KeratoconusNCT06798779
Predicting Manifest Astigmatism in Keratoconus Patients.
An observational study of Keratoconus and Keratoconus, Artificial Intelligence, Support Vector Machine, run by Royal Devon and Exeter NHS Foundation Trust at 1 site in United Kingdom.
Trial summary
The cornea of the human eye has several functions. It is transparent to allow light into the eye and its shape focuses the incoming light onto the retina. The cornea is responsible for two-thirds of this focusing, while the human lens accounts for the remaining third. Read more about this study
- Who is it for?
- Ages 16 years and older
- Conditions the study is for
- Keratoconus, Keratoconus, Artificial Intelligence, Support Vector Machine
- What is being tested?
- Glasses and Corneal topography
- Type of treatment
- Medical device
- How many people
- 800 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 1 Jun 2024to 31 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 16 years and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: - Keratoconus patients at the West of England Eye Unit in Exeter, United Kingdom
Who may be able to join
- All patients with a diagnosis of keratoconus who have had topography performed at the West of England Eye Unit with contemporaneous manifest refraction.
Who cannot join
- Poor quality topography scans.
- History of ocular surgery.
- Age <16 years.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Predicting Manifest Astigmatism in Keratoconus Patients: A Machine Learning Approach Using Corneal Topography.
The cornea of the human eye has several functions. It is transparent to allow light into the eye and its shape focuses the incoming light onto the retina. The cornea is responsible for two-thirds of this focusing, while the human lens accounts for the remaining third.
Keratoconus is a condition with onset in the second to third decades of life, where the cornea warps into an irregular shape. This irregularity reduces vision due to blurring of the image focused on the retina. Only partial improvement is achievable with traditional glasses because the corneal shape becomes irregular.
The glasses prescriptions of patients with keratoconus often differ significantly from the measurements obtained from the cornea in a clinical setting. The predictability of the magnitude and variability of this disparity is poorly understood. As a result, determining the optimal glasses prescription for achieving the best vision correction often involves a time-consuming trial-and-error approach. Improved predictability could reduce the time required to identify the optimal glasses prescription, thereby increasing productivity. For surgical patients, better predictability would enable surgeons to select lenses that provide superior vision outcomes after treatment. In the optometry clinic at the West of England Eye Unit, a database of over 800 patients with glasses prescriptions and corresponding corneal scans has been compiled by the investigators. This is a sufficient dataset to train and assess the prediction accuracy of machine learning models (AI) of glasses measurements using corneal scan parameters as predictor variables.
Read the detailed description
Keratoconus is one of the leading causes of visual impairment worldwide. Its main therapeutic options include corneal cross-linking, intracorneal ring segments, penetrating keratoplasty, and lamellar keratoplasty.
Keratoconus is a progressive condition with onset in childhood, resulting in increasing irregularity of corneal shape. The cornea's role is to refract incoming light to focus it onto the retina to produce a sharp image. Irregularity of the cornea degrades this image. Keratoconus develops throughout the second and third decades of life and usually stabilises in the fourth decade. It leads to progressive vision loss. When detected early, progression can be halted or stabilised with collagen cross-linking treatment. Once the condition progresses to impair the quality of vision, rigid contact lenses or surgical procedures are often required for correction. Spectacles and soft contact lenses are frequently inadequate due to the cornea's irregular shape in keratoconus.
Surgical approaches to keratoconus that do not involve corneal transplantation include implantable collamer lenses (ICL) or toric intraocular lenses (tIOL). There is little published evidence on the use of these approaches, and no consensus exists regarding the extent of astigmatism to correct or the proper axis of astigmatism to select.
In addition to lower-order aberrations (such as those correctable by spectacles, including short-sightedness, long-sightedness, and astigmatism), higher-order aberrations are present, which cannot be corrected with glasses. Due to irregular corneal astigmatism and higher-order aberrations, accurate refraction is often challenging in patients with keratoconus. Eyes with keratoconus exhibit approximately five to six times more higher-order aberrations than normal eyes, particularly vertical coma. The manifest refractive cylinder (the astigmatism component in a glasses prescription) includes both regular and irregular astigmatism. Additionally, the axis of astigmatism in the prescription often differs significantly from the topometric axis derived from three-dimensional corneal imaging. In managing astigmatism for suitable patients, such as those receiving implantable collamer lenses (ICL) or toric intraocular lenses (tIOL), it is crucial to better understand the shape factors contributing to manifest refraction. Improved understanding can help neutralise post-operative refraction more effectively and achieve greater predictability. For non-surgical patients, determining the optimal glasses prescription to achieve the best vision correction often involves a time-consuming trial-and-error process. Improved predictability could reduce the time required to identify the optimal prescription, thereby enhancing productivity and patient care.
