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Although AI is still finding its footing in radiology, new uses emerge on a seemingly daily basis. This month, we’re sharing some exciting news from RSNA on how AI can help radiologists read chest X-rays more efficiently and improve diagnosis.
Where do you think the next big AI breakthrough in radiology will be? Let us know what you think on Twitter and/or Facebook.
— Dave Yeager, editor |
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AI Improves Chest X-Ray Interpretation
AI can detect clinically meaningful chest X-ray findings as effectively as experienced radiologists, according to a study recently published in the journal Radiology. Researchers say their findings, based on deep learning, could provide a valuable resource for the future development of AI chest radiography models.
Chest radiography, one of the most common imaging exams worldwide, is performed to help diagnose the source of symptoms such as cough, fever, and pain. Despite its popularity, the exam has limitations.
“We’ve found that there is a lot of subjectivity in chest X-ray interpretation,” says study coauthor Shravya Shetty, an engineering lead at Google Health in Palo Alto, California. “Significant interreader variability and suboptimal sensitivity for the detection of important clinical findings can limit its effectiveness.”
Deep learning has the potential to improve chest X-ray interpretation, but it has limitations. For example, results derived from one group of patients cannot always be generalized to the population at large.
Researchers at Google Health developed deep learning models for chest X-ray interpretation that overcome some of these limitations. They used two large data sets to develop, train, and test the models. The first data set consisted of more than 750,000 images from five hospitals in India, while the second set included 112,120 images made publicly available by the National Institutes of Health (NIH).
Full story » |
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From the Heart
Researchers are perfecting the art of using 3D printing technology to produce patient-specific cardiac models. Find out how this process is shaping the future of cardiology and how 3D bioprinting could do the same for the heart transplant process. Read more »
Unified Response
The need for stronger collaboration among the fields of radiology, pathology, and genomics is growing. Integrated diagnostics could be key to strengthening interdependence and improving diagnosis. Read more »
Doing Without
Noncontrast MRI techniques, such as hierarchical template matching, represent the newest way that the radiology field is working to better diagnose cardiovascular disease. Read more »
Accessible Care
In rural areas, providing quality care poses both a challenge and an opportunity for radiologists. Specialists are looking toward technology as a means of improving accessibility and standardization for an underserved population. Read more » |
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Intravital Window Affords Snapshot of Colon Cells
Duke University researchers have successfully made real-time observations of individual cells in the colon of a mouse. The technique, involving an implanted window and immobilization of the notoriously unstable region, sheds light on the study of gastrointestinal disorders, according to an article in Nature Communications.
Radiologists Favor Single-Vendor Approach, Survey Suggests
A recent report from Reaction Data has sparked discussion on the merits of deconstructed PACS vs a single-vendor IT system. A follow-up analysis by HIT Infrastructure reveals that a significant number of radiology practices are trending toward the latter.
Study: Unnecessary Appendectomies Common in UK
According to research published in the British Journal of Surgery, thousands of needless appendectomies are performed annually in the United Kingdom. The Guardian reports that, due in part to longstanding concerns about exposure to radiation, the UK’s National Health Service has resisted adopting a risk model that incorporates CT scans, although such a model has the potential to significantly reduce unnecessary appendectomies.
MRI Texture Analysis Streamlines Parkinson’s Diagnosis
Patients with Parkinson’s benefit from early detection of cognitive impairments, and MRI technology offers the potential to enhance the process. According to findings published in Movement Disorders, MRI texture analysis can distinguish between individuals with regular cognitive capabilities and those with Parkinson’s-related impairments. |
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“Imagine you can just take a photo and out comes a 3D model, which means that you can now look at that scene that you have taken a picture of [from] all sorts of different viewpoints. You can go inside it, potentially, view it from different angles—you can take old photographs in your photo collection and turn them into a 3D scene and inspect them like you were there, basically.”
— Sanja Fidler, PhD, of Nvidia Research, regarding a new AI system that takes 3D rendering to new heights, extrapolating robust detail (shape, 3D geometry, color, and texture) from 2D images |
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