Detection of Keratoconus Progression Using Machine Learning

NCT06873399 | Active Not Recruiting FDA Device

• 1 other identifier: EK_24_158_VK
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief Summary

Keratoconus (KC) is a bilateral ocular disease characterized by progressive thinning and steepening of the cornea, usually in its inferotemporal region. The disease often occurs asymmetrically as one eye is more severely affected by the condition. The changes underlying KC lead to the generation of irregular astigmatism resulting in diminished visual acuity of the patients and can even lead to axial corneal scarring in advanced stages. The disease usually occurs in the second or third decade of life, but can develop at any age. KC is a complex condition involving environmental factors such as age, eye rubbing, contact lens use, atopy, sun exposure, hormones, toxins, as well as a genetic component. However, how these factors contribute to the disease is still unknown and intraindividual differences might exist. KC can be categorized into different forms based on the stage of the disease. In clinical KC, there are both topographic and slit lamp findings of the disease. The importance of corneal epithelial imaging in the diagnosis of keratoconus has been further demonstrated in several clinical studies. As new anterior segment optical coherence tomography (AS-OCT) devices provide more detailed measurements for instance of the corneal epithelium. This layer could therefore be an interesting marker for the prediction of KC progression and contribute to earlier diagnosis as well as better outcome of the disease. The aim of this retrospective study is therefore to determine whether different topographical and volumetric data, for instance epithelial thickness maps (ETM), can be reliably used to predict the progression of KC using a machine learning algorithm.

Conditions

Keratoconus; Machine Learning

Keywords

keratoconus; machine learning

Outcome Measures

Primary Outcomes (2)

• Sensitivity of the machine learning algorithm on the final test data set

  Sensitivity of the machine learning algorithm on the final test data set to differentiate between progressive/non-progressive eyes based on Kmax-change per year

  through study completion, one year

• Specificity of the machine learning algorithm on the final test data set

  Specificity of the machine learning algorithm on the final test data set to differentiate between progressive/non-progressive eyes based on Kmax-change per year

  through study completion, one year

Study Arms (2)

Progressive Keratoconus

Patients with progressive keratokonus, based on a one-year change in Kmax values

Diagnostic Test: MS-39

Non-progressive Keratoconus

Patients with non-progressive keratokonus, based on a one-year change in Kmax values

Diagnostic Test: MS-39

Interventions

MS-39 DIAGNOSTIC_TEST

The MS-39 (Costruzione Strumenti Oftalmici, Firenze, Italy) is a device for anterior segment analysis of the eye, which combines Placido disc corneal topography and high-resolution SD-OCT. The device provides information on pachymetry, elevation, curvature, and dioptric power of both corneal surfaces. To obtain corneal topography, 22 Placido disc rings are emanated from a laser emitted diode (LED) light source at 635 nanometres (nm). The central 10 millimetres of the anterior corneal surface are covered. Epithelial thickness maps are calculated for different sectors (central, paracentral inferior/superior/nasal/temporal).

Non-progressive Keratoconus; Progressive Keratoconus

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

Patients with keratoconus that had at least two measurement using the MS-39 device

You may qualify if:

• Patients with KC progression as defined dependent on Kmax/year:
• Kmax \< 48 Dioptres (D): \>0.5 D per year o
• Kmax 48.01-53 D: \>0.6 D per year o
• Kmax 53.01-58 D: \>0.8 D per year o
• Kmax \> 58 D: \>1.5 D per year - Non progressive group: Patients with stable KC (KC progression dependent on Kmax \< than the values described above/year)

You may not qualify if:

• Relevant other ophthalmic diseases that are likely to influence the measurement outcome like corneal scars or epithelial dystrophies
• Too few measurements/too short follow-up to define progression of KC

Sponsors & Collaborators

• Vienna Institute for Research in Ocular Surgery: lead

Study Sites (1)

Vienna Institute for Research in Ocular Surgery (VIROS)

Vienna, Vienna, 1140, Austria

Location

MeSH Terms

Conditions

Keratoconus

Condition Hierarchy (Ancestors)

Corneal Diseases; Eye Diseases

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Head of Department

Study Record Dates

• First Submitted: March 2, 2025
• First Posted: March 12, 2025
• Study Start: December 2, 2024
• Primary Completion: May 1, 2025
• Study Completion: May 1, 2025
• Last Updated: March 12, 2025

Record last verified: 2025-03

Locations

AU (1)