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Deep Learning Assisted Epithelial Basement Membrane Dystrophy Detection

Automated Deep Learning for Detection of Epithelial Basement Membrane Dystrophy Using Optical Coherence Tomography and Longitudinal Reproducibility of Disease Characteristics

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05770492
Enrollment
100
Registered
2023-03-15
Start date
2023-02-27
Completion date
2024-02-27
Last updated
2023-03-15

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Map Dot Fingerprint Dystrophy

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.

Interventions

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.

Sponsors

Vienna Institute for Research in Ocular Surgery
Lead SponsorOTHER

Study design

Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes

Inclusion criteria

(Group 1): * Age 18 or older * Written informed consent * Presence of epithelial basement membrane dystrophy Inclusion Criteria (Group 2): * 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

Design outcomes

Primary

MeasureTime frame
Sensitivity of the deep learning system to detect optical coherence tomography scans with epithelial basement membrane dystrophy on the final test data set1 day
Specificity of the deep learning system to detect optical coherence tomography scans with epithelial basement membrane dystrophy on the final test data set1 day
Area under the curve of the deep learning algorithm on the final test data set1 day

Secondary

MeasureTime frameDescription
Interobserver variability regarding number of dots according to slit lamp photographies1 day
Interobserver variability regarding number of fingerprints according to slit lamp photographies1 day
Interobserver variability regarding number of cysts according to slit lamp photographies1 day
Difference in epithelial thickness mapping between healthy corneas and corneas affected by epithelial basement membrane dystrophy1 day
Difference in Quality of Vision questionnaire score between healthy subjects and patients affected by epithelial basement membrane dystrophy1 day0- to 100-unit linear scale, higher scores indicating poorer quality of vision
Sub-study only: Reproducibility of number of maps between visit 1 and visit 2 according to slit lamp photographies and optical coherence tomography images3 monthsnumber of corneal maps will be assessed by a clinical investigator at both visits
Sub-study only: Reproducibility of number of dots between visit 1 and visit 2 according to slit lamp photographies and optical coherence tomography images3 monthsnumber of corneal dots will be assessed by a clinical investigator at both visits
Sub-study only: Reproducibility of number of fingerprints between visit 1 and visit 2 according to slit lamp photographies and optical coherence tomography images3 monthsnumber of corneal fingerprint lines will be assessed by a clinical investigator at both visits
Sub-study only: Reproducibility of number of cysts between visit 1 and visit 2 according to slit lamp photographies and optical coherence tomography images3 monthsnumber of corneal cysts will be assessed by a clinical investigator at both visits
Difference in Ocular Surface Disease Index between healthy subjects and patients affected by epithelial basement membrane dystrophy1 dayminimum: 0, maximum: 100, higher scores are associated with increased symptoms regarding ocular surface disease
Interobserver variability regarding disease diagnosis (normal cornea vs. epithelial basement membrane dystrophy) according to slit lamp photographies1 day
Interobserver variability regarding number of maps according to slit lamp photographies1 day

Countries

Austria

Contacts

Primary ContactOliver Findl, MD, MBA, FEBO
office@viros.at+43 1 91021- 57564

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026