Join Dr. Bermeo in a conversation with Dr. Ezequiel Gleichgerrcht, Dr. Erik Kaestner, and Dr. Peter Widdess Walsh, as they discuss the article, "More Than Meets the Eye: Human Versus Computer in the Neuroimaging of Temporal Lobe Epilepsy".

Click here to read the article.

This podcast was sponsored by the American Epilepsy Society.

We'd also like to acknowledge contributing editor Dr. Rohit Marawar, and the team at Sage Publishing.

Summary

This fantastic Epilepsy Currents podcast episode delves into a groundbreaking study on the application of artificial intelligence (AI) in differentiating temporal lobe epilepsy from Alzheimer's disease and healthy controls using MRI-based deep learning. The study, co-authored by Dr. Ezequiel Gleichgerrcht and Dr. Erik Kaestner, demonstrated AI's potential to significantly improve the accuracy of neuroimaging analysis in epilepsy. The commentary by Dr. Peter Widdess-Walsh provided a critical examination of these findings, highlighting the practical implications for clinical practice and the limitations of current methodologies. The discussion framed AI not only as a tool for enhancing diagnostic accuracy but also as a means to uncover subtle neurobiological differences between diseases, potentially leading to more personalized treatment approaches.

Key Takeaways

High Diagnostic Accuracy with AI: The study highlighted showed that AI, specifically convolutional neural networks (CNNs), could differentiate between temporal lobe epilepsy, Alzheimer's disease, and healthy controls with high accuracy. This demonstrates AI's capability to identify nuanced patterns in neuroimaging that may not be visible to the human eye.
AI Identifies Subtle Neurological Differences: The inclusion of Alzheimer's disease as a comparative group in the study was strategic to determine if the AI was recognizing temporal lobe epilepsy based on hippocampal atrophy alone or other disease-specific patterns. The findings suggest that AI can discern epilepsy-related patterns beyond the typical markers, indicating a profound potential for AI in understanding complex neurological conditions.
Impact on Clinical Practice: Dr. Widdess-Walsh's commentary underscores the importance of these findings for clinical practice. AI's superior detection rate compared to human analysis (90% versus 47%) suggests that AI could significantly augment clinicians' ability to diagnose temporal lobe epilepsy, potentially leading to earlier and more accurate interventions.
Limitations and Ethical Considerations: Despite AI's impressive capabilities, the podcast discussion acknowledges limitations, including the current inability of AI systems to interpret scans holistically, consider clinical context, or replace human decision-making. These factors highlight the need for AI to be used as an adjunct tool in clinical practice, enhancing rather than replacing the expertise of healthcare professionals.
Future Directions and Challenges: The episode concludes with reflections on the broader implications of AI in neurology, from predicting surgery outcomes to possibly revolutionizing epilepsy care. However, challenges such as external validation, regulatory approval, and integration into clinical workflows must be addressed to fully realize AI's potential in epilepsy diagnosis and treatment.

Transcript

Adriana Bermeo, MD (Host): Hello and welcome to episode five of Epilepsy Currents podcast. Today, we will be talking about the use of artificial intelligence and machine learning in the study of temporal lobe epilepsy. I am your host, Adriana Bermeo. I am the senior podcast editor for Epilepsy Currents, the official journal of the American Epilepsy Society.

Today, I am joined by a team of experts in this very exciting neurodiagnostic arena. I want to first welcome contributing editor, Dr. Peter Widdess-Walsh, who wrote the commentary "More Than Meets the Eye: Human vs. Computer Neuroimaging of Temporal Lobe Epilepsy." This commentary was published on December 2023 issue of Epilepsy Currents. Dr. Widdess-Walsh is a consultant neurologist and Clinical Associate Professor at Beaumont Hospital in Dublin, Ireland. Peter, welcome to Epilepsy Currents podcast. podcast.

Peter Widdess-Walsh, MD: Thank you very much, Adriana. I'm delighted to be here.

