AHRA: AI improves speed, quality of neuroimaging

Artificial intelligence (AI) technology can add significant value in the practice of neuroradiology, especially in the areas of quantitative imaging, triage, and image reconstruction, according to a Monday session at the AHRA 2022 meeting in Phoenix.

“AI in quantitative imaging improves detection, quantification, standardization, and consistency across the board,” said Dr Lawrence Tanenbaum, director of MR, CT, and advanced imaging and medical director for the eastern region of imaging services provider RadNet. “Triage can accelerate care as well as improve outcomes. And [the use of AI in] image reconstruction is a patient-centered care move, enabling less time for patients on the table, better quality across the board, better efficiency, and a great response to this economic environment.”

Deep learning can also be used to support automated image acquisitions in spine MRI, for example, guiding the scan position and scan planes in a more reproducible manner, he said.

Also, natural language processing (NLP) technology can have a big impact on quality, ensuring compliance with guidelines and monitoring of patient compliance with follow- up recommendations. Furthermore, NLP can be combined with computer-aided detection (CAD) software, reviewing reports and alerting radiologists if they may have missed findings on an image, he said.

NLP could even be used to adjust imaging reports to different levels of understanding, such as that of patients and referring physicians, Tanenbaum said. It can also capture AI findings and insert the results directly into the radiology report, and even write the report’s conclusion for the radiologist.

Tools such as these can also improve standardization among radiologists, he said.

Segmentation
Segmentation is one of AI’s natural capabilities and is useful in the brain and the spine, according to Tanenbaum. For example, AI can assist in assessment of multiple sclerosis (MS) by counting lesions and assessing change in both the size and number of lesions, he said.

“It isn’t easy for a radiologist to tell whether a patient has 31 or 32 lesions,” he said. “It’s almost impossible. It’s like counting spots on a Dalmatian.”

There’s also high inter-rater variability in counting of MS lesions.

“So using an AI-driven tool you can actually make the exam more efficient, balance out differences in expertise, and detect the differences and do it in a quantitative way,” he said.

AI can also identify new lesions and show which lesions are bigger and which ones are smaller, Tanenbaum said.

“And that’s very, very powerful,” he said.

Several studies in the literature have shown that AI-assisted software for quantitative neuroimaging and MS can standardize and speed reporting, as well as enhance sensitivity. AuntMinnie