Corneal Ablation by 266-nm and 193-nm Lasers: A Comprehensive Chemical Analysis Study Assisted with Machine Learning | ||||
Journal of Laser Science and Applications | ||||
Article 7, Volume 2, Issue 1, June 2025, Page 119-127 PDF (554.55 K) | ||||
Document Type: Original Article | ||||
DOI: 10.21608/jlsa.2025.372983.1032 | ||||
![]() | ||||
Authors | ||||
Ibrahim Abdelhalim1; Omnia Hamdy ![]() ![]() | ||||
1Engineering Applications of Laser Department , National Institute of Laser Enhanced Sciences, Cairo University | ||||
2Cairo University | ||||
3Medical Applications of Laser Department, National Institute of Laser Enhanced Sciences, Cairo University | ||||
4Vision Science Department, Research Institute of Ophthalmology, Biophysics and Laser Science Unit, , Giza, Egypt | ||||
5Engineering Applications of Laser Department , National Institute of Laser Enhanced Sciences, Cairo University, Giza, 12613, Egypt | ||||
Abstract | ||||
Background: Laser-assisted in situ keratomileusis (LASIK) is a widely used refractive surgical procedure that typically involves the ablation of the corneal stroma using 193 nm excimer laser pulses. While this method is effective in reshaping the cornea, alternative laser sources such as nanosecond Q-switched solid-state lasers at 266 nm (fourth harmonic of Nd:YAG) offer potential benefits, particularly regarding their influence on the chemical integrity of ablated tissue. Objective: This study aims to compare the chemical outcomes of corneal ablation using 193 nm excimer laser and 266 nm Q-switched laser pulses. Using Fourier Transform Infrared (FTIR) spectroscopy, we investigate the molecular composition and bonding differences in the treated tissue. Additionally, machine learning algorithms are employed to support spectral analysis and enhance the interpretation of chemical changes induced by each laser source. Conclusion: The experimental FTIR results reveal distinct chemical differences between the two ablation techniques. Although the 266 nm Q-switched laser exhibits slightly lower ablation rates, it demonstrates favorable chemical effects on the tissue structure. These findings, reinforced by machine learning analysis, contribute to a more comprehensive understanding of how laser parameters influence the biochemical outcomes of corneal ablation. | ||||
Keywords | ||||
Machine learning; laser ablation; cornea; solid-state laser; FTIR | ||||
Statistics Article View: 76 PDF Download: 48 |
||||