KeratoconusNCT06873399
Detection of Keratoconus Progression Using Machine Learning
An observational study of Keratoconus and Machine Learning, run by Vienna Institute for Research in Ocular Surgery at 1 site in Austria.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Keratoconus, Machine Learning
- What is being tested?
- MS-39
- How many people
- 200 participants
- Where
- 1 siteAustria
- Study dates
- 2 Dec 2024to May 2025
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients with keratoconus that had at least two measurement using the MS-39 device
Who may be able to join
- Patients with KC progression as defined dependent on Kmax/year:
- o
- 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)
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
Austria1 site
Vienna Institute for Research in Ocular Surgery (VIROS)
Vienna, State of Vienna, 1140
About this study
Official title: Machine Learning Assisted Prediction of Keratoconus Progression Using Topographic and Volumetric Data: a Retrospective Study
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.
Read more
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
- Being tested
- MS-39
- Keywords
- keratoconus, machine learning
- Medical subject headings
- Keratoconus
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
MS-39Diagnostic 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).
| Study group | Role | What they receive |
|---|---|---|
| Progressive Keratoconus | Patients with progressive keratokonus, based on a one-year change in Kmax values Receives: Diagnostic Test: MS-39 | |
| Non-progressive Keratoconus | Patients with non-progressive keratokonus, based on a one-year change in Kmax values Receives: Diagnostic Test: MS-39 |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 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
Measured: through study completion, one year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Mar 2025
- Study start2 Dec 2024
- Primary completionMay 2025 (estimated)
- Study completionMay 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for KeratoconusStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Vienna Institute for Research in Ocular Surgery
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Prim. Prof. Dr. Oliver Findl, MBA, Vienna Institute for Research in Ocular Surgery
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06873399
- Sponsor's protocol ID
- EK_24_158_VK
- Status last verified
- Mar 2025
- First submitted
- 2 Mar 2025
- Last updated
- 12 Mar 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 12 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning Assisted Prediction of Keratoconus Progression Using Topographic and Volumetric Data: a Retrospective Study
- ClinicalTrials.gov ID
- NCT06873399
- Acronym / sponsor ID
- EK_24_158_VK
- Conditions
- Keratoconus, Machine Learning
- Keywords
- keratoconus, machine learning
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Vienna Institute for Research in Ocular SurgeryUniversity, hospital or other
- Study start
- 2 Dec 2024 (actual)
- Primary completion
- May 2025 (estimated)
- Study completion
- May 2025 (estimated)
- First posted
- 12 Mar 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Progressive Keratoconus | – | Diagnostic Test: MS-39Patients with progressive keratokonus, based on a one-year change in Kmax values |
| Non-progressive Keratoconus | – | Diagnostic Test: MS-39Patients with non-progressive keratokonus, based on a one-year change in Kmax values |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients with keratoconus that had at least two measurement using the MS-39 device
Key inclusion criteria (6 listed)
- Patients with KC progression as defined dependent on Kmax/year:
- o
- 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
Key exclusion criteria (2 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| MS-39The 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). | Diagnostic test | Non-progressive Keratoconus, Progressive Keratoconus |
Primary outcome
Sensitivity of the machine learning algorithm on the final test data set
Time frame: through study completion, one year
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
Specificity of the machine learning algorithm on the final test data set
Time frame: through study completion, one year
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Mar 2025.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Keratoconus and Machine Learning, run by Vienna Institute for Research in Ocular Surgery at 1 site in Austria.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 12 Mar 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Austria. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity of the machine learning algorithm on the final test data set (time frame: through study completion, one year). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Mar 2025.