AIMC Topic: Radiography

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Closing the loop for AI-ready radiology.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin Aug 15, 2023
BACKGROUND: In recent years, AI has made significant advancements in medical diagnosis and prognosis. However, the incorporation of AI into clinical practice is still challenging and under-appreciated. We aim to demonstrate a possible vertical integr...
Algorithms Radiography Artificial Intelligence Humans Radiology Radiologists
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Automated liver segmental volume ratio quantification on non-contrast T1-Vibe Dixon liver MRI using deep learning.

European journal of radiology Aug 14, 2023
PURPOSE: To evaluate the effectiveness of automated liver segmental volume quantification and calculation of the liver segmental volume ratio (LSVR) on a non-contrast T1-vibe Dixon liver MRI sequence using a deep learning segmentation pipeline.
Deep Learning Liver Magnetic Resonance Imaging Radiography Humans Radionuclide Imaging
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Artificial intelligence in diagnosing dens evaginatus on periapical radiography with limited data availability.

Scientific reports Aug 14, 2023
This study aimed to develop an artificial intelligence (AI) model using deep learning techniques to diagnose dens evaginatus (DE) on periapical radiography (PA) and compare its performance with endodontist evaluations. In total, 402 PA images (138 DE...
Bicuspid Humans Tooth Abnormalities Artificial Intelligence Radiography
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Towards reproducible radiomics research: introduction of a database for radiomics studies.

European radiology Aug 12, 2023
OBJECTIVES: To investigate the model-, code-, and data-sharing practices in the current radiomics research landscape and to introduce a radiomics research database.
Reproducibility of Results Radiomics Radiography Artificial Intelligence Retrospective Studies Humans
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Learning from the machine: AI assistance is not an effective learning tool for resident education in chest x-ray interpretation.

European radiology Aug 12, 2023
OBJECTIVES: To assess whether a computer-aided detection (CADe) system could serve as a learning tool for radiology residents in chest X-ray (CXR) interpretation.
Radiography Radiography, Thoracic X-Rays Internship and Residency Humans Artificial Intelligence
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The psc-CVM assessment system: A three-stage type system for CVM assessment based on deep learning.

BMC oral health Aug 12, 2023
BACKGROUND: Many scholars have proven cervical vertebral maturation (CVM) method can predict the growth and development and assist in choosing the best time for treatment. However, assessing CVM is a complex process. The experience and seniority of t...
Humans Deep Learning Radiography Cervical Vertebrae Cephalometry Age Determination by Skeleton
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Detection of the pathological exposure of pulp using an artificial intelligence tool: a multicentric study over periapical radiographs.

BMC oral health Aug 11, 2023
BACKGROUND: Introducing artificial intelligence (AI) into the medical field proved beneficial in automating tasks and streamlining the practitioners' lives. Hence, this study was conducted to design and evaluate an AI tool called Make Sure Caries Det...
ROC Curve Dental Pulp Artificial Intelligence Radiography Humans
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Are the Pilots Onboard? Equipping Radiologists for Clinical Implementation of AI.

Journal of digital imaging Aug 9, 2023
The incorporation of artificial intelligence into radiological clinical workflow is on the verge of being realized. To ensure that these tools are effective, measures must be taken to educate radiologists on tool performance and failure modes. Additi...
Artificial Intelligence Algorithms Humans Radiology Radiologists Radiography
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A semi-supervised learning-based quality evaluation system for digital chest radiographs.

Medical physics Aug 6, 2023
BACKGROUND: Digital radiography is the most commonly utilized medical imaging technique worldwide, and the quality of radiographs plays a crucial role in accurate disease diagnosis. Therefore, evaluating the quality of radiographs is an essential ste...
Supervised Machine Learning Foreign Bodies Humans Deep Learning Radiography Neural Networks, Computer
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Deep learning discrimination of rheumatoid arthritis from osteoarthritis on hand radiography.

Skeletal radiology Aug 2, 2023
PURPOSE: To develop a deep learning model to distinguish rheumatoid arthritis (RA) from osteoarthritis (OA) using hand radiographs and to evaluate the effects of changing pretraining and training parameters on model performance.
Humans Retrospective Studies Radiography Deep Learning Osteoarthritis Arthritis, Rheumatoid
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