificial Intelligence Radiography Diagnostic Imaging
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Multi-modal deep learning for automated assembly of periapical radiographs.

Journal of dentistry Jun 21, 2023
OBJECTIVES: Periapical radiographs are oftentimes taken in series to display all teeth present in the oral cavity. Our aim was to automatically assemble such a series of periapical radiographs into an anatomically correct status using a multi-modal d...
Radiography Mouth Diagnosis, Oral Humans Neural Networks, Computer Deep Learning
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Accuracy and clinical validity of automated cephalometric analysis using convolutional neural networks.

Orthodontics & craniofacial research Jun 16, 2023
BACKGROUND: This study aimed to assess the error range of cephalometric measurements based on the landmarks detected using cascaded CNNs and determine how horizontal and vertical positional errors of individual landmarks affect lateral cephalometric ...
Radiography Humans Young Adult Cephalometry Adult Face Neural Networks, Computer Reproducibility of Results
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