AIMC Journal:
Radiology
Showing 311 to 320 of 374 articles
Patient Characteristics Impact Performance of AI Algorithm in Interpreting Negative Screening Digital Breast Tomosynthesis Studies.
Radiology
May 1, 2024
Background Artificial intelligence (AI) is increasingly used to manage radiologists' workloads. The impact of patient characteristics on AI performance has not been well studied. Purpose To understand the impact of patient characteristics (race and e...
Deep Learning Assessment of Small Renal Masses at Contrast-enhanced Multiphase CT.
Radiology
May 1, 2024
Background Accurate characterization of suspicious small renal masses is crucial for optimized management. Deep learning (DL) algorithms may assist with this effort. Purpose To develop and validate a DL algorithm for identifying benign small renal ma...
Enhancing AI-assisted Interpretation of Chest Radiographs: A Critical Analysis of Methods and Applicability.
Radiology
May 1, 2024
Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI.
Radiology
May 1, 2024
Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models...
Lower Extremity Growth according to AI Automated Femorotibial Length Measurement on Slot-Scanning Radiographs in Pediatric Patients.
Radiology
Apr 1, 2024
Background Commonly used pediatric lower extremity growth standards are based on small, dated data sets. Artificial intelligence (AI) enables creation of updated growth standards. Purpose To train an AI model using standing slot-scanning radiographs ...
B-mode US and Deep Learning Rivals Shear-Wave Elastography in Screening for Fibrosis.
Radiology
Apr 1, 2024
Predicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification Models.
Radiology
Apr 1, 2024
Background Preoperative discrimination of preinvasive, minimally invasive, and invasive adenocarcinoma at CT informs clinical management decisions but may be challenging for classifying pure ground-glass nodules (pGGNs). Deep learning (DL) may improv...
Deep Learning to Differentiate Benign and Malignant Vertebral Fractures at Multidetector CT.
Radiology
Mar 1, 2024
Background Differentiating between benign and malignant vertebral fractures poses diagnostic challenges. Purpose To investigate the reliability of CT-based deep learning models to differentiate between benign and malignant vertebral fractures. Materi...
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