Efficiency of an Algorithm Derived From Corneal Tomography Parameters to Distinguish Highly Susceptible Corneas to Ectasia From Healthy

NCT04313387 | Completed DMC

• 1 other identifier: 37232214.1.0000.5415
• Study Type: observational
• Target: 588
• Locations: 0 countries
• Sites: N/A

Brief Summary

The objective of this study was to identify and build an algorithm through an imaging process using a support vector machine (SVM) with the tomography variables of cases with, KC, highly susceptible corneas to ectasia (HSCE), and healthy corneas and to compare this algorithm to BAD-D (Belin\_Ambrosio Display) and PRFI (Pentacam Random Forest Index). The study included 148 eyes with KC, 351 with healthy corneas, and 88 eyes with HSCE.

Conditions

Keratoconus, Artificial Intelligence, Support Vector Machine

Keywords

keratoconus, ectasia, support vector machine, artificial intelligence

Outcome Measures

Primary Outcomes (1)

• CTMVI designed to screen patients prior to refractive surgery

  ROC curves of CTMVI comparing with BAD D, and PRFI

  january 2012 until january 2018

Study Arms (3)

Control group - Normal eyes (CG)

• Control group - Normal eyes (CG): 351 eyes without KC of 351 patients who underwent LASIK or photorefractive keratectomy (PRK), stable after at least 18 months of follow-up, without any changes in the posterior elevation at the 18-month Pentacam in relation to the preoperative exam (surgeries performed in 2012-2018). Our objective topographic criteria were: both eyes with a KISA% index of less than 60%, Kmax of 47.2 D or less, and I-S difference of less than 1.45 D. Because no truly established tomographic parameter(s)/cut-off(s) for differentiating normal from keratoconus suspect eyes exist, we adapted our classification for normal eyes to the recent publication by Ambrósio et al. by adding the criterion of "overall subjective normal topography and tomography examinations" based on the evaluation of experienced refractive surgeon (GCAJ). Only one eye was randomly selected for further statistical analysis.

Diagnostic Test: Corneal tomography multivariate index derived from a support vector machine (CTMVI).

Very assimetric ectasia with normal topography

• Very assimetric ectasia with normal topography group (VAE-NT G): 88 eyes of 88 patients with very asymmetric ectasia with normal topography (VAE-NT) in one eye and frank ectasia (VAE-E) in the fellow eye. The inclusion criteria followed previous studies (28, 32, 33) Eyes in this group with insufficient topographic findings to meet diagnostic criteria for keratoconus, and following features normal-appearing cornea on slit-lamp biomicroscopy, keratometry, retinoscopy. These cases were the less affected eye (fellow eye) of a keratoconic patient was included if the following criteria were met: KISA% index of less than 60%, I-S difference of less than 1.45 D, and Kmax of 47.2 D or less (ie, same topographic criteria as in normal eyes, except than in normal eyes, both eyes of the patient met the criteria). These patients can be considered with corneas highly susceptible to ectasia.

Diagnostic Test: Corneal tomography multivariate index derived from a support vector machine (CTMVI).

Keratoconus group (KCG)

• Keratoconus group (KCG): 148 patients (one eye each) with bilateral clinical KC. The KCG included one eye randomly selected from 148 patients with keratoconus; one eye was randomly included per patient to avoid selection bias related to the use of both eyes from the same patient. The inclusion criteria were the same as for VAE-E, except that both eyes of the patient met the ectasia criteria.

Diagnostic Test: Corneal tomography multivariate index derived from a support vector machine (CTMVI).

Interventions

Corneal tomography multivariate index derived from a support vector machine (CTMVI). DIAGNOSTIC_TEST

MATHEMATICAL ALGORITHM: To build the equation extracted from SVM, 58 variables were used, some of them were extracted from the spreadsheet.. After the construction of these 58 feature vectors (FV), an SVM-derived index was created, which was called the corneal tomography multivariate index derived from a support vector machine (CTMVI). Considering that each patient represents a point on a cartesian plane with 58 dimensions (each coordinate representing one of the 58 FV), the role of SVM is to find the hyperplane that best separates the CG, KCG, and VAE-NT G subjects. A hyperplane is algebraically described by a linear equation; in this case, there are 59 coefficients, 58 of which are related to the FV and one independent coefficient representing the bias (which is a possible parallel dislocation of a given hyperplane).

Control group - Normal eyes (CG); Keratoconus group (KCG); Very assimetric ectasia with normal topography

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Population served at an ophthalmological center in a city with an area covered by an estimated population of one million inhabitants. Patients who sought the service for refractive surgery or for the evaluation and treatment of keratoconus.

You may qualify if:

• Patients were considered to be very asymmetric (VAE-NT) if the diagnosis of ectasia was confirmed in one eye based on the previously described criteria and the fellow eye had a normal front surface curvature (topometric) map. Objective criteria for considering normal topography was applied for defining the cases of VAE-NT, including objective front surface curvature metrics derived from Pentacam. Normal topography was rigorously considered based on objective criteria (27, 28) of a maximum curvature Kmax (Steepest Front Keratometry) \<47.2 diopters, a paracentral inferior-superior (I-S value) asymmetry value at 6 mm (3-mm radii) \< 1.45, and keratoconus percentage index (KISA%) score \< 60 and (29). The cutoff point used to discriminate normal corneas and VAE-NT from KC corneas was the maximal posterior elevation (\< 29 µm). This cutoff point had been determined in a previous study, using the same instrument and the same setting. The posterior elevation map was displayed with a 5-mm color-coded scale, and maximal posterior elevation was measured manually using the cursor in the central 5 mm.

