Clinical trial NCT06873399: Detection of Keratoconus Progression Using Machine Learning
Official title: Machine Learning Assisted Prediction of Keratoconus Progression Using Topographic and Volumetric Data: a Retrospective Study
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Keratoconus
Machine Learning
Keywords: keratoconus
machine learning
Lead sponsor: Vienna Institute for Research in Ocular Surgery
Sponsor class: OTHER
First posted: 2025-03-12
Enrollment: 200
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with keratoconus that had at least two measurement using the MS-39 device

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.


Eligibility criteria: Inclusion Criteria:

* Patients with KC progression as defined dependent on Kmax/year:

o

1. Kmax \< 48 Dioptres (D): \>0.5 D per year o
2. Kmax 48.01-53 D: \>0.6 D per year o
3. Kmax 53.01-58 D: \>0.8 D per year o
4. 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)

Exclusion Criteria:

* 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

Primary outcomes:
measure: Sensitivity of the machine learning algorithm on the final test data set
timeFrame: through study completion, one year
measure: Specificity of the machine learning algorithm on the final test data set
timeFrame: through study completion, one year
Secondary outcomes:
Other outcomes:
Locations:
city: Vienna
country: Austria
facility: Vienna Institute for Research in Ocular Surgery (VIROS)
Study officials:
References: