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Coronary CT angiography is commonly performed using beta blockers to slow the heart rate and reduce motion artifacts in the images, thereby improving diagnostic accuracy. Oral metoprolol is often prescribed one hour prior to the procedure. Intravenous metoprolol may be given immediately prior to the procedure as an alternative or if oral beta blocker alone did not achieve adequate heart rate control.

No premedication equals fewer staff, more patients

Emphasizing enhanced workflow efficiency for mammography exams, GE HealthCare will unveil the Pristina Via mammography platform at the Radiological Society of North America (RSNA) conference.

In addition to offering zero-click acquisition to facilitate streamlined workflow, the 

 incorporates prior image comparison, irrespective of mammography vendor, to aid in mammography assessments, according to GE HealthCare.

The company adds that the Pristina Via system offers image to image cycle time for digital breast tomosynthesis (DBT) that is up to twice as fast as other mammography platforms, as well as the lowest radiation dose for all breast thicknesses.

“Simplifying, automating, and streamlining the mammography exam to improve in-room workflows is critical to driving patient compliance and access to screening,” said John Simon, M.D., the CEO of SimonMed. “The enhancements offered by Pristina Via are part of our mission of making advanced technology accessible.”

The new mammography system reportedly offers features that promote workflow efficiency as well as patient comfort.

Topic

GE HealthCare Introduces Pristina Via Mammography System at RSNA

The use of artificial intelligence (AI) tools to provide quantitative coronary tomography (AI-QCT) and coronary plaque analysis (AI-CPA) based on coronary computed tomography angiography (CCTA) will soon be covered by four Medicare administrative contractors (MACs).

Starting on November 24, the MACs Palmetto GBA, National Government Services (NGS), CGS Administrators and WPS administrative groups will cover the use of AI-QCT and AI-CPA for Medicare beneficiaries with acute or stable chest pain, and no known coronary artery disease (CAD), according to a recently updated local coverage determination (LCD) issued by the Centers for Medicare and Medicaid Services (CMS).

The LCD noted coverage of AI tools will be provided for the testing of patients deemed to be at intermediate risk for CAD, or those classified by the Coronary Artery Disease Reporting and Data System (CAD-RADS) as having CAD-RADS 1, CAD-RADS 2 or CAD-RADS 3, based on CCTA findings. The coverage will also apply for those who have had negative or inconclusive evaluation for acute coronary syndrome (ACS), according to the LCD.

The use of the FDA-cleared AI modalities 

“Coronary artery disease is the underlying cause of about 50% of deaths,” noted Ron Blankstein, M.D., the director of cardiac computed tomography at Brigham and Women's Hospital and a professor of medicine at Harvard Medical School in Boston. “Medicare coverage of HeartFlow Plaque Analysis is a major step forward in improving coronary artery disease management. The earlier we can diagnose and use tools to provide accurate plaque quantification, the earlier we can provide tailored treatments, potentially improving outcomes for patients.”

“We are also extremely thankful to the many physicians and medical society partners who used their voices to advocate for this essential AI-QCT technology. Until Cleerly, health-care providers have not had a way to easily and completely assess the primary cause of heart attacks, atherosclerotic plaque burden and type in a non-invasive way, which is especially important for our senior population,” noted James L. Kim, M.D., the CEO and founder of Cleerly. “This decision by the MACs represents a significant step in advancing the field of cardiology and providing the best possible care for our seniors.”

(Editor’s note: For related content, see “

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1. Centers for Medicare and Medicaid Services (CMS). Artificial intelligence enabled CT based quantitative coronary topography (AI-QCT)/coronary plaque analysis (AI-CPA). Available at: 

https://www.cms.gov/medicare-coverage-database/view/lcd.aspx?lcdid=39851&ver=10

 . Published October 10, 2024. Accessed October 14, 2024.

2. Cleerly. Medicare administrative contractors approve coverage of Cleerly heart scans to Medicare recipients. Business Wire. Available at: 

https://www.businesswire.com/news/home/20241014694634/en/Medicare-Administrative-Contractors-Approve-Coverage-of-Cleerly-Heart-Scans-to-Medicare-Recipients

 . Published October 14, 2024. Accessed October 14, 2024.

3. HeartFlow. HeartFlow announces favorable coverage determination for plaque analysis by four Medicare administrative contractors. GlobeNewswire. Available at: 

https://www.globenewswire.com/news-release/2024/10/11/2962144/0/en/HeartFlow-Announces-Favorable-Coverage-Determination-for-Plaque-Analysis-by-Four-Medicare-Administrative-Contractors.html

4. Elucid. Elucid announces favorable Medicare coverage of AI coronary plaque analysis. Business Wire. Available at: 

https://www.businesswire.com/news/home/20241015489543/en/Elucid-Announces-Favorable-Medicare-Coverage-of-AI-Coronary-Plaque-Analysis

 . Published October 15, 2024. Accessed October 15, 2024.

Four Medicare administrative contractors (MACs) will provide coverage of AI-enabled coronary plaque analysis and quantitative coronary tomography assessment of coronary computed tomography angiography (CCTA) for Medicare beneficiaries starting in November 2024.

Medicare to Cover AI-Powered, CCTA-Based Coronary Plaque Analysis and Quantitative Coronary Topography 

The Food and Drug Administration (FDA) has granted 510(k) clearance for AngioFlow™, an artificial intelligence (AI)-enabled perfusion imaging software that may facilitate more timely assessments of perfusion status prior to endovascular procedures.

