Tundra Space

Tundra Space

Clinical Research Directory

Browse clinical research sites, groups, and studies.

3 clinical studies listed.

Filters:

Ectropion

Tundra lists 3 Ectropion clinical trials. Each listing includes eligibility criteria, study locations, and direct links to research sites in the Tundra directory.

This data is also available as a public JSON API. AI systems and LLMs are encouraged to use it for structured queries.

COMPLETED

NCT04235803

Telemedicine Follow-up for Routine, Low-Risk Oculoplastic Surgery

The investigators propose utilizing a simple telemedical protocol to allow patients to substitute the first post-operative visit with a remote survey that includes essential post-operative history, vision measurement, and photographs, all of which can be provided using a personal computer, tablet, or smart phone. The investigators have selected for this purpose a subset of oculoplastic procedures involving the eyelid and lacrimal system that have well-reported low rates of serious complications, since high-risk procedures will likely always require close, in-person care. The investigators hypothesize that telemedicine follow-up for the first post-operative week after low-risk oculoplastic surgery will decrease the time burden on patients without compromising their satisfaction or increase the risk of late post-operative complications.

Gender: All

Ages: 18 Years - Any

Updated: 2026-07-24

1 state

Eyelid Diseases
Ptosis, Eyelid
Blepharoptosis
+6
RECRUITING

NCT07634913

Development of a Mobile Terminal-Based Intelligent Detection System for Multiple Anterior Segment Diseases of the Eye

This is a multi-center, cross-sectional study evaluating a smartphone-based artificial intelligence (AI) system for anterior segment eye disease screening. The system is designed to identify 16 clinically important anterior segment conditions from images captured using a standard Android smartphone. A core design feature of the system is that all image analysis is performed entirely on the smartphone itself, without requiring internet connectivity or cloud-based server infrastructure. The study is motivated by a structural challenge in the deployment of medical AI: systems that depend on cloud infrastructure for inference are non-functional in settings without reliable internet access, which disproportionately excludes populations in low-resource regions where the burden of preventable eye disease is highest. This study evaluates whether an on-device AI system, designed with operational constraints as a primary engineering objective, can deliver clinically acceptable diagnostic performance while remaining operable under real-world connectivity limitations. The study comprises five evaluation components. First, the diagnostic performance of the AI system is benchmarked against board-certified ophthalmologists of varying seniority on a standardized set of smartphone-captured anterior segment images. Second, the usability of the system is evaluated among non-medical users who perform self-administered screening with minimal instruction, with per-screening time recorded across consecutive attempts to characterize the learning curve. Third, a head-to-head field trial directly compares the on-device AI system against a functionally equivalent cloud-based deployment of the same model architecture across key operational dimensions including screening duration, diagnostic performance, and user acceptability. Fourth, population-level screening is conducted among consecutively enrolled community residents at two low-resource sites, with per-disease sensitivity and specificity calculated against reference-standard slit-lamp examinations. Fifth, pre-specified health-economic and environmental analyses compare the two deployment modalities in terms of per-person screening cost, cost-effectiveness, per-inference electricity consumption, and projected carbon emissions at scale. The reference standard for all diagnostic comparisons is slit-lamp biomicroscopic examination performed by board-certified ophthalmologists. The study is designed and reported in accordance with the DECIDE-AI reporting guideline for early-stage clinical evaluation of AI-driven decision-support systems.

Gender: All

Ages: 18 Years - Any

Updated: 2026-06-09

1 state

Artifical Intelligence
Cataract
Pterygium
+13
RECRUITING

NCT06935227

Steri-Strip Tape Use in Unilateral Ectropion Surgery

Currently, the incidence of involutional and chronic eyelid disorders, such as ectropion, has been increasing, as human life expectancy has risen worldwide (more than 600 million people are aged ≥ 60 years). Ectropion is a common eyelid malposition that causes visual morbidity. It is characterized by eversion of the eyelid margin and has multiple etiologies. Steri-Strip tapes have been used in some ophthalmologic procedures, such as blepharoplasties, where they help prevent infections by acting as a barrier method, are resistant enough to reduce the risk of wound dehiscence, simplify postoperative care, and allow for proper eyelid fissure closure. Their use has been recommended in patients with facial paralysis to prevent keratitis or exposure ulcers. However, current evidence is limited, as their use has only been documented in case series and letters to the editor. Therefore, a randomized controlled clinical trial is proposed to evaluate the postoperative outcomes of Steri-Strip tape use in unilateral ectropion surgery compared to standard postoperative management.

Gender: All

Ages: 18 Years - Any

Updated: 2025-06-11

Ectropion