Arjan Hura, MD, notes that one of the most rewarding aspects of artificial intelligence integration in laser cataract surgery is how it enhances surgeon confidence. (Image credit: AdobeStock/zabhie)
Surgeons strive for precision, efficiency, and consistency in every procedure they perform. Integrating artificial intelligence (AI) into laser-assisted cataract surgery (LACS) is a significant leap forward in achieving these goals. In my experience, a robotic cataract laser system (ALLY; LENSAR, Inc) incorporates 2 AI-driven functions—image analysis and laser fragmentation optimization—to streamline surgical planning and execution.
Precision in 3D lens reconstruction and image segmentation
AI plays a crucial role in the segmentation of diagnostic imaging, another area where precision is paramount. This platform leverages deep neural networks trained on thousands of Scheimpflug images to accurately delineate the cornea and lens capsule, even in the presence of dense cataracts.
Studies have shown that AI-driven segmentation can repeatedly identify the boundaries of these structures at a pixel level and reconstruct the surfaces with remarkable accuracy, reducing the risk of errors during cataract surgery.1
Moreover, AI-assisted pupil and limbus segmentation further enhances intraoperative alignment and iris registration, improving the predictability of toric IOL placement and astigmatism correction.2 Although manual marking may still be performed as a backup, the automated alignment capabilities of a robotic cataract laser system significantly streamline the process and offer an added layer of precision.
AI-driven customization for cataract density
A challenge in LACS is determining the ideal laser energy and fragmentation pattern for each eye. Traditionally, surgeons rely on predefined settings, such as standard or dense cataract modes, to dictate energy levels. Although effective, the approach lacks nuance and often requires intraoperative adjustments. Alternatively, robotic laser cataract surgery uses AI to analyze vast data sets of cataract images to automatically tailor energy settings based on cataract density, optimizing efficiency while minimizing phaco energy.
Like many surgeons, I use predefined laser fragmentation patterns depending on the specific case—whether it’s a routine cataract, a denser nucleus, or a case involving a light adjustable lens where precise capsulotomy sizing is critical. Rather than manually adjusting these settings for each case, a robotic cataract laser system allows me to select a predefined pattern, and AI fine-tunes the laser energy parameters in real time. I now make fewer intraoperative adjustments, resulting in reduced case times, clearer corneas postoperatively, and enhanced consistency across procedures.
One of the most rewarding aspects of AI integration in laser cataract surgery is how it enhances surgeon confidence. Knowing that my surgical parameters are continuously optimized based on real-world data allows me to focus on the nuances of each case rather than micromanaging settings. This efficiency translates to a smoother surgical experience—not just for me but also for my patients.
Arjan Hura, MD
E: arjan.hura@gmail.com
Hura is in private practice at Maloney-Shamie-Hura Vision Institute in Los Angeles, California. He is a consultant to LENSAR, Inc.
References
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Morley D, Evans M. Scheimpflug image segmentation using deep learning. Presented at: Association for Research in Vision and Ophthalmology 2024 Annual Meeting; May 5-9, 2024; Seattle, WA.
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Morley D, Evans M. Multi-device pupil, limbus, and eyelid segmentation using deep learning. Presented at: Association for Research in Vision and Ophthalmology 2024 Annual Meeting; May 5-9, 2024; Seattle, WA.
