Deep Learning Assisted Epithelial Basement Membrane Dystrophy Detection

NCT ID: NCT05770492

Last Updated: 2023-03-15

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 100 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-02-27
• Study Completion Date: 2024-02-27

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.

Detailed Description

This study aims to investigate the capability of an automated deep learning system using anterior segment optical coherence tomography scans to distinguish between normal human corneas and corneas affected by epithelial basement membrane dystrophy. In an optional substudy, a second visit will be scheduled to investigate the reproducibility of disease characteristics as a secondary outcome.

One-hundred eyes of 50 patients with epithelial basement membrane dystrophy and 100 eyes of 50 healthy subjects will be included in this study. After successful screening, all study participants will undergo one single study visit. During this visit, two questionnaires (Ocular Surface Disease Index, Quality of Vision), two different anterior segment optical coherence tomography devices (MS-39, Anterion), a slit lamp examination including slit lamp photography will be performed.

In an optional substudy, patients with epithelial basement membrane dystrophy will have a second visit, to compare the variability of disease characteristics, including number of maps, dots, fingerprint lines and cysts between the two visits.

Conditions

Map Dot Fingerprint Dystrophy

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: PROSPECTIVE

Study Groups

Epithelial Basement Membrane Dystrophy

Patients with epithelial basement membrane dystrophy

anterior segment optical coherence tomography

Intervention Type: 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.

Healthy

Patients/Subjects without corneal pathologies

anterior segment optical coherence tomography

Intervention Type: 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.

Interventions

anterior segment optical coherence tomography

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.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• 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

Exclusion Criteria

• 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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Vienna Institute for Research in Ocular Surgery

OTHER

Sponsor Role: lead

Responsible Party

Prim. Prof. Dr. Oliver Findl, MBA

Head of Ophthalmology Department

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Vienna Institute for Research in Ocular Surgery (VIROS)

Vienna, , Austria

Site Status: RECRUITING

Countries

Austria

Central Contacts

Oliver Findl, MD, MBA, FEBO

Role: CONTACT

office@viros.at

+43 1 91021- 57564

Facility Contacts

Andreas Schlatter, MD

Role: primary

a.schlatter@viros.at

+43 1 91021- 57564

Other Identifiers

DL_EBMD

Identifier Type: -

Identifier Source: org_study_id