Dynamic topography analysis of the cornea and its application to the diagnosis of keratoconus

Li, Xue Fei; Luo, Sheng Long; Wang, Zheng; Miao, Yuan Yuan; Zhu, ManMan; Zheng, Xiang; Luo, Guo Xin; Bao, Fang Jun; Chen, Shi Hao; Wang, Jun Jie

doi:10.1016/j.compbiomed.2023.106800

Cited by 2 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For such cases, it would have been convenient to spend more time without glasses, but the patients' vision without glasses was too low. In the topographic image, curvature values were found in the central area close to 63.00 D and 300 mm of curvature [39,40]. This is classified as grade IV keratoconus according to the Amsler Krumeich scale, and it produces high corneal eccentricity in areas of ectasia [40].…”

Section: Discussionmentioning

confidence: 95%

See 1 more Smart Citation

Biomechanical Study of the Eye with Keratoconus-Type Corneal Ectasia Using a 3D Geometric Model

Sánchez-Real,

Otuna-Hernández,

Fajardo-Cabrera

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Keratoconus is an eye disease that distorts the shape of the cornea. This study aimed to analyze the effect of an increase in intraocular pressure applied to eyes with different severity of keratoconus disease using patient-specific models. Finite element models of the normal eye, eye with keratoconus, and eye with keratoglobus were constructed. The loading conditions considered the intraocular pressure increment as well as their physiological intraocular pressure. The analysis was performed with distinct materials for normal and keratoconic eyes. The finite element analysis revealed differences in the three models in terms of their deformation and maximum principal stress, and differences were observed in corneal curvature and thickness. These findings could enhance research in the biomechanical area, leading to more successful treatment options and a more individualized approach in the field of practical ophthalmology. Further investigation with larger sample sizes and more precise data on eye material would allow us to evaluate whether these disparities could inform the diagnosis of keratoconus.

show abstract

Section: Discussionmentioning

confidence: 95%

“…In the topographic image, curvature values were found in the central area close to 63.00 D and 300 mm of curvature [39,40]. This is classified as grade IV keratoconus according to the Amsler Krumeich scale, and it produces high corneal eccentricity in areas of ectasia [40].…”

Section: Discussionmentioning

confidence: 95%

Biomechanical Study of the Eye with Keratoconus-Type Corneal Ectasia Using a 3D Geometric Model

Sánchez-Real,

Otuna-Hernández,

Fajardo-Cabrera

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Evidence of Corvis ST parameters in pediatric KC and SKC is still lacking. ( Li et al, 2023 ). SP A1 is a novel stiffness parameter that is defined as resultant pressure divided by deflection at the first applanation, and the KC eye tends to have a smaller value that represents a weaker cornea.…”

Section: Discussionmentioning

confidence: 99%

Machine learning analysis with the comprehensive index of corneal tomographic and biomechanical parameters in detecting pediatric subclinical keratoconus

Ren,

Yang,

et al. 2023

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Background: Keratoconus (KC) occurs at puberty but diagnosis is focused on adults. The early diagnosis of pediatric KC can prevent its progression and improve the quality of life of patients. This study aimed to evaluate the ability of corneal tomographic and biomechanical variables through machine learning analysis to detect subclinical keratoconus (SKC) in a pediatric population.Methods: Fifty-two KC, 52 SKC, and 52 control pediatric eyes matched by age and gender were recruited in a case-control study. The corneal tomographic and biomechanical parameters were measured by professionals. A linear mixed-effects test was used to compare the differences among the three groups and a least significant difference analysis was used to conduct pairwise comparisons. The receiver operating characteristic (ROC) curve and the Delong test were used to evaluate diagnostic ability. Variables were used in a multivariate logistic regression in the machine learning analysis, using a stepwise variable selection to decrease overfitting, and comprehensive indices for detecting pediatric SKC eyes were produced in each step.Results: PE, BAD-D, and TBI had the highest area under the curve (AUC) values in identifying pediatric KC eyes, and the corresponding cutoff values were 12 μm, 2.48, and 0.6, respectively. For discriminating SKC eyes, the highest AUC (95% CI) was found in SP A1 with a value of 0.84 (0.765, 0.915), and BAD-D was the best parameter among the corneal tomographic parameters with an AUC (95% CI) value of 0.817 (0.729, 0.886). Three models were generated in the machine learning analysis, and Model 3 (y = 0.400*PE + 1.982* DA ratio max [2 mm]−0.072 * SP A1−3.245) had the highest AUC (95% CI) value, with 90.4% sensitivity and 76.9% specificity, and the cutoff value providing the best Youden index was 0.19.Conclusion: The criteria of parameters for diagnosing pediatric KC and SKC eyes were inconsistent with the adult population. Combined corneal tomographic and biomechanical parameters could enhance the early diagnosis of young patients and improve the inadequate representation of pediatric KC research.

show abstract

Dynamic topography analysis of the cornea and its application to the diagnosis of keratoconus

Cited by 2 publications

References 45 publications

Biomechanical Study of the Eye with Keratoconus-Type Corneal Ectasia Using a 3D Geometric Model

Biomechanical Study of the Eye with Keratoconus-Type Corneal Ectasia Using a 3D Geometric Model

Machine learning analysis with the comprehensive index of corneal tomographic and biomechanical parameters in detecting pediatric subclinical keratoconus

Contact Info

Product

Resources

About