Arjan Hura, MD, notes that one of the most rewarding aspects of the integration of artificial intelligence in laser cataract surgery is how he improves the confidence of the surgeon. (Image credit: Adobestock / Zabhie)
Surgeons seek accuracy, efficiency and consistency in each procedure they perform. The integration of artificial intelligence (AI) into cataract surgery assisted by laser (lakes) is a leap forward in achieving these objectives. According to my experience, a robotic cataract laser system (Ally; Lensar, inc) incorporates 2 functions led by AI – Image analysis and laser fragmentation optimization – to rationalize surgical planning and execution.
Precision in the reconstruction of the 3D objective and the segmentation of the image
AI plays a crucial role in the segmentation of diagnostic imaging, another area where precision is essential. This platform operates networks of deep neurons formed on thousands of Scheimpflug images to accurately delimit the cornea and objective capsule, even in the presence of dense cataracts.
Studies have shown that AI -focused segmentation can repeatedly identify the limits of these structures at a pixel level and reconstruct surfaces with remarkable precision, reducing the risk of errors during cataract surgery.1
In addition, the segmentation of students and Limbus assisted by AI further improves the peroperative alignment and the recording of the iris, improving the predictability of the Toric Iol placement and the correction of astigmatism.2 Although manual marking can always be carried out as backup, the automated alignment capacities of a robotic cataract laser system considerably rationalize the process and offer an additional layer of precision.
Personalization led by AI for the density of the cataract
A challenge in the lakes is to determine the ideal laser energy and the fragmentation model for each eye. Traditionally, surgeons are counting on predefined parameters, such as standard or dense cataract modes, to dictate energy levels. Although effective, the approach lacks nuances and often requires peroperative adjustments. Alternately, robotic laser cataract robotic surgery uses AI to analyze large sets of cataract image data to automatically adapt energy parameters according to the density of the cataract, optimizing efficiency while minimizing Phaco energy.
Like many surgeons, I use predefined laser fragmentation models depending on the specific case, whether it is a routine cataract, a denser nucleus or a case involving a light adjustable lens where the precise dimensioning of the capsulotomy is essential. Rather than manually adjusting these parameters for each case, a robotic cataract laser system allows me to select a predefined pattern, and refined the laser energy parameters in real time. I now make fewer peroperative adjustments, which leads to a reduction in cases, lighter postoperative cornea and increased consistency between procedures.
One of the most rewarding aspects of AI integration into laser cataract surgery is how it improves the confidence of surgeons. Knowing that my surgical parameters are continuously optimized according to the real world data allows me to focus on the nuances of each case rather than the micro -management parameters. This efficiency results in a more fluid surgical experience – not just for me but also for my patients.
Arjan Hura, MD
Hura is in a private cabinet in Maloney-Shamie-Hura Vision Institute in Los Angeles, California. He is a consultant of Lensar, Inc.
References
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Morley D, Evans M. Scheimpflug Image Segmentation using in -depth learning. Presented at: Association for Research in Vision and Ophthalmology 2024 Annual Assembly; May 5-9, 2024; Seattle, wa.
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Morley D, Evans M. Pupil Multi-Appareils, limbus and segmentation of the eyelids using in-depth learning. Presented at: Association for Research in Vision and Ophthalmology 2024 Annual Assembly; May 5-9, 2024; Seattle, wa.
