100
J. Eur. Opt. Society-Rapid Publ. 21, 10( 2025)
Figure 7. Direct comparison of the automated results to the manual results.( a, b) illustrate the comparison for the flap and( c) is an instance of the lenticule morphology.
approach ensures a reliable estimation by smoothing out local variations and potential noise in the calculations.
For thicknesses below 100 lm, larger deviations from the intended values were observed in the dataset. This discrepancy arises because both anterior and posterior intrastromal cuts have a certain width, which affects the performance of the peakfinder. Consequently, the estimated thickness may fluctuate by up to the average cut width, leading to variations in measurements for thinner thicknesses( Fig. 6). The two white lines per layer represent the boundaries of the laser tissue disruption, i. e. the vertical extent of tissue damaged by the irradiation. This disruption occurs where laser energy exceeds the breakdown threshold of the tissue. Bubbles are caused by localized plasma formation due to high-intensity laser pulses, leading to tissue disruption. The breakdown process scatters light significantly, making these regions appear as bright spots on the OCT images.
As a result, whether measurements are obtained manually or automatically, a positioning error of approximately 10 lm should be considered when comparing the measurements to the intended( thickness) values.
This positioning error adds up to StdDev calculated to present thickness values in proximity with the center. This error margin may vary with pulse energies exceeding 100 nJ.
Considering the proposed pipeline, the automated results align with the gold standard manual measurements, given correlation coefficients greater than 92 % as shown in Figure 7. This validation, crucial for establishing the reliability of our approach, highlights the automated system’ s ability to replicate manual measurements while offering significant advantages. Notably, automated measurements exhibit significantly less variation compared to manual measurements, suggesting improved precision and reduced inter-observer variability.
Furthermore, our approach offers additional insights beyond basic measurements. For instance, it enables the analysis of lenticule power( LP), as well as the optical transition zone and the incision angles.
The LPs were estimated and compared with the intended LPs in Figure 8a. WithaR 2 = 0.96, thevalues signify a correlation. However, the mild corrections( �2D and �4D) deviate more compared to the higher corrections. This observation may route back to the overall systematic errors associated with the tiny lenticule estimations. In Figure 8b, the powers associate with the larger planned diameters, and large powers usually correspond to the treatment planned with smaller diameters.
All intended treatments were planned with corrections for myopic or compound myopic astigmatism. Without a fully automated characterization approach, inferential analyses may not be achievable as addressed in [ 15 ].
The estimated LPs in Figure 8a determine approximately 0.5D less correction than intended. The observed discrepancy might be attributed to the diameter shortening by the BO routine, the ex vivo optical breakdown and measurements on porcine eyes [ 49 ].
Overall, despite the influence of the discussed factors, the estimated values still remain reasonably close to the intended. This demonstrates that the observed discrepancies are within an acceptable range.
The optimization algorithm, combined with higherorder Taylor’ s monomials, allows for a comprehensive capture of the posterior segments of lenticule cuts. This capability is particularly valuable as it enables the differentiation between the optical zone and the transition zone.
An example of transition zone determination has been shown in Figure 8b. On average, the transition zone could be determined in 8 lenticule cases as 382 lm ± 15lm versus the expected 400 lm. This small subset corresponds to the cases with strong astigmatism corrections which require the greatest bend in the transition zone.
To accurately characterize possible transition zones, Taylor’ s monomials of at least the 10th order should be fitted. Otherwise, the true shape of posterior cuts may not be properly reconstructed.
Incision angles for flap cuts can be determined using the same method. However, it is crucial to make continuous and complete flap cuts to ensure a precise fit.
An example of edge detection is shown in Figure 9c. Due to the small width of edge cuts, angle estimations may have an error margin of up to 10 °. For a subgroup with an intended edge cut of 135 °, the algorithm estimated