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AI-Orchestrated Workflow Versus Consultant Ophthalmologist for Refractive Surgery and Keratoconus Diagnosis (AEYE Trial)

NCT07096232 · Status: ENROLLING_BY_INVITATION · Type: OBSERVATIONAL · Enrollment: 50

Last updated 2025-09-15

No results posted yet for this study

Summary

Background and Rationale:

Laser vision correction procedures, such as LASIK (Laser-Assisted In Situ Keratomileusis), PRK (Photorefractive Keratectomy), and SMILE (Small Incision Lenticule Extraction), are highly effective but require careful preoperative screening to ensure safety. One of the most critical aspects of screening is identifying keratoconus and other corneal ectatic disorders-conditions that cause progressive thinning and bulging of the cornea, often contraindicating surgery. Early detection is essential to avoid vision-threatening complications.

Despite advanced corneal imaging tools such as Scheimpflug tomography and anterior segment optical coherence tomography (AS-OCT), accurate diagnosis-particularly in borderline or early-stage cases-remains challenging and subject to variability in human interpretation. Artificial intelligence (AI) offers the potential to improve diagnostic precision, reduce oversight, and standardize surgical planning.

Purpose of the Study:

This study evaluates the performance of AEYE (Automated Evaluation for Your Eye), a multi-agent AI system designed to support ophthalmologists in diagnosing keratoconus and determining refractive surgery eligibility. AEYE simulates the clinical workflow of an anterior segment specialist by orchestrating three specialized agents:

History \& Risk Agent: Reviews patient history and extracts risk factors.

Imaging Agent: Analyzes corneal tomography, AS-OCT, and epithelial mapping scans.

Surgical Decision Agent: Integrates all findings, assigns a diagnosis, and recommends appropriate treatment options, including surgical eligibility or corneal cross-linking (CXL).

Study Design:

The study includes 50 real-world patient cases, both retrospective (from 2020 onward) and prospective, who were evaluated for refractive surgery or keratoconus. Each case is analyzed independently by AEYE and a consultant ophthalmologist (blinded to AI output), using the same multimodal clinical and imaging data. Diagnostic accuracy, agreement in surgical recommendations, and workflow efficiency are assessed.

Anticipated Impact:

By comparing AI-derived decisions with expert clinical judgment, this study aims to validate whether structured AI workflows like AEYE can serve as reliable, safe, and explainable decision support tools. If successful, AEYE may offer a scalable solution to reduce diagnostic variability and enhance the safety and consistency of refractive surgery screening.

Conditions

  • Keratoconus
  • Refractive Surgery
  • Machine Learning
  • Artifical Intelligence
  • Diagnostic Accuracy
  • Clinical Decision Support
  • Ophthalmology

Interventions

DIAGNOSTIC_TEST

Multi-Agent AI Diagnostic Workflow (AEYE)

This intervention consists of an orchestrated diagnostic workflow utilizing multiple specialized artificial intelligence (AI) agents to analyze patient data and ophthalmic imaging for the diagnosis of refractive errors and keratoconus. The workflow, named Automated Evaluation for Your Eye (AEYE), integrates various AI modules designed for data extraction, image interpretation, and decision support. Each patient's clinical information, corneal topography, tomography, and other relevant imaging are processed sequentially through these AI agents, with results synthesized into a diagnostic recommendation. The system operates independently of clinician input, and outputs are blinded prior to comparison with consultant ophthalmologist assessments. The intervention is intended to assess diagnostic accuracy, efficiency, and concordance with expert clinical decision-making.

Sponsors & Collaborators

  • Ahmed I ElSayegh

    collaborator UNKNOWN

  • Hazem Yassin Clinics

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-01-01
Primary Completion
2025-09-18
Completion
2025-09-30

Countries

  • Egypt

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07096232 on ClinicalTrials.gov