July 23rd 2024
For women with dense breasts, the combination of mammography and supplemental breast ultrasound had a 36.4 percent higher sensitivity rate for detecting breast cancer in comparison to the combination of mammography and adjunctive AI, according to a new study.
FDA Clears AI-Powered Digital Cytology Platform for Cervical Cancer
February 2nd 2024Combining advanced volumetric imaging technology with deep learning, the Genius Digital Diagnostics System has reportedly shown a 28 percent reduction in false negatives for high-grade squamous intraepithelial lesions and other severe lesions in comparison to microscopic review.
Study: Deep Learning Reconstruction Reduces Effective Dosing for CT Colonography by 83 Percent
January 31st 2024Researchers found the use of deep learning reconstruction for computed tomography colonography (CTC) led to 25 percent higher subjective image quality scoring than iterative image reconstruction at a substantially reduced rate of radiation dosing.
3D Hip CT AI Model Bolsters Future Fracture Risk Assessment for Patients with Hip Fractures
January 30th 2024Employing reconstructed radiographs from 3D hip CT scans, a deep learning model demonstrated a higher concordance index (C index) and higher two- and three-year AUCs than multiple imaging models and three clinical models for predicting subsequent fracture risk in patients with hip fractures.
Key Takeaways from Multiple Radiology Societies on AI Assessment and Integration
January 22nd 2024In a recently issued statement from multiple radiology societies including the RSNA and ACR, researchers offer practical advice for evaluating artificial intelligence (AI) tools, implementing AI into current workflows and monitoring of the technology to help ensure optimal benefit and effectiveness.
FDA Clears Updated AI Software for Lung CT
January 22nd 2024Reportedly offering improved delineation of pulmonary structures and greater accuracy with computed tomography (CT) values of pulmonary tissue, the artificial intelligence (AI)-powered LungQ 3.0.0. may facilitate enhanced precision and efficiency with interventional procedures such as lung volume reduction and ablation procedures.
FDA Clears AI-Powered CT Assessment Tool for Lung Fibrosis
January 18th 2024For patients with suspected interstitial lung disease, the digital biomarker solution Fibresolve offers machine learning capability of diagnosing idiopathic pulmonary fibrosis (IPF) based on assessment of lung computed tomography (CT) scans.
FDA Clears AI Software for Enhanced Lung Nodule Detection on Chest X-Rays
January 9th 2024Tailored for incidental findings on chest radiographs, the qXR for Lung Nodule (qXR-LN) software utilizes artificial intelligence (AI) to help detect suspected pulmonary nodules ranging between 6 to 30 mm.
Hybrid MRI Deep Learning Model Shows Promise in Predicting Tumor Deposits with Rectal Cancer
January 3rd 2024An emerging deep learning model, which incorporates T2-weighted MRI and clinical data, demonstrated an 83.9 percent AUC and an 85 percent specificity rate for preoperative prediction of tumor deposits in patients with rectal cancer.
Can ChatGPT and Bard Bolster Decision-Making for Cancer Screening in Radiology?
December 21st 2023In a study examining the potential of the large language models ChatGPT-4 and Bard to follow ACR Appropriateness Criteria for breast cancer, lung cancer, ovarian cancer and colorectal cancer screening, researchers noted “impressive accuracy in making radiologic clinical decisions.”
AI Facilitates Nearly 83 Percent Improvement in Turnaround Time for Fracture X-Rays
December 19th 2023In addition to offering a 98.5 percent sensitivity rate in diagnosing fractures on X-ray, an emerging artificial intelligence (AI) software reportedly helped reduce mean turnaround time on X-ray fracture diagnosis from 48 hours to 8.3 hours, according to new research presented at the Radiological Society of North America (RSNA) conference.
Study: AI Enhances Abnormality Detection on CXR Across Radiologist Experience Levels
December 13th 2023Emerging research suggests that adjunctive artificial intelligence (AI) improves sensitivity for a variety of abnormalities on chest X-rays regardless of radiologist experience level, including an average 26 percent increase in sensitivity for pneumothorax.