AIMC Journal:
Radiology
Showing 351 to 360 of 374 articles
Radiologist's Guide to Evaluating Publications of Clinical Research on AI: How We Do It.
Radiology
Sep 1, 2023
Literacy in research studies of artificial intelligence (AI) has become an important skill for radiologists. It is required to make a proper assessment of the validity, reproducibility, and clinical applicability of AI studies. However, AI studies ar...
Commercially Available Chest Radiograph AI Tools for Detecting Airspace Disease, Pneumothorax, and Pleural Effusion.
Radiology
Sep 1, 2023
Background Commercially available artificial intelligence (AI) tools can assist radiologists in interpreting chest radiographs, but their real-life diagnostic accuracy remains unclear. Purpose To evaluate the diagnostic accuracy of four commercially ...
Comparison of the Diagnostic Accuracy of Mammogram-based Deep Learning and Traditional Breast Cancer Risk Models in Patients Who Underwent Supplemental Screening with MRI.
Radiology
Sep 1, 2023
Background Access to supplemental screening breast MRI is determined using traditional risk models, which are limited by modest predictive accuracy. Purpose To compare the diagnostic accuracy of a mammogram-based deep learning (DL) risk assessment mo...
Mammography-based Deep Learning for Breast Cancer Risk Assessment for Supplemental MRI Screening.
Radiology
Sep 1, 2023
Dual-Energy CT Deep Learning Radiomics to Predict Macrotrabecular-Massive Hepatocellular Carcinoma.
Radiology
Aug 1, 2023
Background It is unknown whether the additional information provided by multiparametric dual-energy CT (DECT) could improve the noninvasive diagnosis of the aggressive macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC). Purpose T...
Musculoskeletal CT Imaging: State-of-the-Art Advancements and Future Directions.
Radiology
Aug 1, 2023
CT is one of the most widely used modalities for musculoskeletal imaging. Recent advancements in the field include the introduction of four-dimensional CT, which captures a CT image during motion; cone-beam CT, which uses flat-panel detectors to capt...
Prior CT Improves Deep Learning for Malignancy Risk Estimation of Screening-detected Pulmonary Nodules.
Radiology
Aug 1, 2023
Background Prior chest CT provides valuable temporal information (eg, changes in nodule size or appearance) to accurately estimate malignancy risk. Purpose To develop a deep learning (DL) algorithm that uses a current and prior low-dose CT examinatio...
If Only We Had a Time Machine: Prior CT in Deep Learning for Lung Nodule Prognostication.
Radiology
Aug 1, 2023
Quantifying Uncertainty in Deep Learning of Radiologic Images.
Radiology
Aug 1, 2023
In recent years, deep learning (DL) has shown impressive performance in radiologic image analysis. However, for a DL model to be useful in a real-world setting, its confidence in a prediction must also be known. Each DL model's output has an estimate...
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