OBJECTIVES: To compare diagnostic accuracy of a deep learning artificial intelligence (AI) for cervical spine (C-spine) fracture detection on CT to attending radiologists and assess which undetected fractures were injuries in need of stabilising ther...
RATIONALE AND OBJECTIVES: This study aimed to determine the feasibility of using the deep learning (DL) method to determine the degree (whether myometrial invasion [MI] >50%) of MI in patients with endometrial cancer (EC) based on ultrasound (US) ima...
Diagnostic and interventional radiology (Ankara, Turkey)
Dec 25, 2023
Rapid technological advances have transformed medical education, particularly in radiology, which depends on advanced imaging and visual data. Traditional electronic learning (e-learning) platforms have long served as a cornerstone in radiology educa...
OBJECTIVES: Artificial intelligence (AI) is expected to alleviate the negative consequences of rising case numbers for radiologists. Currently, systematic evaluations of the impact of AI solutions in real-world radiological practice are missing. Our ...
PURPOSE: High volumes of chest radiographs (CXR) remain uninterpreted due to severe shortage of radiologists. These CXRs may be informally reported by non-radiologist physicians, or not reviewed at all. Artificial intelligence (AI) software can aid l...
RATIONALE AND OBJECTIVES: To evaluate the standalone performance of a deep learning (DL) based fracture detection tool on extremity radiographs and assess the performance of radiologists and emergency physicians in identifying fractures of the extrem...
OBJECTIVES: To validate an AI system for standalone breast cancer detection on an entire screening population in comparison to first-reading breast radiologists.
OBJECTIVES: To develop and externally validate multiphase CT-based deep learning (DL) models for differentiating adrenal metastases from benign lesions.
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