Original articleUse of a Support Vector Machine for Keratoconus and Subclinical Keratoconus Detection by Topographic and Tomographic Data
Section snippets
Materials and Methods
This was a retrospective case series study. Clinical data and corneal examinations were retrieved from clinical records at the Muscat Eye Laser Center (Muscat, Oman) and Studio Oculistico d'Azeglio (Bologna, Italy). The study was conducted in accordance with the ethical standards stated in the 1964 Declaration of Helsinki and approved by the local clinical research ethics committee with informed consent obtained.
Results
Overall, 3502 eyes were enrolled. According to the clinical diagnosis, they were classified as follows:
- 1
Keratoconus: 877 eyes of 451 patients (mean age, 34.8±12.6 years; range, 15–71 years).
- 2
Subclinical keratoconus: 426 eyes of 340 patients (mean age, 40.4±17.1 years; range, 15–65 years). This group included 229 eyes with early keratoconus and 197 eyes with suspect keratoconus.
- 3
Abnormal: 940 eyes of 486 patients (mean age, 43.6±13.6 years; range, 14–78 years).
- 4
Normal: 1259 eyes of 756 normal
Discussion
Preoperative screening of patients undergoing corneal refractive surgery requires correct identification of eyes with subclinical keratoconus because subjects with this condition are known to be at increased risk of developing iatrogenic ectasia.3, 4, 5, 6, 7 When no signs of keratoconus are detected in either eye, this task represents a challenge for the ophthalmologist, given the lack—by definition—of any clinical difference between normal eyes and eyes with subclinical keratoconus. The only
References (42)
Keratoconus
Surv Ophthalmol
(1998)Analysis of ectasia after laser in situ keratomileusis: risk factors
J Cataract Refract Surg
(2007)Risk factors for ectasia after LASIK [letter]
J Cataract Refract Surg
(2008)- et al.
KISA% index: a quantitative videokeratography algorithm embodying minimal topographic criteria for diagnosing keratoconus
J Cataract Refract Surg
(1999) - et al.
Automated keratoconus detection using the EyeSys videokeratoscope
J Cataract Refract Surg
(2000) - et al.
Changes in anterior and posterior corneal curvatures in keratoconus
Ophthalmology
(2000) - et al.
Corneal elevation indices in normal and keratoconic eyes
J Cataract Refract Surg
(2006) - et al.
Corneal-thickness spatial profile and corneal-volume distribution: tomographic indices to detect keratoconus
J Cataract Refract Surg
(2006) - et al.
Evaluation of Scheimpflug imaging parameters in subclinical keratoconus, keratoconus, and normal eyes
J Cataract Refract Surg
(2011) - et al.
Sensitivity and specificity of posterior corneal elevation measured by Pentacam in discriminating keratoconus/subclinical keratoconus
Ophthalmol
(2008)
Corneal volume, pachymetry, and correlation of anterior and posterior corneal shape in subclinical and different stages of clinical keratoconus
J Cataract Refract Surg
Pachymetric measurements with a new Scheimpflug photography–based system: intraobserver repeatability and agreement with optical coherence tomography pachymetry
J Cataract Refract Surg
Repeatability of automatic measurements by a new Scheimpflug camera combined to Placido topography
J Cataract Refract Surg
Keratoconus: classification scheme based on videokeratography and clinical signs
J Cataract Refract Surg
Role of Orbscan II in screening keratoconus suspects before refractive corneal surgery
Ophthalmology
Keratoconus: it is hard to define, but …
Am J Ophthalmol
Keratoconus and related noninflammatory corneal thinning disorders
Surv Ophthalmol
Complications of laser in situ keratomileusis: etiology, prevention, and treatment
J Refract Surg
Validation of the Ectasia Risk Score System for preoperative laser in situ keratomileusis screening
Am J Ophthalmol
Risk assessment for ectasia after corneal refractive surgery
Ophthalmology
Automated keratoconus screening with corneal topography analysis
Invest Ophthalmol Vis Sci
Cited by (182)
Optimized Artificial Intelligence for Enhanced Ectasia Detection Using Scheimpflug-Based Corneal Tomography and Biomechanical Data
2023, American Journal of OphthalmologyDetecting dry eye from ocular surface videos based on deep learning
2023, Ocular SurfaceComputer-aided diagnosis of keratoconus through VAE-augmented images using deep learning
2023, Scientific ReportsArtificial intelligence for detecting keratoconus
2023, Cochrane Database of Systematic Reviews
Manuscript no. 2012-78.
Financial Disclosure(s): The author(s) have made the following disclosure(s):
Francesco Versaci and Gabriele Vestri are employees of CSO srl.