The aim of this study is to use data already collected from more than 800 patients to compare topographic elements (three-dimensional modelling of corneal shape), such as anterior and posterior curvatures and higher-order aberrations, to subjective manifest astigmatism (the amount of astigmatism in the glasses prescription) in keratoconic eyes. The impact of keratoconus severity on disparities between these methods will also be evaluated.
- Being tested
- Glasses, Corneal topography
- Keywords
- Keratoconus, Machine learning, Astigmatism, AI, Corneal topography, Regression, Glasses, Cylinder, Clinder axis, Sphere, Prediction
- Medical subject headings
- Keratoconus, Astigmatism
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
GlassesDevice
The patients will have had their vision corrected with glasses that may contain both spherical and cylindrical corrections. The glasses prescription that offers the best subjective correction of vision are being recorded.
Corneal topographyDiagnostic test
This is a scan of the cornea (front of the eye). This gives us information on how the cornea is refracting light for the patient.
What is being measured
The primary outcome is the main question the study is designed to answer.
The root mean squared error (RMSE) of the machine learning model's predictions for the patient's manifest astigmatism.
The primary outcome measure will be derived from the machine learning regression analysis, with demographic and keratometry values serving as predictor variables (X) and manifest astigmatism as the outcome variable (Y).
For each patient, the earliest Pentacam scan recorded during the study period, for which a manifest astigmatism measurement exists on the same day, will be used.
Measured: Baseline
Secondary outcomes (1)
Shapley values for each predictor variable in the best-performing machine learning model.
Shapley values provide insights into the importance of each variable in the model's predictions. This approach helps identify the relationships between corneal topographic factors and manifest astigmatism in keratoconus patients. These values will be found during the machine learning analysis.
For each patient, the earliest Pentacam scan recorded during the study period, for which a manifest astigmatism measurement exists on the same day, will be used.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jan 2025
- Study start1 Jun 2024
- Primary completion31 May 2025 (estimated)
- Study completion31 May 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- 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
- Case only
- Time perspective
- Retrospective
- Enrolment
- 800 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Royal Devon and Exeter NHS Foundation Trust
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Harry Roberts, Royal Devon and Exeter NHS Foundation Trust
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06798779
- Sponsor's protocol ID
- 2304410
- Status last verified
- Jan 2025
- First submitted
- 20 Jan 2025
- Last updated
- 29 Jan 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: To avoid publishing personally identifiable information. Instead, summary statistics of the population analysed will be made available.
Publications
Elkadim M, Nasef MH, Alagorie AR, Allam WA. Corneal Topographic versus Manifest Refractive Astigmatism in Patients with Keratoconus: A Retrospective Cross-Sectional Study. Clin Ophthalmol. 2022 Jun 20;16:2033-2039. doi: 10.2147/OPTH.S361338. eCollection 2022.
PubMed 35757018
Study at a glance
From the registry record, last updated by the sponsor on 29 Jan 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Manifest Astigmatism in Keratoconus Patients: A Machine Learning Approach Using Corneal Topography.
- ClinicalTrials.gov ID
- NCT06798779
- Acronym / sponsor ID
- 2304410
- Keywords
- Keratoconus, Machine learning, Astigmatism, AI, Corneal topography, Regression, Glasses, Cylinder
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 800 participants (estimated)
- Lead sponsor
- Royal Devon and Exeter NHS Foundation TrustUniversity, hospital or other
- Study start
- 1 Jun 2024 (actual)
- Primary completion
- 31 May 2025 (estimated)
- Study completion
- 31 May 2025 (estimated)
- First posted
- 29 Jan 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.
The cornea of the human eye has several functions. It is transparent to allow light into the eye and its shape focuses the incoming light onto the retina. The cornea is responsible for two-thirds of this focusing, while the human lens accounts for the remaining third.
Keratoconus is a condition with onset in the second to third decades of life, where the cornea warps into an irregular shape. This irregularity reduces vision due to blurring of the image focused on the retina. Only partial improvement is achievable with traditional glasses because the corneal shape becomes irregular.
The glasses prescriptions of patients with keratoconus often differ significantly from the measurements obtained from the cornea in a clinical setting. The predictability of the magnitude and variability of this disparity is poorly understood. As a result, determining the optimal glasses prescription for achieving the best vision correction often involves a time-consuming trial-and-error approach. Improved predictability could reduce the time required to identify the optimal glasses prescription, thereby increasing productivity. For surgical patients, better predictability would enable surgeons to select lenses that provide superior vision outcomes after treatment. In the optometry clinic at the West of England Eye Unit, a database of over 800 patients with glasses prescriptions and corresponding corneal scans has been compiled by the investigators. This is a sufficient dataset to train and assess the prediction accuracy of machine learning models (AI) of glasses measurements using corneal scan parameters as predictor variables.
