It's an honor to be asked to participate. It's always difficult being the last up for a meeting. I know everyone's had an enriching day so far, so I'll hopefully provide some good insights as to what's to come next in capsule endoscopy. So just to recap recent developments that we're all fairly familiar with in the world of capsule endoscopy. One thing for certain are image quality, the quality of the images we've been able to get over the last five to ten years has ramped tremendously with the increased competition in the capsule endoscopy space. Definitely one area of improvement. Along with that, we've been able to optimize battery life. There's always the concern that enhanced image quality would diminish battery life. Thankfully, these developments have gone hand in hand. The ability to transmit data remotely, they're now of course wireless, the ability to capture images wirelessly, reducing how cumbersome the device is for the patient and for staff to attach the device. Of course, increased accuracy going along with enhanced image quality and then more detailed images as a result of that increased accuracy. Overlay that with the advent of AI and machine learning algorithms to help triage images, to help re-stratify the images that are being captured to help the interpreter perhaps go to what could potentially be the highest yield images first. So, future directions for capsule endoscopy. I'm going to start with miniaturization and integration. Efforts to do this would of course improve patient comfort with ease of ingestion, but would also hopefully allow you to integrate multiple functions including imaging, sensing, and of course the opportunity to provide therapy using the capsule endoscope. And this would allow for streamlining of both diagnostic and therapy procedures. Again, trying to make this smaller but with more functions in the device. The ongoing efforts to improve the quality and resolution of images, this is only expected to continue along with higher definition cameras as has already been the trend. And more advanced optics to enhance the above. Image processing algorithms and techniques, and these go hand in hand with the overlay of AI and machine learning tools to again optimize the images, re-stratify them, and then allow the interpreter, the reader, to again go to what could perhaps be the highest risk, highest yield findings first, along with clear and more detailed visualization. And there's also the opportunity to add OCT, optical coherence tomography, to allow for cross-sessional imaging to go along with the actual images themselves in high resolution to really allow for localization of lesions, better localization of lesions. So, there are ongoing research efforts to look at drug delivery using capsule endoscopes as drug delivery systems. This would allow the targeted delivery of treatments to specific regions of the small bowel. This could be particularly useful in inflammatory management of patients with inflammatory bowel disease, obviously for hemostasis and control of bleeding, and even in the management of malignancies. Another area of innovation involves the ability to control the capsule endoscope and, of course, power the device in wireless forms, allowing for even more prolonged battery life and allowing for even more detailed studies. To eliminate the need for batteries would also address what I spoke about a couple minutes ago regarding making the device less cumbersome, easier for staff to attach the device, begin the study for the patient, and easier for the patient to deal with as well. Again, the enhanced power capabilities would enable longer and more comprehensive imaging studies. And, of course, the opportunity to allow for real-time control of the capsule's movement and orientation. This is a device already available to some degree, already available in limited degree that can allow for steering of the capsule within the stomach. Again, the ability to provide wireless control, additional clinical indications such as hemostasis, the ability to deliver hemostatic agents, to provide tamponade, and even to apply limited forms of energy, either in the form of electrocautery or even laser. And this could also help with the management of benign and malignant lesions. So, I'll go back to what we talked about in the beginning, but to build on what's already happening in the realm of AI and machine learning. These enhancements and these advancements would allow for the ability to automate image analysis, to improve diagnostic accuracy, hopefully allowing for earlier detection of lesions that we might be able to intervene on endoscopically, as Dr. Dice would inform me so eloquently detailed, and, of course, allow for the opportunity for real-time feedback to identify lesions, perhaps bleeding in real-time to allow for more expeditious intervention. Devices are already out there that allow for, with the screen on the reader, that allow you to localize the capsule and watch the images in real-time, building on that technology that's already available. These are some examples. Another area of innovation in this space is the opportunity to biopsy and to sample tissue. And these are diagrams and schematics of some devices that are being experimented with. This first one to our left, it's designed as a shaving device that rotates, so you can use the hole that you see there to capture tissue and store it within the capsule. Next to that, on the right, is actually the ability to deploy small forceps that's carried within the capsule body itself and then capture that tissue and bring it back within the capsule for later retrieval. A couple of other innovations that are being worked on is similar to that rotational capture device, rather than the sheath and the capture channel being on the side of the capsule endoscope, this one on the left actually has it at the tip of the capsule endoscope, where you have basically a rotating blade within the capsule itself to rotate, cut, and capture tissue to bring into the capsule. And then another similar innovation with a rotating cutting knife, but again, more towards the front of the capsule rather than towards the side. Moving on to the capabilities looking for sensors and biomarkers, again, allowing for real-time monitoring for patients, looking at physiologic parameters, looking for disease markers, and also evaluating response to treatment, really giving us an opportunity to offer personalized medicine in the realm of capsule endoscopy for our patients, and hopefully assisting with better clinical decision-making. This is a schematic of a device using fluorescence technology with the idea that different processes have different fluorescent signatures. So in summary, R&D in capsule endoscopy continues to advance the field, improving diagnostic capabilities and developing therapeutic capabilities. Overall, the goal here is to improve the patient experience and, of course, guide for better clinical outcomes. I thank you for your attention.

capsule endoscopy

AI and machine learning

diagnostic and therapeutic integration

personalized medicine

image quality enhancement