Diagnosis of Subclinical Keratoconus with a Combined Model of Biomechanical and Topographic Parameters

Diagnostic value of corneal optical densitometry in keratoconus

PURPOSE

To investigate the clinical significance of the correlation between optical densitometry and both biomechanical and morphological parameters in keratoconus (KC) and to verify the diagnostic value of optical densitometry in KC.

METHOD

This cross-sectional study included 436 eyes of 295 patients with KC. Corneal optical densitometry, morphological parameters and biomechanical parameters were measured. Spearman's correlation analysis was employed to investigate the association between optical densitometry and both biomechanical and morphological parameters.

RESULT

Optical densitometry of the anterior (0-2 mm and 2-6 mm), central (0-2 mm), posterior (2-6 mm) and total (2-6 mm) layers correlated positively with SPA1, while the posterior layer (0-2 mm) correlated negatively. Optical densitometry of the anterior layers 2-6 mm, 6-10 mm, and the central layer 6-10 mm negatively affected AL1, while the posterior layer 0-2 mm positively affected it. Optical densitometry of the anterior, central, and posterior layers 0-2 mm and 2-6 mm positively influenced the morphological parameters K1F, K2F, KmF and the absolute values of K1B, K2B, KmB. Optical densitometry of the center (0-2 mm) and posterior (2-6 mm) layers negatively influenced TCT. Optical densitometry of the anterior (0-2 mm and 2-6 mm), center (0-2 mm), posterior (2-6 mm) and total (2-6 mm) layers correlated positively with ACE and PCE, whereas the posterior layer (0-2 mm) correlated negatively.

CONCLUSION

Optical densitometry was correlated with biomechanical and morphological parameters in keratoconus, suggesting its potential as a diagnostic indicator for assessing keratoconus progression and treatment efficacy.

Corneal biomechanics in early diagnosis of keratoconus using artificial intelligence

Keratoconus is a blinding eye disease that affects activities of daily living; therefore, early diagnosis is crucial. Great efforts have been made toward an early diagnosis of keratoconus. Recent studies have shown that corneal biomechanics is associated with the occurrence and progression of keratoconus. Hence, detecting changes in corneal biomechanics may provide a novel strategy for early diagnosis. However, an early keratoconus diagnosis remains challenging due to the subtle and localized nature of its lesions. Artificial intelligence has been used to help address this problem. Herein, we reviewed the literature regarding three aspects of keratoconus (keratoconus, early keratoconus, and keratoconus grading) based on corneal biomechanical properties using artificial intelligence. Furthermore, we summarized the current research progress, limitations, and possible prospects.

Corneal biomechanics in normal and subclinical keratoconus eyes

BACKGROUND

The diagnosis of keratoconus, as the most prevalent corneal ectatic disorder, at the subclinical stage gained great attention due to the increased acceptance of refractive surgeries. This study aimed to assess the pattern of the corneal biomechanical properties derived from Corneal Visualization Scheimpflug Technology (Corvis ST) and evaluate the diagnostic value of these parameters in distinguishing subclinical keratoconus (SKC) from normal eyes.

METHODS

This prospective study was conducted on 73 SKC and 69 normal eyes. Subclinical keratoconus eyes were defined as corneas with no clinical evidence of keratoconus and suspicious topographic and tomographic features. Following a complete ophthalmic examination, topographic and tomographic corneal assessment via Pentacam HR, and corneal biomechanical evaluation utilizing Corvis ST were done.

RESULTS

Subclinical keratoconus eyes presented significantly higher Deformation Amplitude (DA) ratio, Tomographic Biomechanical Index (TBI), and Corvis Biomechanical Index (CBI) rates than the control group. Conversely, Ambrósio Relational Thickness to the Horizontal profile (ARTh), and Stiffness Parameter at the first Applanation (SPA1) showed significantly lower rates in SKC eyes. In diagnosing SKC from normal eyes, TBI (AUC: 0.858, Cut-off value: > 0.33, Youden index: 0.55), ARTh (AUC: 0.813, Cut-off value: ≤ 488.1, Youden index: 0.58), and CBI (AUC: 0.804, Cut-off value: > 0.47, Youden index: 0.49) appeared as good indicators.