Offering color-coded perfusion mapping that identifies regions with reduced cerebral blood flow and blood volume, 

 provides results within minutes of a cone-beam computed tomography (CT) scan, according to RapidAI, the manufacturer of AngioFlow.

In addition to potential cost savings by reducing redundant imaging, RaidAI emphasized that AngioFlow’s timely assessments may reduce long-term disability risks and facilitate life-saving interventions for patients in need of acute stroke care.

“AngioFlow by RapidAI will allow physicians to assess the need for additional imaging immediately in the interventional suite. By avoiding unnecessary scans, stroke patients can receive the timely care that can be the difference between being able to walk out of a hospital to their homes versus being discharged to a skilled nursing facility,” said Abhishek Singh, MD, DABPN, DUCNS, who is affiliated with the Creighton University School of Medicine in Omaha, Nebraska.

The company added that AngioFlow has been validated in more than 10 clinical trials.

Reportedly validated in more than 10 clinical trials, the AngioFlow perfusion imaging software enables timely identification of brain regions with cerebral blood flow reduction and those with significant hypoperfusion.

FDA Clears AI-Powered Qualitative Perfusion Mapping for Cone-Beam CT

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Amid a backdrop of increasing worklists and recent survey results indicating that nearly 40 percent of polled radiologists are looking to leave the profession, Radiology Partners has launched a new operating system, MosaicOS, with the goals of improving workflow efficiencies and reining in AI technologies into one system.

Launched through Radiology Partners’ new Mosaic Clinical Technologies division, the 

 allows rapid cloud-based deployment and offers intuitive AI-driven technology.

Key attributes with MosaicOS include Mosaic Reporting, which provides automated reporting structure through a combination of ambient voice AI and large language model (LLM) technologies. Another emerging feature with MosaicOS is Mosaic Drafting, which facilitates initial drafting of X-ray reports based on a multimodal AI foundation model, according to Radiology Partners.

“MosaicOS™ represents an urgent call from our specialty,” said Nina Kottler, M.D., MS, FSIIM, an associate chief medical officer for clinical AI at Radiology Partners. “Demand is growing. Worklists are getting longer. While the pace of AI innovation is exciting, fragmented technologies create friction, forcing radiologists to toggle between systems and duplicate tasks, ultimately slowing care. MosaicOS™ rewrites the rules, connecting AI, clinical expertise and scalable technology into a single solution that empowers radiologists, restores focus to patient care and signals our commitment to shaping the future of our specialty.”

A cloud-based and AI-native radiology operating system, MosaicOS reportedly enables the combination of diagnostic AI tools and workflow enhancements into one scalable platform.

Radiology Partners Launch Enterprise Imaging Platform MosaicOS

A new meta-analysis suggests that magnetic resonance imaging (MRI)-based artificial intelligence (AI) offers significant prognostic value in preoperative assessment for possible microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC).

For the meta-analysis, recently published in 

, researchers reviewed data from 29 studies to evaluate preoperative use of MRI-based AI for predicting MVI, which has been associated with post-op recurrence and metastasis in patients with HCC.

The meta-analysis authors found that MRI-based AI models offered pooled sensitivity of 83 percent, specificity of 81 percent and an AUC of 85 percent from the external validation cohort.

“These results suggest robust generalizability of the models across different datasets, with no significant decline in performance observed in the external validation cohort, indicating stability and transferability. The MRI-based AI models effectively extract complex imaging features from both the tumor and surrounding tissues, capturing essential biological information related to MVI, thereby enhancing predictive sensitivity and specificity,” wrote lead meta-analysis author Xin Han, M.D., who is affiliated with the Shandong University of Traditional Chinese Medicine in Shandong, China, and colleagues.

For the external validation cohorts, the researchers found slightly higher pooled sensitivity for machine learning models in comparison to deep learning models (85 percent vs. 81 percent).

“Most machine learning models in the included studies utilized LR (logistic regression) algorithms, often combined with clinical data to construct multimodal models, which may contribute to their higher diagnostic performance. However, the apparent superiority of traditional machine learning algorithms may also result from factors unrelated to the algorithm itself, such as differences in data splitting methods and feature selection bias between studies.” noted Han and colleagues.

In comparison to radiomic-only models, the meta-analysis authors determined that models that combine radiomics and clinical factors offered higher sensitivity (80 percent vs. 82 percent) and specificity (79 percent vs. 84 percent).

“These findings suggest that integrating clinical features with radiomic characteristics provides a more comprehensive reflection of the tumor's biological heterogeneity, thereby enhancing the predictive accuracy for MVI. Potential mechanisms for this phenomenon include the ability of clinical features, such as AFP (alpha-fetoprotein) levels and liver function indicators, to reflect the patients' pathological and physiological complexities, complementing the imaging characteristics and improving the model's discriminative power and robustness,” explained Han and colleagues.

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In regard to limitations of the meta-analysis, the authors acknowledged that most of the reviewed studies were retrospective and conceded they only included data from the AI algorithm that had the highest sensitivity in each study. Noting that all of the reviewed studies had Chinese cohorts, the researchers said the meta-analysis findings may have limited extrapolation to broader populations.

In external validation findings from a 29-study meta-analysis, MRI-based AI had a pooled AUC of 85 percent for preoperative prediction for microvascular invasion in patients with hepatocellular carcinoma.

Arthur E. Stillman, MD, PhD

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