The Impact of Epithelial Remodeling on Surgical Techniques Used in Topography-guided Surface Ablation in Irregular Corneas

PURPOSE

To analyze the optical consequences of epithelial remodeling in irregular corneas and their impact on the choice of different surface ablation techniques.

METHODS

Anterior corneal and stromal surface topographies and epithelial thickness maps were analyzed in 24 eyes with irregular corneal optics. On two of the eyes, four different surface ablation techniques were simulated: (1) conventional anterior topography-guided photorefractive keratectomy (PRK), (2) transepithelial phototherapeutic keratectomy (PTK), (3) transepithelial anterior topography-guided PRK, and (4) stromal topography-guided PRK.

RESULTS

Stromal surface topographies showed higher keratometric values, astigmatism, asphericity, and corneal higher order aberrations compared to topographies of anterior corneas covered by epithelium. Transepithelial anterior topography-guided PRK and stromal topography-guided PRK both resulted in regularized stromal surface, transepithelial PTK achieved partial regularization corresponding to the smoothing effect of the epithelial remodeling, and conventional anterior topography-guided PRK delivered after epithelial removal resulted in residual stromal surface irregularities.

CONCLUSIONS

The difference in optical landscapes between the stromal and anterior surfaces in irregular corneas will represent a source of error when anterior topography-guided treatments are delivered on the deepithelialized stroma, as in conventional PRK. In contrast, anterior topography-guided ablations performed as transepithelial PRK and stromal topography-guided PRK delivered after epithelial removal address the full stromal irregularity, whereas transepithelial PTK alone may be used when topography-guided treatments are not possible. The authors conclude topography-guided PRK of irregular corneas should lead to significantly improved regularization only if it includes the effect of epithelial remodeling. [J Refract Surg. 2022;38(8):529-537.].

Mapping of corneal birefringence in thin and asymmetric keratoconus corneas with ultrahigh resolution polarization sensitive OCT

PURPOSE

To evaluate phase retardation (PR) across healthy, thin corneas (< 500 µm), asymmetric and bilateral KC.

SETTING

Narayana Nethralaya, Bangalore.

DESIGN

Observational, cross-sectional.

METHODS

There were four eye groups: healthy (group 1; n=10 eyes), thin corneas with no clinical disease (group 2; n=10 eyes), asymmetric KC (group 3; n=5 eyes) and clinical KC (group 4; n=15 eyes). All eyes were imaged with polarization sensitive OCT (PS-OCT), MS-39 and Corvis-ST. Using PS-OCT, the phase retardation (PR) was analysed in annular regions. The anterior (A-E) and Bowman's (E-B) wavefront aberrations, Epithelium Zernike indices (EZI), total corneal thickness, Corvis biomechanical index (CBI), total biomechanical index (TBI) and Belin-Ambrosio overall deviation index (BAD-D) were analysed.

RESULTS

Only the CBI, TBI, BAD-D, the A-E and E-B aberrations, EZI and total corneal thickness distributions of groups 1, 2 and 3 were similar (p>0.05) but not CCT (p<0.05). The PR distributions clearly showed that the eyes in groups 1, 2 and 3 had a normal corneal birefringence unlike group 4 eyes (p<0.05). The PR map was similar to the preferred orientations of collagen fibers seen in X-ray diffraction ex vivo studies of corneal stroma.

CONCLUSION

The PR distributions may eliminate the uncertainty associated with the stromal status of thin and asymmetric KC corneas. The group 2 and 3 eyes appeared as healthy due to normal corneal birefringence at the time of imaging and longitudinal follow-up of these eyes with PS-OCT may assist in early detection of onset of disease.

Universal architecture of corneal segmental tomography biomarkers for artificial intelligence-driven diagnosis of early keratoconus

AIMS

To develop a comprehensive three-dimensional analyses of segmental tomography (placido and optical coherence tomography) using artificial intelligence (AI).

METHODS

Preoperative imaging data (MS-39, CSO, Italy) of refractive surgery patients with stable outcomes and diagnosed with asymmetric or bilateral keratoconus (KC) were used. The curvature, wavefront aberrations and thickness distributions were analysed with Zernike polynomials (ZP) and a random forest (RF) AI model. For training and cross-validation, there were groups of healthy (n=527), very asymmetric ectasia (VAE; n=144) and KC (n=454). The VAE eyes were the fellow eyes of KC patients but no further manual segregation of these eyes into subclinical or forme-fruste was performed.

RESULTS

The AI achieved an excellent area under the curve (0.994), accuracy (95.6%), recall (98.5%) and precision (92.7%) for the healthy eyes. For the KC eyes, the same were 0.997, 99.1%, 98.7% and 99.1%, respectively. For the VAE eyes, the same were 0.976, 95.5%, 71.5% and 91.2%, respectively. Interestingly, the AI reclassified 36 (subclinical) of the VAE eyes as healthy though these eyes were distinct from healthy eyes. Most of the remaining VAE (n=104; forme fruste) eyes retained their classification, and were distinct from both KC and healthy eyes. Further, the posterior surface features were not among the highest ranked variables by the AI model.

