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An artificial intelligence (AI) algorithm using deep learning can enable clinicians to estimate the CAC score on routine non-contrast chest CT, potentially allowing opportunistic early preventive interventions. A multi-center team (including UCSF) took part in a study and developed a fully automatic, end-to-end deep learning model for automated CAC scoring using routine non-gated unenhanced chest CT exams. The model was trained using traditional CAC scores based on gated CT as ground truth; the trained model was evaluated on multiple external datasets. Investigators released labeled datasets of gated and non-gated scans with annotations to potentially help fuel further efforts in this domain by other investigators. The result was good news.
Radiologists can assess skeletal age on hand radiographs faster and more accurately with assistance from an artificial intelligence (AI) algorithm, according to a prospective analysis published online September 28 in Radiology. A group of researchers led by David Eng of Stanford University trained a deep-learning algorithm and then conducted a prospective and randomized multicenter controlled trial to evaluate its performance across six different sites. Although results varied among the centers, AI yielded an overall improvement in speed and accuracy.
Coronary artery disease (CAD), the most common manifestation of cardiovascular disease, remains the most common cause of mortality in the United States. Risk assessment is key for primary prevention of coronary events and coronary artery calcium (CAC) scoring using computed tomography (CT) is one such non-invasive tool. Despite the proven clinical value of CAC, the current clinical practice implementation for CAC has limitations such as the lack of insurance coverage for the test, need for capital-intensive CT machines, specialized imaging protocols, and accredited 3D imaging labs for analysis (including personnel and software).
Our AI algorithim can help determine which patients are at risk and improve treatment accuracy.
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