Artificial Intelligence

Artificial intelligence (AI) is becoming a crucial component of healthcare to help augment physicians and make them more efficient. In medical imaging, it is helping radiologists more efficiently manage PACS worklists, enable structured reporting, auto detect injuries and diseases, and to pull in relevant prior exams and patient data. In cardiology, AI is helping automate tasks and measurements on imaging and in reporting systems, guides novice echo users to improve imaging and accuracy, and can risk stratify patients. AI includes deep learning algorithms, machine learning, computer-aided detection (CAD) systems, and convolutional neural networks. 

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Deep learning algorithm predicts emphysema mortality

Authors of the study noted that using the algorithm eliminates the issue of subjectivity and time-consuming visual assessments of emphysema.

breast cancer screening mammography

Malignant architectural distortion ably diagnosed on breast imaging by human-AI combo

Combining ensemble AI models with reads from breast radiologists of mixed experience levels can help health systems consistently diagnose malignant architectural distortion on mammography.

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DL networks augment radiologist performance for thyroid cancer detection

After being presented with more than 15,000 images, each DL network yielded results comparable to that of four seasoned radiologists, authors of a new EJR study said.

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AI model could open doors for greater access to obstetric ultrasound

Experts have developed an artificial intelligence model that can estimate gestational age with accuracy that rivals that of formally trained sonographers completing fetal biometry scans. 

Stratifying patients by risk of poor outcomes could reduce overtreatment of lung cancer

Researchers are using radiomics to narrow patient cohorts down to those who are at the greatest risk of poor lung cancer outcomes.

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AI spots pancreatic cancer in its earliest stages

Experts involved in the study suggest their findings could eventually be used to detect pancreatic cancer in its earliest stages when patients are most likely to respond to interventions favorably.

Machine learning model quickly and accurately predicts outcomes for TBI patients

The model combines clinical data with imaging from head CT scans in individuals with severe traumatic brain injuries to quickly predict 6-month outcomes.

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AI-based mammo screening protocol reduces radiologist workload by 62%

Researchers reported that the artificial intelligence system was able to interpret more than 114,000 screening mammograms using a reading protocol with high sensitivity and specificity.

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Positron partners with Upbeat Cardiology Solutions to improve cardiac PET/CT access

Positron, a New York-based nuclear imaging company, will now provide Upbeat Cardiology Solutions with advanced PET/CT systems and services. 

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‘A huge win’: CMS significantly increases Medicare payments for cardiac CT

CMS has more than doubled the CCTA payment rate from $175 to $357.13. The move, expected to have a significant impact on the utilization of cardiac CT, received immediate praise from imaging specialists.