Sponsors & Collaborators

• Gildasio Castello de Almeida Junior: lead
• Fundação de Amparo à Pesquisa do Estado de São Paulo: collaborator

Related Publications (12)

• Motlagh MN, Moshirfar M, Murri MS, Skanchy DF, Momeni-Moghaddam H, Ronquillo YC, Hoopes PC. Pentacam(R) Corneal Tomography for Screening of Refractive Surgery Candidates: A Review of the Literature, Part I. Med Hypothesis Discov Innov Ophthalmol. 2019 Fall;8(3):177-203.

  PMID: 31598520 RESULT

• Luz A, Lopes B, Hallahan KM, Valbon B, Ramos I, Faria-Correia F, Schor P, Dupps WJ Jr, Ambrosio R Jr. Enhanced Combined Tomography and Biomechanics Data for Distinguishing Forme Fruste Keratoconus. J Refract Surg. 2016 Jul 1;32(7):479-94. doi: 10.3928/1081597X-20160502-02.

  PMID: 27400080 RESULT

• Yoo TK, Ryu IH, Lee G, Kim Y, Kim JK, Lee IS, Kim JS, Rim TH. Adopting machine learning to automatically identify candidate patients for corneal refractive surgery. NPJ Digit Med. 2019 Jun 20;2:59. doi: 10.1038/s41746-019-0135-8. eCollection 2019.

  PMID: 31304405 RESULT

• Lopes BT, Ramos IC, Salomao MQ, Guerra FP, Schallhorn SC, Schallhorn JM, Vinciguerra R, Vinciguerra P, Price FW Jr, Price MO, Reinstein DZ, Archer TJ, Belin MW, Machado AP, Ambrosio R Jr. Enhanced Tomographic Assessment to Detect Corneal Ectasia Based on Artificial Intelligence. Am J Ophthalmol. 2018 Nov;195:223-232. doi: 10.1016/j.ajo.2018.08.005. Epub 2018 Aug 9.

  PMID: 30098348 RESULT

• Smadja D, Touboul D, Cohen A, Doveh E, Santhiago MR, Mello GR, Krueger RR, Colin J. Detection of subclinical keratoconus using an automated decision tree classification. Am J Ophthalmol. 2013 Aug;156(2):237-246.e1. doi: 10.1016/j.ajo.2013.03.034. Epub 2013 Jun 7.

  PMID: 23746611 RESULT

• Steinberg J, Siebert M, Katz T, Frings A, Mehlan J, Druchkiv V, Buhren J, Linke SJ. Tomographic and Biomechanical Scheimpflug Imaging for Keratoconus Characterization: A Validation of Current Indices. J Refract Surg. 2018 Dec 1;34(12):840-847. doi: 10.3928/1081597X-20181012-01.

  PMID: 30540367 RESULT

• Awad EA, Abou Samra WA, Torky MA, El-Kannishy AM. Objective and subjective diagnostic parameters in the fellow eye of unilateral keratoconus. BMC Ophthalmol. 2017 Oct 6;17(1):186. doi: 10.1186/s12886-017-0584-2.

  PMID: 28985735 RESULT

• Ferreira-Mendes J, Lopes BT, Faria-Correia F, Salomao MQ, Rodrigues-Barros S, Ambrosio R Jr. Enhanced Ectasia Detection Using Corneal Tomography and Biomechanics. Am J Ophthalmol. 2019 Jan;197:7-16. doi: 10.1016/j.ajo.2018.08.054. Epub 2018 Sep 8.

  PMID: 30201341 RESULT

• Hashemi H, Beiranvand A, Yekta A, Maleki A, Yazdani N, Khabazkhoob M. Pentacam top indices for diagnosing subclinical and definite keratoconus. J Curr Ophthalmol. 2016 Mar 29;28(1):21-6. doi: 10.1016/j.joco.2016.01.009. eCollection 2016 Mar.

  PMID: 27239598 RESULT

• Ruiz Hidalgo I, Rodriguez P, Rozema JJ, Ni Dhubhghaill S, Zakaria N, Tassignon MJ, Koppen C. Evaluation of a Machine-Learning Classifier for Keratoconus Detection Based on Scheimpflug Tomography. Cornea. 2016 Jun;35(6):827-32. doi: 10.1097/ICO.0000000000000834.

  PMID: 27055215 RESULT

• Arbelaez MC, Versaci F, Vestri G, Barboni P, Savini G. Use of a support vector machine for keratoconus and subclinical keratoconus detection by topographic and tomographic data. Ophthalmology. 2012 Nov;119(11):2231-8. doi: 10.1016/j.ophtha.2012.06.005. Epub 2012 Aug 11.

  PMID: 22892148 RESULT

• Bae GH, Kim JR, Kim CH, Lim DH, Chung ES, Chung TY. Corneal topographic and tomographic analysis of fellow eyes in unilateral keratoconus patients using Pentacam. Am J Ophthalmol. 2014 Jan;157(1):103-109.e1. doi: 10.1016/j.ajo.2013.08.014. Epub 2013 Oct 25.

  PMID: 24452012 RESULT

MeSH Terms

Conditions

Keratoconus; Dilatation, Pathologic

Condition Hierarchy (Ancestors)

Corneal Diseases; Eye Diseases; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: PhD, MD

Study Record Dates

• First Submitted: March 15, 2020
• First Posted: March 18, 2020
• Study Start: January 1, 2012
• Primary Completion: January 1, 2018
• Study Completion: January 1, 2018
• Last Updated: March 18, 2020

Record last verified: 2020-03

Data Sharing

• IPD Sharing: Will not share