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.

AI automation could 'revolutionize' Crohn's Disease severity CT assessments
Machine learning models could help create a more standardized, reproducible and efficient way of grading Crohn’s disease severity in the small bowel based on CT imaging.
New publicly available deep learning model for CT organ segmentation in children shows promise
The model was developed and validated specifically for liver, spleen and pancreas segmentation, and outperformed a publicly available segmentation model already in use.
New research offers reminder of why ChatGPT should not be used for second opinions
Although these tools have proven themselves valuable in numerous settings, they must be used with caution, especially by patients and nonradiologist providers who may be seeking clarification on imaging reports.
GPT-4 confidently struggles on radiology exam
Image-based questions were significantly more challenging for the large language model to answer, despite the latest version now being capable of accepting image prompts.
AI improves CT assessments, boosts care for real-world heart patients
Automated AI-generated measurements combined with annotated CT images can improve treatment planning and help referring physicians and patients better understand their disease, explained Sarah Jane Rinehart, MD, director of cardiac imaging with Charleston Area Medical Center.

Imaging data backs physical activity as guard against Alzheimer’s

Increasing daily physical activity may help older adults delay their progression to Alzheimer’s disease (AD), according to research published July 16 in JAMA Neurology.

July 25, 2019

As artificial intelligence (AI) adoption expands in radiology, there is growing concern that AI algorithms needs to undergo quality assurance (QA) reviews. How to validate radiology AI? How can you validate medical imaging AI?

New AI tool may be a powerful force in cancer care

A deep learning platform created by researchers at the Dana-Farber Cancer Institute can identify cancer in radiology reports as well as clinicians, but in a fraction of the time, according to new research published July 25 in JAMA Oncology.

July 25, 2019

4D flow MRI offers insight into COPD, emphysema

“Our multicenter study found that 4D flow MRI provided a promising way of measuring blood flow in the superior and inferior caval veins and right heart, which may provide further insight into physiologic and pathologic blood flow patterns in individuals with COPD and emphysema,” wrote researchers in a July 24 Radiology study.

July 24, 2019

MRI reveals neurological insight into 2016 ‘auditory attack’ in Cuba

New research published July 23 in JAMA reported neurological differences in several areas of the brain, including white matter volume, among U.S. government personnel involved in a 2016 auditory attack in Cuba compared to healthy controls.

July 23, 2019

AI identifies cancerous regions on OCT breast tissue images

A deep learning classification approach can identify cancerous regions from benign areas in optical coherence tomography (OCT) images of breast tissue, according to results of a July 17 study published in Academic Radiology.

July 22, 2019

Medtronic, Viz.ai partner on AI-powered stroke detection

Medtronic and Viz.ai, a growing leader in artificial intelligence, are collaborating on a new software to automatically alert specialists when a stroke is identified during a CT scan.

July 22, 2019

Deep learning distinguishes Alzheimer’s from normal cognition

A deep learning model that simulates a clinician’s diagnostic process can accurately diagnose Alzheimer’s disease from cognitively normal patients, according to a study published July 16 in Neurocomputing.

July 22, 2019

AI predicts long-term mortality from single chest x-ray

“The CXR-risk score took as input the radiograph only. This was intended to prove a point—that a CNN can extract prognostic information embedded in the image, without any other demographic or clinical information,” wrote authors of a new study published in JAMA Network Open.

July 19, 2019

Around the web

Cardiovascular Business

AI improves CT assessments, boosts care for real-world heart patients

Automated AI-generated measurements combined with annotated CT images can improve treatment planning and help referring physicians and patients better understand their disease, explained Sarah Jane Rinehart, MD, director of cardiac imaging with Charleston Area Medical Center.

Cardiovascular Business

AI predicts cardiovascular risk during CT scans—no invasive tests or contrast required

Two advanced algorithms—one for CAC scores and another for segmenting cardiac chamber volumes—outperformed radiologists when assessing low-dose chest CT scans.

Radiology Business

Google teams with Bayer to address radiologist burnout via AI

"Gen AI can help tackle repetitive tasks and provide insights into massive datasets, saving valuable time," Thomas Kurian, CEO of Google Cloud, said Tuesday.