Women's Health CT

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Photon-counting CT-optimized features with the OmniTom Elite system include 30 cm field of view scanning, continuous spiral scanning, and an ultra-high-resolution capability of 0.141 mm resolution.

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Incorporating age, lesion shape and effective atomic number in the venous phase of dual-energy CT (DECT), an emerging model demonstrated a 79.1 percent AUC for differentiating between malignant and benign breast lesions.

In newly issued proposals addressing changes to coverage for Medicare services in 2025, the Centers for Medicare and Medicaid Services (CMS) announced its intent to provide coverage of computed tomography colonography (CTC) for Medicare beneficiaries in 2025.

Photon-counting CT enables enhanced fissure visualization and a lower degree of cardiac motion artifacts for lung perfusion imaging at a significantly reduced scan time in contrast to dual-energy CT, according to new research findings.

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Catch up on the top AI-related news and research in radiology over the past month.

Proprietary technologies with the FCT iStream computed tomography system reportedly facilitate enhanced workflow efficiency and significantly reduced radiation dosing.

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Artificial intelligence facilitated a 96.2 percent sensitivity rate for incidental pulmonary embolism (IPE) on contrast-enhanced CT chest or abdomen exams, according to new prospective research involving over 4,300 patients.

The AI-enabled CINA-VCF algorithm can reportedly detect vertebral compression fractures within seconds after assessment of chest or abdominal CT scans.

Facilitating additional consultation on chest and abdominal CT scans, the Second Opinions teleradiology platform now features FDA-cleared AI tools for cardiac, bone and liver assessments.

An AI algorithm that incorporates scoring of coronary inflammation based on coronary CT angiography (CCTA) may enhance long-term cardiovascular risk stratification beyond conventional risk factor and imaging assessments, even in patients without obstructive CAD.

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The AI-powered Heuron ICH software reportedly has an 86 percent sensitivity rate for diagnosing intracranial hemorrhage on computed tomography (CT) scans.

An AI model that includes extracted radiomic features from CT scans more than doubled the sensitivity rate for preoperative prediction of lung cancer recurrence in comparison to traditional TNM staging, according to study findings to be presented at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting in Chicago.

In comparison to pure solid nodules in patients with non-small cell lung cancer (NSCLC), nodules with a minor ground glass opacity component were associated with over a 38 percent higher rate of recurrence-free survival.

In addition to detecting missed lung nodules on X-rays, the AI-powered Qure.ai lung cancer continuum platform reportedly automates lung nodule measurement on CT scans and facilitates multimodality reporting.

The second-generation version of the VUZE System reportedly offers expanded functionality and incorporation of varied sources of 3D imaging data, including cone-beam CT scans obtained in the OR.

One deep learning model had a 72.4 percent accuracy rate for differentiating between benign and malignant solid pulmonary nodules on non-contrast CT while another deep learning model demonstrated an 87.1 percent AUC for differentiating benign and inflammatory findings.

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Catch up on the top AI-related news and research in radiology over the past month.

For patients with no history of coronary artery disease (CAD), new research shows the use of CT-derived fractional flow reserve (FFR-CT) guided revascularizations after elective vascular surgery reduced myocardial infarction and all-cause death by 20 percent in comparison to standard care.

Image quality, sharpness, and contrast with AI-based denoising were significantly enhanced for neck CT in comparison to conventional CT image reconstruction at 100 percent and 50 percent mAs, according to newly published research.

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Reportedly trained on thousands of computed tomography scans, the e-Lung software utilizes machine learning to detect and assess the progression of features associated with interstitial lung diseases.

Combining clinical and CT features, adjunctive use of a classification and regression tree (CART) diagnostic model demonstrated AUCs for detecting clear cell renal cell carcinoma (ccRCC) that were 15 to 22 percent higher than unassisted radiologist assessments.