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Substituting r r = 2.46 lm, equation 11 yields approximately 3.47 lm which matches the expected uncertainty of the nominal resolution.
The primary objective of this work was to demonstrate the applicability of our proposed method. However, its feasibility and generalizability can potentially provide deeper insight into new measurements via another diagnostic device, incorporating another femtosecond laser and a different eye model. In light of that, we envisage further work that incorporates the SCHWIND MS-39 OCT system [ 74 ], the goat eye model and two different femtosecond lasers( SCHWIND ATOS and VisuMax 500). This investigation further supported our aim of developing a generalized approach with consistent performance across various combinations [ 68 ].
For instance, with the MS-39 instrument, each eye can be scanned across 12 full meridians. The application of multi-meridian scans( with a minimum of 3) would enhance the capability to fit Zernike polynomials or the Gatinel-Malet polynomials to the detected corneal heights [ 69, 75 – 77 ]. This advancement could significantly improve the determination of corneal properties, including wavefront representation and aberration analysis. While Taylor’ s monomials are effective for describing 2D slices, they do not support comprehensive inferences related to aberration or refraction, which are critical for advanced diagnostic and corrective applications. Integrating these polynomial fits could thus offer a more detailed and accurate assessment of corneal topography and optical performance.
Notably, our proposed method shows promise not only as a standalone solution but also as an annotation tool for training machine learning segmentation architectures, such as Image-to-Image translation models [ 78 ]. As part of future research, we envision integrating knowledge of corneal layer sequencing into our model, as outlined in [ 43 ].
These findings have direct applications in the design and calibration of laser systems. Also, it facilitates assessing the repeatability and reproducibility( accuracy and precision) of laser-generated corneal substructures.
5 Conclusion
Intrastromal peri-operative( intra-OP) segmentations allow for precise measurement and visualization of the cuts made by the laser. This process ensures that the intended refractive corrections are accurately implemented.
We proposed a generalized routine to accomplish intrastromal flap and lenticule segmentation with an accuracy comparable with the manual expert level segmentations. Our method also characterizes the geometry of perioperative intrastromal flap and lenticule cuts. This is particularly valuable for ultra-thin flaps and lenticules, where manual measurements can be highly variable and challenging. Additionally, our approach exceeds the accuracy of manual measurements through complex characterizations where user-based marking can be significantly doubted. Specifically, for the purpose of evaluational( comparison) measurements, through different refractive correction systems, an automated approach would essentially substitute manual measurements.
The proposed computational algorithm significantly accelerates characterization tasks. It reduces the processing time from several minutes to just a few seconds and maintains the accuracy and solution uniqueness. After finetuning the parameters over a small batch, the algorithm runs unsupervised across the dataset.
Our approach can facilitate feedback assessment routines to evaluate the peri-operative situations. It also can be used during( or ex-situ of) a refractive surgery treatment to reflect the influence of versatile parameters on the cuts’ quality.
Our effort was concentrated on demonstrating the potential of the proposed work, particularly its ability to achieve comprehensive intrastromal characterization. This capability, to the best of our knowledge, has been showcased for the first time. Consequently, statistical analysis of subgroups was not prioritized. Our primary goal was not to derive specific clinical findings or conclusions, but rather to establish the generalizability and robustness of our approach.
Acknowledgments
Dr. Shwetabh Verma from SCHWIND eye-tech-solutions and Francesco Versaci from CSO, Italy are acknowledged for the handling and preparing data.
Funding This research received no external funding.
Conflicts of internet The authors have nothing to disclose.
Data availability statement
Thesourcecodesupportingthefindings of this study are available from the corresponding author upon reasonable request. Data supporting the findings of this article are not publicly available due to clinical restrictions.
Author contribution statement
Conceptualization, S. A. M. and K. D.; Methodology, M. M.; Software, M. M., F. B; Experiment, A. P. L. K; Validation, M. M., S. A. M; Investigation, M. M., A. P.; Data Curation, M. M.; Writing – Original Draft Preparation, M. M.; Writing – Review Editing, M. M., S. A. M.; Visualization, M. M.; Supervision, K. D. and S. A. M; Project Administration, A. P.
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