Investigation of How Corneal Densitometry Artefacts Affect the Imaging of Normal and Keratoconic Corneas

PURPOSE

To investigate corneal densitometry artefacts found in Pentacam Scheimpflug scans and their potential effect on assessing keratoconic (KC) corneas compared to normal (N) corneas.

METHODS

The current study utilises Pentacam data of 458 N eyes, aged 35.6 ± 15.8 (range 10-87), referred to as the "N group", and 314 KC eyes, aged 31.6 ± 10.8 (range 10-72), referred to as the "KC group", where densitometry data were extracted and analysed via a custom-built MATLAB code. Radial summations of the densitometry were calculated at diameters ranging from 0.5 mm to 5.0 mm. The minimum normalised radial summation of densitometry (NRSD) value and angle were determined at each diameter and then linked. KC cone locations and areas of pathology were determined, and a comparison between N and KC groups was carried out both within the averaged area of pathology and over the corneal surface.

RESULTS

Joining minimum NRSD trajectory points marked a clear distortion line pointing to the nasal-superior direction at 65° from the nasal meridian. The findings were found to be independent of eye laterality or ocular condition. Consistency was detected in the right and left eyes among both the N and KC groups. The location of the KC cone centre and the area of pathology were determined, and the densitometry output was compared both within the area of pathology and over the whole cornea. When the average densitometry was compared between N and KC eyes within the KC area of pathology, the N group recorded a 16.37 ± 3.15 normalised grey-scale unit (NGSU), and the KC group recorded 17.74 ± 3.4 NGSU (p = 0.0001). However, when the whole cornea was considered, the N group recorded 16.71 ± 5.5 NGSU, and the KC group recorded 15.72 ± 3.98 NGSU (p = 0.0467). A weak correlation was found between the Bad D index and NGSU when the whole measured cornea was considered (R = -0.01); however, a better correlation was recorded within the KC area of pathology (R = 0.21).

CONCLUSIONS

Nasal-superior artefacts are observed in the densitometry Pentacam maps, and analysis shows no significant differences in their appearance between N or KC corneas. When analysing KC corneas, it was found that the cone positions are mostly on the temporal-inferior side of the cornea, opposite to the densitometry artefact NRSD trajectory. The analysis suggests that the corneal densitometry artefacts do not interfere with the KC area of pathology as it reaches its extreme in the opposite direction; therefore, weighting the densitometry map to increase the contribution of the inferior-temporal cornea and decreasing that of the superior-nasal area would improve the classification or identification of KC if densitometry is to be used as a KC metric.

Multimodal diagnostics for keratoconus and ectatic corneal diseases: a paradigm shift

Different diagnostic approaches for ectatic corneal diseases (ECD) include screening, diagnosis confirmation, classification of the ECD type, severity staging, prognostic evaluation, and clinical follow-up. The comprehensive assessment must start with a directed clinical history. However, multimodal imaging tools, including Placido-disk topography, Scheimpflug three-dimensional (3D) tomography, corneal biomechanical evaluations, and layered (or segmental) tomography with epithelial thickness by optical coherence tomography (OCT), or digital very high-frequency ultrasound (dVHF-US) serve as fundamental complementary exams for measuring different characteristics of the cornea. Also, ocular wavefront analysis, axial length measurements, corneal specular or confocal microscopy, and genetic or molecular biology tests are relevant for clinical decisions. Artificial intelligence enhances interpretation and enables combining such a plethora of data, boosting accuracy and facilitating clinical decisions. The applications of diagnostic information for individualized treatments became relevant concerning the therapeutic refractive procedures that emerged as alternatives to keratoplasty. The first paradigm shift concerns the surgical management of patients with ECD with different techniques, such as crosslinking and intrastromal corneal ring segments. A second paradigm shift involved the quest for identifying patients at higher risk of progressive iatrogenic ectasia after elective refractive corrections on the cornea. Beyond augmenting the sensitivity to detect very mild (subclinical or fruste) forms of ECD, ectasia risk assessment evolved to characterize the inherent susceptibility for ectasia development and progression. Furthermore, ectasia risk is also related to environmental factors, including eye rubbing and the relational impact of the surgical procedure on the cornea.

Ocular manifestations of Chikungunya fever in the chronic phase

PURPOSE

To identify ocular manifestations in patients with Chikungunya fever in the chronic phase and describe their sociodemographic profile.

