AIMC Topic: Radiography
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Utility of accelerated T2-weighted turbo spin-echo imaging with deep learning reconstruction in female pelvic MRI: a multi-reader study.
European radiology
Jun 14, 2023
OBJECTIVES: To determine the clinical feasibility of T2-weighted turbo spin-echo (T2-TSE) imaging with deep learning reconstruction (DLR) in female pelvic MRI compared with conventional T2 TSE in terms of image quality and scan time.
Patient Identification Based on Deep Metric Learning for Preventing Human Errors in Follow-up X-Ray Examinations.
Journal of digital imaging
Jun 12, 2023
Biological fingerprints extracted from clinical images can be used for patient identity verification to determine misfiled clinical images in picture archiving and communication systems. However, such methods have not been incorporated into clinical ...
Evaluation of automated detection of head position on lateral cephalometric radiographs based on deep learning techniques.
Annals of anatomy = Anatomischer Anzeiger : official organ of the Anatomische Gesellschaft
Jun 9, 2023
BACKGROUND: Lateral cephalometric radiograph (LCR) is crucial to diagnosis and treatment planning of maxillofacial diseases, but inappropriate head position, which reduces the accuracy of cephalometric measurements, can be challenging to detect for c...
Measurement of interspinous motion in dynamic cervical radiographs using a deep learning-based segmentation model.
Journal of neurosurgery. Spine
Jun 9, 2023
OBJECTIVE: Interspinous motion (ISM) is a representative method for evaluating the functional fusion status following anterior cervical discectomy and fusion (ACDF) surgery, but the associated measuring difficulty and potential errors in the clinical...
How do patients perceive the AI-radiologists interaction? Results of a survey on 2119 responders.
European journal of radiology
Jun 8, 2023
PURPOSE: In this study we investigate how patients perceive the interaction between artificial intelligence (AI) and radiologists by designing a survey.
AI vs FRCR: What it means for the future.
European journal of radiology
Jun 8, 2023
A recent work by Shelmerdine et al. was published in the Christmas edition of the BMJ. The authors were inspired by George Hinton's statement that artificial intelligence (AI) would supersede radiologists, and ventured to investigate whether the AI s...
A deep learning approach for radiological detection and classification of radicular cysts and periapical granulomas.
Journal of dentistry
Jun 7, 2023
OBJECTIVES: Dentists and oral surgeons often face difficulties distinguishing between radicular cysts and periapical granulomas on panoramic imaging. Radicular cysts require surgical removal while root canal treatment is the first-line treatment for ...
VLTENet: A Deep-Learning-Based Vertebra Localization and Tilt Estimation Network for Automatic Cobb Angle Estimation.
IEEE journal of biomedical and health informatics
Jun 5, 2023
Scoliosis diagnosis and assessment rely upon Cobb angle estimation from X-ray images of the spine. Recently, automated scoliosis assessment has been greatly improved using deep learning methods. However, in such methods, the Cobb angle is usually pre...
Pneumonia detection with QCSA network on chest X-ray.
Scientific reports
Jun 3, 2023
Worldwide, pneumonia is the leading cause of infant mortality. Experienced radiologists use chest X-rays to diagnose pneumonia and other respiratory diseases. The diagnostic procedure's complexity causes radiologists to disagree with the decision. Ea...
Can incorrect artificial intelligence (AI) results impact radiologists, and if so, what can we do about it? A multi-reader pilot study of lung cancer detection with chest radiography.
European radiology
Jun 2, 2023
OBJECTIVE: To examine whether incorrect AI results impact radiologist performance, and if so, whether human factors can be optimized to reduce error.
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