Let me tell you, folks, we’ve got something huge here. Focal cortical dysplasias, or FCDs, are a big deal in the world of epilepsy. They’re tough, really tough to spot, but catching them early? That’s a game-changer for people suffering from drug-resistant epilepsy. Now, there’s been progress, sure, but it’s been slow. We needed something better, something smarter. Enter House et al. (2021) with their convolutional neural network (CNN) – a real piece of work for automated FCD detection. But, let’s be honest, it wasn’t perfect. Great sensitivity, but the specificity? Only 5.5%. Not good enough.

So, what did we do? We took it to the next level. We didn’t settle. We gathered a dataset of 300 MRIs, right from the trenches of daily clinical practice. We’re talking about the real deal here – 3D T1, FLAIR sequences, the works. We had experts, the best neuroradiologists and epileptologists, take a look. And then, we trained our CNN like it’s never been trained before, using data-driven training, algorithm optimization, and let me tell you, the results? Phenomenal.

With this new and improved CNN, we hit a sensitivity of 90.0%, specificity of 70.0%, and accuracy of 72.0%. And the false positives? Down to an average of 0.41 per MRI. For segmentation, we’re talking about a Dice coefficient of 0.56. Every phase of training, we got better, stronger. And this continual learning approach? Blew classical learning out of the water.

So here’s the bottom line: We’ve got a CNN that’s not just good, it’s great. High sensitivity, high specificity, and it keeps getting better the more we use it. This isn’t just a tool; it’s a revolution for detecting FCDs in daily clinical practice. We’re talking about a real chance to change the game for people with drug-resistant focal epilepsy. This CNN? It’s going to make a huge difference. Believe me.