METHODS

Patients with serologic confirmation of Chikungunya infection were included in this transverse study. All subjects underwent a comprehensive ophthalmologic evaluation, including specific lacrimal function tests (tear break-up time test, Schirmer test, and lissamine green).

RESULTS

Overall, 64 eyes of 32 patients were evaluated. Most patients were women (71.9%), with the mean age of 50.0 ±13.7 years. The mean interval between serologic confirmation and the examination was 12.7 ±7.7 months. Twenty patients (62%) presented with dry eye. No statistically significant association was observed between dry eye and infection diagnosis time (p=0.5546), age (p=0.9120), sex (p=1.00), race (p=0.2269), arthralgia in acute infection (p=0.7930), retro-orbital pain (p=0.3066), and conjunctivitis (p=1.00).

CONCLUSION

Dry eye was the most prevalent manifestation observed. No signs of intraocular inflammation and affected visual acuity were observed.

The Role of Corneal Biomechanics for the Evaluation of Ectasia Patients

Purpose: To review the role of corneal biomechanics for the clinical evaluation of patients with ectatic corneal diseases. Methods: A total of 1295 eyes were included for analysis in this study. The normal healthy group (group N) included one eye randomly selected from 736 patients with healthy corneas, the keratoconus group (group KC) included one eye randomly selected from 321 patients with keratoconus. The 113 nonoperated ectatic eyes from 125 patients with very asymmetric ectasia (group VAE-E), whose fellow eyes presented relatively normal topography (group VAE-NT), were also included. The parameters from corneal tomography and biomechanics were obtained using the Pentacam HR and Corvis ST (Oculus Optikgeräte GmbH, Wetzlar, Germany). The accuracies of the tested variables for distinguishing all cases (KC, VAE-E, and VAE-NT), for detecting clinical ectasia (KC + VAE-E) and for identifying abnormalities among the VAE-NT, were investigated. A comparison was performed considering the areas under the receiver operating characteristic curve (AUC; DeLong’s method). Results: Considering all cases (KC, VAE-E, and VAE-NT), the AUC of the tomographic-biomechanical parameter (TBI) was 0.992, which was statistically higher than all individual parameters (DeLong’s; p < 0.05): PRFI- Pentacam Random Forest Index (0.982), BAD-D- Belin -Ambrosio D value (0.959), CBI -corneal biomechanical index (0.91), and IS Abs- Inferior-superior value (0.91). The AUC of the TBI for detecting clinical ectasia (KC + VAE-E) was 0.999, and this was again statistically higher than all parameters (DeLong’s; p < 0.05): PRFI (0.996), BAD-D (0.995), CBI (0.949), and IS Abs (0.977). Considering the VAE-NT group, the AUC of the TBI was 0.966, which was also statistically higher than all parameters (DeLong’s; p < 0.05): PRFI (0.934), BAD- D (0.834), CBI (0.774), and IS Abs (0.677). Conclusions: Corneal biomechanical data enhances the evaluation of patients with corneal ectasia and meaningfully adds to the multimodal diagnostic armamentarium. The integration of biomechanical data and corneal tomography with artificial intelligence data augments the sensitivity and specificity for screening and enhancing early diagnosis. Besides, corneal biomechanics may be relevant for determining the prognosis and staging the disease.

Biomechanical diagnostics of the cornea

Corneal biomechanics has been a hot topic for research in contemporary ophthalmology due to its prospective applications in diagnosis, management, and treatment of several clinical conditions, including glaucoma, elective keratorefractive surgery, and different corneal diseases. The clinical biomechanical investigation has become of great importance in the setting of refractive surgery to identify patients at higher risk of developing iatrogenic ectasia after laser vision correction. This review discusses the latest developments in the detection of corneal ectatic diseases. These developments should be considered in conjunction with multimodal corneal and refractive imaging, including Placido-disk based corneal topography, Scheimpflug corneal tomography, anterior segment tomography, spectral-domain optical coherence tomography (SD-OCT), very-high-frequency ultrasound (VHF-US), ocular biometry, and ocular wavefront measurements. The ocular response analyzer (ORA) and the Corvis ST are non-contact tonometry systems that provide a clinical corneal biomechanical assessment. More recently, Brillouin optical microscopy has been demonstrated to provide in vivo biomechanical measurements. The integration of tomographic and biomechanical data into artificial intelligence techniques has demonstrated the ability to increase the accuracy to detect ectatic disease and characterize the inherent susceptibility for biomechanical failure and ectasia progression, which is a severe complication after laser vision correction.