CONCLUSIONS

TBI, CBI, and ARTh parameters could be valuable in distinguishing SKC eyes from normal ones.

Detection ability of corneal biomechanical parameters for early diagnosis of ectasia

PURPOSE

To assess the detection ability of corneal biomechanical parameters for early diagnosis of ectasia.

METHODS

This retrospective descriptive-analytical study included 134 normal eyes (control group) from 134 healthy subjects and 128 eyes with asymmetric contralateral corneal ectasia with normal topography (ACE-NT, study group) from 128 subjects with definite keratoconus in the opposite eye. Placido-disk-based corneal topography with TMS-4, Scheimpflug corneal tomography with Pentacam HR, and corneal biomechanical assessment with Corvis ST and ocular response analyzer (ORA) were performed. A general linear model was used to compare Corvis ST and ORA biomechanical parameters between groups, while central corneal thickness (CCT) and biomechanically corrected intraocular pressure (bIOP) were considered covariates. Receiving operator sensitivity curve (ROC) analysis was used to determine the cut-off point with the highest sensitivity and specificity along with the area under the curve (AUC) for each parameter.

RESULT

All parameters of Corvis ST and ORA showed a statistically significant difference between the two groups except for the first (P = 0.865) and second (P = 0.226) applanation lengths, and deformation amplitude (P = 0.936). The discriminative analysis of corneal biomechanical showed that the highest accuracy for the classic, new, and combined parameters of Corvis ST was related to HCR (AUC: 0.766), IR & DAR (0.846), and TBI (0.966), respectively. Using ORA, the corneal resistance factor (0.866) had a higher detection ability than corneal hysteresis (0.826).

CONCLUSIONS

TBI has the best accuracy and the highest effect size for differential diagnosis of normal from ACE-NT eyes with a cut-off point of 0.24.

Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications

Artificial intelligence (AI) technology is promising in the field of healthcare. With the developments of big data and image-based analysis, AI shows potential value in ophthalmology applications. Recently, machine learning and deep learning algorithms have made significant progress. Emerging evidence has demonstrated the capability of AI in the diagnosis and management of anterior segment diseases. In this review, we provide an overview of AI applications and potential future applications in anterior segment diseases, focusing on cornea, refractive surgery, cataract, anterior chamber angle detection, and refractive error prediction.

Comprehensive Assessment of Corvis ST Biomechanical Indices in Normal and Keratoconus Corneas with Reference to Corneal Enantiomorphism

The aim of this study was to assess Corvis ST biomechanical indices in reference to corneal enantiomorphism. In a prospective observational cohort study, 117 eyes from 63 patients with normal or keratoconus corneas were assessed by three independent observers. In the control group (n = 62), no significant differences were observed between the three observers for all indices. The best reproducibility was obtained with pachymetry and the weakest with CBI. All indices but CBI and arc length featured COV < 10%. All indices except the PD and SSI correlated with pachymetry; all but Rad correlated with IOP. The comparison of the thinnest with the thickest corneas showed no significant differences for any index except pachymetry. In the keratoconus group (n = 55), loss of corneal enantiomorphism was confirmed for all indices except the arc length, velocity, and PD. Significant differences between both groups were found for all indices, even after adjustment for pachymetry and intraocular pressure. The CBI featured the best accuracy (92%), sensitivity (91%), and graphical relevance for keratoconus diagnosis. However, its reproducibility was weak in normal corneas and was strongly dependent on corneal thickness. The SSI was independent of corneal thickness, highly reproducible, and provided the expected enantiomorphism characteristics in both groups, making it a relevant biomarker of biomechanical corneal behavior.

Clinical Applications of Artificial Intelligence in Glaucoma

Ophthalmology is one of the major imaging-intensive fields of medicine and thus has potential for extensive applications of artificial intelligence (AI) to advance diagnosis, drug efficacy, and other treatment-related aspects of ocular disease. AI has made impressive progress in ophthalmology within the past few years and two autonomous AI-enabled systems have received US regulatory approvals for autonomously screening for mid-level or advanced diabetic retinopathy and macular edema. While no autonomous AI-enabled system for glaucoma screening has yet received US regulatory approval, numerous assistive AI-enabled software tools are already employed in commercialized instruments for quantifying retinal images and visual fields to augment glaucoma research and clinical practice. In this literature review (non-systematic), we provide an overview of AI applications in glaucoma, and highlight some limitations and considerations for AI integration and adoption into clinical practice.