Host: Thank you for being here. It is also my pleasure to welcome Dr. Ezequiel Zeke Gleichgerrcht, who authored the work that inspired this commentary titled, "MRI Based Deep Learning Can Discriminate Between Temporal Lobe Epilepsy, Alzheimer's Disease, and Healthy Controls. Their paper was published in Nature Communications Medicine in 2023. Dr. Gleichgerrcht is an Assistant Professor of Neurology at Emory University. Zeke, welcome.

Ezequiel Gleichgerrcht, MD: Thank you. Thanks for having me.

Host: We are also joined by Dr. Erik Kaestner, co-author on the original paper and lead author on other related publications addressing the use of artificial intelligence in epilepsy. Dr. Kaestner is a postdoctoral scholar at University of California, San Diego. Erik, thanks for being with us today.

Erik Kaestner, PhD: Thank you. Excited for the conversation.

Host: Very good. I can tell you that artificial intelligence is one of the most requested topics when we ask our listeners and the epilepsy community. The challenges and opportunities presented by the use of AI in almost every aspect of our daily life, but particularly in healthcare, are intriguing, exciting, and potentially concerning to some. But before we start discussing AI, Peter, can you share with our listeners what is the added value of expanded neuroradiology in the care of patients with epilepsy?

Peter Widdess-Walsh, MD: Thanks, Adriana. And thank you for highlighting this very interesting and topical article for the podcast. And many thanks to Dr. Gleichgerrcht and Dr. Kaestner for being here with us. We know that MRI is a key tool in finding the underlying cause of focal epilepsy. However, up to 30% of MRIs are non-lesional. That is, there is no visible lesion responsible for the patient's epilepsy. We can sometimes lower this non-lesional percentage by using additional MRI techniques such as volumetry or various additional sequences or higher strength magnets. But these are not universally available or have limitations, so there's a gap there. We know that there is a focal epileptogenic network, but we cannot see it. And it is partly due to limitations of MRI technology, which are improving all the time, but it's partly due to limitations of what we can see visually with the human eye. There are also human error rates in analyzing MRI, even with skilled radiologists known as perceptual errors.

A shrunken, bright hippocampus is easier to see than subtle alterations in grey matter volumes. Particularly in surgically remediable epilepsies, finding a lesion or visualizing the epileptic network can make a huge difference in a better patient outcome after surgery. And even in non-surgical patients, it's very reassuring to have an imaging correlate of the epilepsy that you know is clinically present.

I'm not an AI expert and I wrote the commentary from the perspective of an epileptologist in clinical practice like many of our listeners. So, it was fascinating to learn more about AI and how it might fit into our daily practice in this area. The authors used the term computer vision in this article, and I'm excited to learn more about how computer vision can help us in the diagnosis of epilepsy.

Host: Thank you very much. We are certainly excited to see how we can advance our field further than what we are practicing at the moment. Zeke, could you paint the picture as for how is AI revolutionizing the field of radiology and particularly when it comes to neuroimaging analysis?

Ezequiel Gleichgerrcht, MD: Yeah, absolutely. And I think we start acknowledging that AI is becoming this true transformative force in medicine. Like you said, neuroimaging is by no means the exception. So for instance, if we think about AI and how it gives us frameworks to analyze huge amounts of imaging data, the pace and precision with which it can achieve it is unattainable by humans alone, right?

Humans not using this computer vision that Peter was mentioning. So, let's talk about neuroimaging specifically. AI algorithms can sift through MRI, CT scans, and, almost do a first pass at detecting abnormalities from brain tumors, there's applications for aneurysms, areas affected by stroke, you name it. Of course, our interest is in epilepsy, but we know how crucial early and accurate detection of some of these findings can be. So, you know, when trying to answer this question, how is it revolutionizing the field of radiology? I think it's really about how the accurate detection of these findings can be instrumental in patient care. So, AI ap...