Director, IT and Neuroinformatics Department, Buffalo Neuroimaging Analysis Center

Michael Dwyer, PhD

"The next frontier is to be able to leverage these giant databases we have. Now that we have tools to work on quality clinical scans, we essentially have unlocked these databases for mining these metrics."

Buffalo Neuroimaging Analysis Center (BNAC)

 has been at the forefront at developing new precision neuroimaging techniques and reevaluating the standard methods that have been in place for decades. There are several ongoing projects at the center, with therapeutic areas that include, but are not limited to, demyelinating diseases, neuromuscular diseases, and neurodegenative diseases. One notable study, CASA-MS, will compare advanced-stage patients with multiple sclerosis (MS) to age-, sex-, and disease-duration-matched patients with MS, in hopes to establish a comprehensive framework within which to study the unique needs of those severely impacted by MS.

, director of IT and Neuroinformatics Development, BNAC, specializes in quantitative neuroimaging analysis methods, as well as the use of artificial intelligence (AI). His highlights include the development and validation of a method for detecting and quantifying demyelination and remyelination in vivo, the development of a method dramatically improving on the precision of conventional tissue-specific atrophy measurement in clinical routine, and the investigation of the MRI “connectome” on cognition in patients with MS.

Dwyer discussed the highly anticipated work being done at the center as well as some of the most pressing needs within the space. He highlighted a previously conducted study using a deep gray matter approach and its association with MS disease progression.

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The director of IT and Neuroinformatics Development at the Buffalo Neuroimaging Analysis Center discussed future neuroimaging projects the center is evaluating and where the focus turns to next. [WATCH TIME: 3 minutes]

Future Direction of AI, Neuroimaging Research: Michael Dwyer, PhD

"The floodgates are not fully open yet, but we’ve already seen the first kind of vanguard of AI tools that have been FDA-approved. I think we’ll see it in a lot of other fields, too. The question is just making sure that we have good validation so that we know why it’s making those decisions."

Artificial intelligence (AI) and its capability to improve the healthcare system has always been a phenomenon. Through deep learning algorithms, computers can be taught to answer questions that augment human capabilities in imaging. Deep learning is a form of machine learning that employing convolutional neural networks, which has shown potential in medical imaging applications. This niche approach can help elevate and increase human capacity as well as improve efficiency by assisting in reading and computing large sets of data.

For some, AI is not just the future of just medicine, but of all occupational sectors because of its power to enhance efficiency, accuracy, value, and quality. Since its founding in 2000, the 

 has been developing and using state-of-the-art neuroimaging techniques to help better understand and combat neurological diseases and disorders. 

, director, IT and Neuroinformatics Development, BNAC, has also led research efforts that utilize AI; however, he considers it a "double-edged sword."

Dwyer, who also serves as an assistant professor of neurology and biomedical engineering at the University at Buffalo, claimed AI tends to have a black-box nature, which has limited it from becoming more relevant in the clinical space. In an interview with 

 he provided more context on the knowledge of AI, current limitations of this technique, and when clinicians can realistically expect it to incorporate it into neurology.

The director of IT and Neuroinformatics Development at the Buffalo Neuroimaging Analysis Center provided a realistic perspective of when and how AI will be incorporated into the wider medical system. 

Applying Artificial Intelligence and Neuroimaging Into Clinical Care: Michael Dwyer, PhD

"We can use AI to measure and assess the quality of traditional tools. It’s very complementary and not something that’s going to replace it, but instead augment what we can do."

MRI has become the most widely used imaging technique in the investigation and diagnosis of multiple sclerosis (MS). It has been an invaluable tool in understanding and monitoring the disease, as well as distinguishing it from other central nervous system demyelinating diseases. Various types of MRI cans are using in MS, including gadolinium, a contrast agent, which is injected into the veins during an MRI to help detect areas of new inflammation.

, efforts by Michael Dwyer, PhD, and his esteemed team have helped push the field forward by improving the capabilities of MRI through artificial intelligence (AI). Dwyer the director of IT and Neuroinformatics at BNAC, and an assistant professor of neurology and biomedical engineering at the University at Buffalo, believes that while lesion volume is an important area to monitor, brain atrophy can sometimes take a backseat, potentially creating more harmful problems to a patient’s brain than some of the lesions themselves.

Dwyer provided background on topics within the neuroimaging space that interest researchers and the importance of focusing on multiple types of biomarkers when evaluating progression of MS. He also provided comments on how AI can play a key role in alleviating the process of collecting large datasets and helping clinicians apply them to clinical practice.

The director of IT and Neuroinformatics Development at the Buffalo Neuroimaging Analysis Center provided thoughts on the trending topics within the neuroimaging space. 

Techniques, Biomarkers, and Hot Topics in Neuroimaging: Michael Dwyer, PhD

"We were able to use that data to train an artificial intelligence system to automatically delineate and locate the thalamus on T2 flare image, which is the lowest common denominator of imaging."

By leveraging new advances in machine learning and artificial intelligence (AI), the 

 has been able to develop numerous new MRI acquisition and analysis methods that are capable of analyzing large-scale datasets with tens of thousands of scans across multiple time points in thousands of individuals.

The efforts from the center coincide with the direction that next-generation precision medicine is headed. Michael Dwyer, PhD, director, IT and Neuroinformatics Development, BNAC, and assistant professor of neurology and biomedical engineering, University at Buffalo, has been a major driver for this type of innovative thinking. To date, he’s published over 150 scientific papers in peer-reviewed medical, imaging, and brain-related journals and reviewed publications for numerous journals in his field. Most recently, he formed an AI special interest group with a specific focus on applications of deep-learning techniques in neuroimaging.

Dwyer claims there are several capabilities and advantages this niche type of technology can bring to neurology. For example, he notes that they have used AI to translate existing metrics from clinical trials to quality, usable images. In an interview with 

Dwyer discussed the hype surrounding AI in neuroimaging, its ability to bridge untranslatable metrics, and its potential use in predicting disease progression in multiple sclerosis.

The director of IT and Neuroinformatics Development at the Buffalo Neuroimaging Analysis Center discussed the potential advantages AI-built neuroimaging brings to neurology.

Michael Dwyer, PhD

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Future Direction of AI, Neuroimaging Research: Michael Dwyer, PhD

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Techniques, Biomarkers, and Hot Topics in Neuroimaging: Michael Dwyer, PhD

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