Analysis of the diagnostic accuracy of Belin/Ambrósio Enhanced Ectasia and Corvis ST parameters for subclinical keratoconus

OBJECTIVE

To investigate the diagnostic accuracy of the parameters in the Belin/Ambrósio Enhanced Ectasia Display built in Pentacam, which is designed for the screening of subclinical keratoconus (SKC) built in Pentacam, and the parameters in Corneal visualization Scheimpflug technology (Corvis ST).

METHODS

A retrospective study: The fellow eyes of unilateral keratoconus cases were diagnosed with SKC. Patients presented to Shanxi Eye Hospital with SKC from October 2020 to November 2021 were included as the SKC group, and myopic patients undergoing corneal refractive surgery at the Refractive Surgery Department in our hospital within the same period were included as the control group. The Belin/Ambrósio and Corvis ST parameters were extracted from the system and analyzed using independent samples t test. Receiver operating curves (ROCs) were also created to test the diagnostic accuracy of each parameter.

RESULTS

There were 70 patients (70 eyes) in the SKC group and 137 patients (137 eyes) in the control group. For Corvis ST parameters, Radius (P = 0.021), PachySlope (P = 0.040), SP-A1 (P = 0.002), A2 Deformation Amp. (P = 0.028), A2 Deflection Length (P < 0.001), Max ICR (P = 0.005), DA Ratio Max (1 mm) (P = 0.023), IR (P = 0.016), CBI (P = 0.003) and TBI (P < 0.001) were statistically different between the two groups. For Belin/Ambrósio parameters, PPI min. Axis, ART min, ART max, ART avg, Pachy min, Front K2, Astig, BAD-Df, BAD-Db, BAD-Dp, BAD-Dt, BAD-Da, BAD-D, PPI min, PPI max, PPI max. Axis, PPI avg and Dist.Apex-Thin.Loc. were significantly different between the two groups (all p < 0.001). TBI and BAD-D showed the best diagnostic accuracy, with AUCs of 0.944 and 0.965, respectively.

CONCLUSIONS

Some Belin/Ambrósio and Corvis ST parameters differed between SKC eyes and eyes with normal cornea. TBI and BAD-D showed the ideal diagnostic performance for SKC. In clinical practice, conventional corneal topography could not be replaced by Corvis ST.

Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques

(1) Background: Keratoconus is a non-inflammatory corneal disease characterized by gradual thinning of the stroma, resulting in irreversible visual quality and quantity decline. Early detection of keratoconus and subsequent prevention of possible risks are crucial factors in its progression. Random forest is a machine learning technique for classification based on the construction of thousands of decision trees. The aim of this study was to use the random forest technique in the classification and prediction of subclinical keratoconus, considering the metrics proposed by Pentacam and Corvis. (2) Methods: The design was a retrospective cross-sectional study. A total of 81 eyes of 81 patients were enrolled: sixty-one eyes with healthy corneas and twenty patients with subclinical keratoconus (SCKC): This initial stage includes patients with the following conditions: (1) minor topographic signs of keratoconus and suspicious topographic findings (mild asymmetric bow tie, with or without deviation; (2) average K (mean corneal curvature) < 46, 5 D; (3) minimum corneal thickness (ECM) > 490 μm; (4) no slit lamp found; and (5) contralateral clinical keratoconus of the eye. Pentacam topographic and Corvis biomechanical variables were collected. Decision tree and random forest were used as machine learning techniques for classifications. Random forest performed a ranking of the most critical variables in classification. (3) Results: The essential variable was SP A1 (stiffness parameter A1), followed by A2 time, posterior coma 0°, A2 velocity and peak distance. The model efficiently predicted all patients with subclinical keratoconus (Sp = 93%) and was also a good model for classifying healthy cases (Sen = 86%). The overall accuracy rate of the model was 89%. (4) Conclusions: The random forest model was a good model for classifying subclinical keratoconus. The SP A1 variable was the most critical determinant in classifying and identifying subclinical keratoconus, followed by A2 time.