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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Machine learning-based ‘red dot’ triage system shows promise for optimizing radiologist workload

A machine learning-based “red dot” triage system could help differentiate between normal and abnormal chest radiographs while optimizing clinician workflow, British researchers reported this month in Clinical Radiology.

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ASNR honors neuroradiology fellow for deep learning research

The American Society of Neuroradiology (ASNR) announced that Peter Chang, MD, a neuroradiology fellow at the University of California San Francisco, has received the Cornelius G. Dyke Memorial Award for his recent research involving deep learning technologies.

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Machine learning trumps conventional analysis in detecting lymphedema

Machine learning algorithms can now identify lymphedema—a chronic side effect of breast cancer treatment—with 94 percent accuracy, New York University researchers reported this month in mHealth.

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What can self-driving vehicles teach us about radiology’s relationship with AI?

Radiology professionals working on artificial intelligence (AI) technologies can learn a lot from studying self-driving vehicles, according to a new commentary published in the Journal of the American College of Radiology.

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AI detects skin cancers with more accuracy than dermatologists

Convolutional neural networks (CNN) were more accurate than even the most expert dermatologists in detecting skin cancer, researchers reported in Annals of Oncology this week.

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Machine learning accurately diagnoses breast lesions identified during cone-beam CT exams

Machine learning techniques perform well when tasked with predicting malignancy in breast lesions identified during breast cone-beam CT (CBCT) exams, according to a new study from German researchers published by the American Journal of Roentgenology. One technique, back propagation neural networks (BPN), outperformed two radiologists.

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British prime minister pledges millions to develop AI as a ‘weapon’ against cancer

British Prime Minister Theresa May announced this week she will pledge millions of pounds to the fight against cancer through the development of artificial intelligence (AI), Forbes has reported.

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Hospitals in London to start using AI for tasks typically performed by doctors, nurses

A new partnership between University College London Hospitals and the Alan Turing Institute aims to start using artificial intelligence (AI) to perform certain tasks typically carried out by doctors and nurses.

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Around the web

Health Imaging
Nearly $800,000 settlement offered to family of man who died during MRI exam

The patient, who was being cared for in the ICU, was not accompanied or monitored by nursing staff during his exam, despite being sedated.

Cardiovascular Business
Global shortage of nuclear imaging isotopes may be over

The nuclear imaging isotope shortage of molybdenum-99 may be over now that the sidelined reactor is restarting. ASNC's president says PET and new SPECT technologies helped cardiac imaging labs better weather the storm.

Cardiovascular Business
‘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.