CONCLUSIONS

A universal architecture of combining segmental tomography with ZP and AI was developed. It achieved an excellent classification of healthy and KC eyes. The AI efficiently classified the VAE eyes as 'subclinical' and 'forme-fruste'.

Simultaneous Topography-Guided PRK/CXL Versus Topography-Assisted PTK/CXL: 1-Year Prospective Outcomes in Keratoconic Eyes

PURPOSE

To compare 1-year visual and tomographic outcomes of topography-guided photorefractive keratectomy (TGPRK) and topography-assisted phototherapeutic keratectomy (TPTK) with corneal cross-linking (CXL).

METHODS

TGPRK and TPTK were performed in 72 eyes (68 patients) and 74 eyes (71 patients), respectively. Based on the TGPRK ablation plan, the eyes underwent TPTK where the theoretical minimum corneal thickness (MCT) after surgery was less than 400 µm. In the TGPRK group, the theoretical maximum ablation depth was 50 µm after epithelium removal. In TPTK, a decentered single-step PTK was performed only in the steepest anterior curvature zone and the stromal ablation depth was limited to 25 µm. After ablation, accelerated CXL was performed in the central 8-mm zone (9 mW/cm² for 10 minutes in "epithelium-off" mode) in both TGPRK and TPTK. The visual acuity and tomography were assessed.

RESULTS

Improvement in uncorrected (P = .73) and corrected (P = .66) distance visual acuity was similar between the two groups. However, TGPRK eyes had a greater decrease in keratometry, anterior defocus, and spherical aberration (P < .001) at the cost of greater ablation of tissue (P < .001). The median MCT decreased by 27 and 52.5 µm in the TPTK and TGPRK eyes, respectively. Both groups had similar decreases in anterior root mean square of lower (P = .10) and higher (P = .12) order aberrations.

CONCLUSIONS

Both TGPRK and TPTK improved visual acuity in the keratoconic eyes at 1 year of follow-up. However, TPTK removed less volume of tissue. Further, it could be an alternative to TGPRK if the theoretical stromal ablation exceeds 50 µm in thin keratoconic corneas. [J Refract Surg. 2021;37(8):562-569.].

Intraoperative Swept-Source OCT-Based Corneal Topography for Measurement and Analysis of Stromal Surface After Epithelial Removal

PURPOSE

To assess intraoperative stromal topography measurements using swept-source optical coherence tomography (OCT)-based topography/tomography after epithelial removal and to analyze the epithelial contribution to the corneal topography and optics.

METHODS

This was a prospective series of 22 eyes of 19 patients referred to receive phototherapeutic keratotomy (PTK) for treatment of recurrent corneal erosion and a control group of 22 virgin eyes. Swept-source OCT corneal topography/tomography was obtained immediately before and immediately after mechanical deepithelialization before PTK. Epithelial thickness maps were obtained before the surgery using spectral-domain OCT in the control group and as a reference in the group with anterior basement membrane dystrophy. Topographic and optical characteristics, including the curvature, astigmatism, asphericity, and higher order aberrations of the cornea before and after deepithelialization were compared, and their differences correlated with the measurements derived from the epithelial thickness maps.

RESULTS

Stromal topography measurements after deepithelialization were easily obtained and showed excellent repeatability. Assessment of corneal edema induced by deepithelialization revealed that it did not significantly affect the measured parameters. The stromal surface was steeper by 1.28 diopters, had higher with-the-rule astigmatism by 0.41 diopters, was more prolate, and had more higher order aberrations compared to the intact epithelialized corneal surface. These differences correlated well with the parameters derived from epithelial thickness maps.

CONCLUSIONS

Measurement of stromal topography using swept-source OCT immediately after mechanical deepithelialization may be a viable method in therapeutic refractive surgery, where stromal topography-guided ablation is needed. A significant epithelial contribution to anterior corneal topography and optics was confirmed. [J Refract Surg. 2021;37(7):484-492.].

Unique corneal tomography features of allergic eye disease identified by OCT imaging and artificial intelligence

The purpose of this study was to assess unique corneal tomographic parameters of allergic eye disease (AED) using optical coherence tomography (OCT) and artificial intelligence (AI). A total of 57 eyes diagnosed with AED were included. The curvature and aberrations of the air-epithelium (A-E) and epithelium-Bowman's layer (E-B) interfaces were calculated. Random forest AI models were built combing this data with the parameters of healthy, forme fruste keratoconus (FFKC) and KC eyes. The AI models were cross-validated with 3-fold random sampling. Each model was limited to 10 trees. The AI model incorporating both A-E and E-B parameters provided the best classification of AED eyes (area under the curve = 0.958, sensitivity = 80.7%, specificity = 98.5%, precision = 88.2%). Further, the E-B interface parameters provided the highest information gain in the AI model. A few AED eyes (n = 9) had tomography parameters similar to FFKC and KC eyes and may be at risk of progression to KC.