University of Rochester Medical Center in the US selects Sectra Enterprise Imaging in the cloud

Linköping, Sweden and Shelton, CT – October 17, 2022– International medical imaging IT and cybersecurity company Sectra (STO: SECT B) will provide enterprise imaging as a cloud subscription service (Sectra One Cloud), throughout the University of Rochester Medical Center (URMC). This will allow the US health system scalability as enterprise imaging volumes grow, in a secure and fully managed cloud environment.

Breast density notification laws: FDA provides updated timeline on rollout

Example of the four types of breast tissue density. The density of fibroglandular tissue inside the breast impacts the ability to easily see cancers. Cancers are very easy to spot in fatty breasts, but are almost impossible to find in extremely dense breasts. These examples show craniocaudal mammogram findings characterized as almost entirely fatty (far left), scattered areas of fibroglandular density (second from left), heterogeneously dense (second from right), and extremely dense (far right). RSNA

Example of the four types of breast tissue density. The density of fibroglandular tissue inside the breast impacts the ability to easily see cancers. On X-ray mammography, cancer and dense breast tissue both appear as white and can hide smaller cancers on 2D mammography. Dense breasts are also a risk factor for cancer. Cancers are very easy to spot in fatty breasts, but are almost impossible to find in extremely dense breasts. These examples show craniocaudal mammogram findings characterized as almost entirely fatty (far left), scattered areas of fibroglandular density (second from left), heterogeneously dense (second from right), and extremely dense (far right). Read more. Image courtesy of RSNA

The proposed regulations would require all healthcare providers to offer patients a summary of their breast density that details their breast cancer risks and covers additional screening options that may be available.

HeartFlow gains FDA clearance for 2 new AI-powered imaging assessments

Left, HeartFlow's RoadMap analysis enables cardiac CT readers to identify stenoses in the major coronary arteries. The AI provides visualization and quantification of the location and severity of anatomic narrowings. Right image, HeartFlow's Plaque Analysis AI algorithm automates assessment of coronary plaque characteristics and volume on CCTA exams to greatly reduce the time it takes to manually assess and quantify these features.
Left, HeartFlow's RoadMap AI analysis enables cardiac CT readers to identify stenoses in the major coronary arteries. The AI provides visualization and quantification of the location and severity of anatomic narrowings. Right image, HeartFlow's Plaque analysis AI algorithm automates assessment of coronary plaque characteristics and volume on CCTA exams to greatly reduce the time it takes to manually assess and quantify these features.

The solutions, Plaque Analysis and RoadMap Analysis, both use coronary CT angiography to provide clinicians with a noninvasive look at patients who present with coronary artery disease and face a heightened myocardial infarction risk.

AI system boosts intracranial hemorrhage detection

Cureus, ICH on CT
Image courtesy of Cureus. Examples of scans initially missed by radiologists that were correctly predicted with the system. (A) A perimesencephalic subarachnoid hemorrhage (yellow arrow) and (B) a chronic convexity subdural hemorrhage (red arrow).

“This study implies that future clinical workflows may see AI be used in an adjunct capacity to improve interpretations of CT scans by helping call radiologists' attention to findings that may be overlooked.” 

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