Detailed description
Keratoconus is one of the leading causes of visual impairment worldwide. Its main therapeutic options include corneal cross-linking, intracorneal ring segments, penetrating keratoplasty, and lamellar keratoplasty.
Keratoconus is a progressive condition with onset in childhood, resulting in increasing irregularity of corneal shape. The cornea's role is to refract incoming light to focus it onto the retina to produce a sharp image. Irregularity of the cornea degrades this image. Keratoconus develops throughout the second and third decades of life and usually stabilises in the fourth decade. It leads to progressive vision loss. When detected early, progression can be halted or stabilised with collagen cross-linking treatment. Once the condition progresses to impair the quality of vision, rigid contact lenses or surgical procedures are often required for correction. Spectacles and soft contact lenses are frequently inadequate due to the cornea's irregular shape in keratoconus.
Surgical approaches to keratoconus that do not involve corneal transplantation include implantable collamer lenses (ICL) or toric intraocular lenses (tIOL). There is little published evidence on the use of these approaches, and no consensus exists regarding the extent of astigmatism to correct or the proper axis of astigmatism to select.
In addition to lower-order aberrations (such as those correctable by spectacles, including short-sightedness, long-sightedness, and astigmatism), higher-order aberrations are present, which cannot be corrected with glasses. Due to irregular corneal astigmatism and higher-order aberrations, accurate refraction is often challenging in patients with keratoconus. Eyes with keratoconus exhibit approximately five to six times more higher-order aberrations than normal eyes, particularly vertical coma. The manifest refractive cylinder (the astigmatism component in a glasses prescription) includes both regular and irregular astigmatism. Additionally, the axis of astigmatism in the prescription often differs significantly from the topometric axis derived from three-dimensional corneal imaging. In managing astigmatism for suitable patients, such as those receiving implantable collamer lenses (ICL) or toric intraocular lenses (tIOL), it is crucial to better understand the shape factors contributing to manifest refraction. Improved understanding can help neutralise post-operative refraction more effectively and achieve greater predictability. For non-surgical patients, determining the optimal glasses prescription to achieve the best vision correction often involves a time-consuming trial-and-error process. Improved predictability could reduce the time required to identify the optimal prescription, thereby enhancing productivity and patient care.
The aim of this study is to use data already collected from more than 800 patients to compare topographic elements (three-dimensional modelling of corneal shape), such as anterior and posterior curvatures and higher-order aberrations, to subjective manifest astigmatism (the amount of astigmatism in the glasses prescription) in keratoconic eyes. The impact of keratoconus severity on disparities between these methods will also be evaluated.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
Who may qualify
- Ages
- 16 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- - Keratoconus patients at the West of England Eye Unit in Exeter, United Kingdom
Key inclusion criteria (1 listed)
- All patients with a diagnosis of keratoconus who have had topography performed at the West of England Eye Unit with contemporaneous manifest refraction.
Key exclusion criteria (3 listed)
- Poor quality topography scans.
- History of ocular surgery.
- Age <16 years.
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 |
|---|---|---|
| GlassesThe patients will have had their vision corrected with glasses that may contain both spherical and cylindrical corrections. The glasses prescription that offers the best subjective correction of vision are being recorded. | Device | – |
| Corneal topographyThis is a scan of the cornea (front of the eye). This gives us information on how the cornea is refracting light for the patient. | Diagnostic test | – |
Primary outcome
The root mean squared error (RMSE) of the machine learning model's predictions for the patient's manifest astigmatism.
Time frame: Baseline
The primary outcome measure will be derived from the machine learning regression analysis, with demographic and keratometry values serving as predictor variables (X) and manifest astigmatism as the outcome variable (Y). For each patient, the earliest Pentacam scan recorded during the study period, for which a manifest astigmatism measurement exists on the same day, will be used.
1 secondary outcome
Shapley values for each predictor variable in the best-performing machine learning model.
Time frame: Baseline
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 29 Jan 2025.
- Background: PubMed 35757018Elkadim M, Nasef MH, Alagorie AR, Allam WA. Corneal Topographic versus Manifest Refractive Astigmatism in Patients with Keratoconus: A Retrospective Cross-Sectional Study. Clin Ophthalmol. 2022 Jun 20;16:2033-2039. doi: 10.2147/OPTH.S361338. eCollection 2022.
- Individual participant dataSharing planned: No. To avoid publishing personally identifiable information. Instead, summary statistics of the population analysed will be made available.
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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 Keratoconus, Artificial Intelligence, Support Vector Machine, run by Royal Devon and Exeter NHS Foundation Trust at 1 site in United Kingdom.
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 1 inclusion and 3 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 29 Jan 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 United Kingdom. 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?
The root mean squared error (RMSE) of the machine learning model's predictions for the patient's manifest astigmatism. (time frame: Baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06798779, which also lists 1 linked publication.
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 29 Jan 2025.