Deep Learning Assisted Epithelial Basement Membrane Dystrophy Detection

NCT05770492 | Unknown

• 1 other identifier: DL_EBMD
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

Epithelial basement membrane dystrophy, also known as Map-Dot fingerprint dystrophy or Cogan microcystic dystrophy, is a common bilateral dystrophy of the anterior human cornea. According to one study, it affects approximately 2% of the human population. A more recent study even reported basement membrane changes in 25% of the general population. However, due to its clinical and morphological appearance, the disease is probably often overlooked. Although epithelial basement membrane dystrophy is asymptomatic in many affected patients, there are some important clinical consequences of the disease to consider: Dystrophy is estimated to be the second most common cause of recurrent corneal erosion syndrome and is also an important differential diagnosis of dry eye disease. Therefore, it can cause severe pain in affected patients. In addition, epithelial basement membrane dystrophy plays an important role in the context of cataract surgery, one of the most commonly performed surgeries worldwide: besides the importance of appropriate disease management before surgery to prevent postoperative exacerbation of ocular surface symptoms, epithelial basement membrane dystrophy is also a risk factor for inaccurate preoperative biometry. In recent years, specific features of epithelial basement membrane dystrophy have been introduced in examination methods other than slit-lamp biomicroscopy, such as epithelial thickness mapping or optical coherence tomography. Due to the recent introduction of a variety of deep learning systems, the application of machine learning could significantly increase the detection rate for epithelial basement membrane dystrophy. Furthermore, to the best of our knowledge, the change in disease characteristics over time is currently unknown. Therefore, the first part of this study will investigate the ability of an automated deep learning system using optical coherence tomography scans to distinguish between normal human corneas and corneas affected by epithelial basement membrane dystrophy. For this purpose, 100 eyes of 50 patients will be included in both study groups. In an optional 2nd part of the study, a second visit will be planned in patients with epithelial basement membrane dystrophy to investigate the reproducibility of disease characteristics as a secondary outcome.

Conditions

Map Dot Fingerprint Dystrophy

Outcome Measures

Primary Outcomes (3)

• Sensitivity of the deep learning system to detect optical coherence tomography scans with epithelial basement membrane dystrophy on the final test data set

  1 day

• Specificity of the deep learning system to detect optical coherence tomography scans with epithelial basement membrane dystrophy on the final test data set

  1 day

• Area under the curve of the deep learning algorithm on the final test data set

  1 day

Secondary Outcomes (12)

• Interobserver variability regarding disease diagnosis (normal cornea vs. epithelial basement membrane dystrophy) according to slit lamp photographies

  1 day

• Interobserver variability regarding number of maps according to slit lamp photographies

  1 day

• Interobserver variability regarding number of dots according to slit lamp photographies

  1 day

• Interobserver variability regarding number of fingerprints according to slit lamp photographies

  1 day

• Interobserver variability regarding number of cysts according to slit lamp photographies

  1 day

Study Arms (2)

Epithelial Basement Membrane Dystrophy

Patients with epithelial basement membrane dystrophy

Diagnostic Test: anterior segment optical coherence tomography

Healthy

Patients/Subjects without corneal pathologies

Diagnostic Test: anterior segment optical coherence tomography

Interventions

anterior segment optical coherence tomography DIAGNOSTIC_TEST

Two different optical systems (MS-39, Costruzione Strumenti Oftalmici Italy; Anterion optical coherence tomographer, Heidelberg Engineering) will be used for acquisition of cross-sectional scans. Radial scan patterns will be used for acquisition.

Epithelial Basement Membrane Dystrophy; Healthy

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Community sample

You may qualify if:

• Age 18 or older
• Written informed consent
• Presence of epithelial basement membrane dystrophy
• Age 18 or older
• Written informed consent
• No corneal pathology in both eyes

You may not qualify if:

• Other corneal conditions (such as corneal scarring, fuchs endothelial corneal dystrophy, etc.)
• Pregnancy (pregnancy test will be taken in women of reproductive age), nursing women

Sponsors & Collaborators

• Vienna Institute for Research in Ocular Surgery: lead

Study Sites (1)

Vienna Institute for Research in Ocular Surgery (VIROS)

Vienna, 1140, Austria

RECRUITING

MeSH Terms

Conditions

Corneal dystrophy, epithelial basement membrane

Central Study Contacts

Oliver Findl, MD, MBA, FEBO

CONTACT

+43 1 91021- 57564; office@viros.at

Study Design

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

Study Record Dates

• First Submitted: February 27, 2023
• First Posted: March 15, 2023
• Study Start: February 27, 2023
• Primary Completion: February 27, 2024
• Study Completion: February 27, 2024
• Last Updated: March 15, 2023

Record last verified: 2023-03

Locations

AU (1)