Orchestrated Multi Agent Artificial Intelligence (AI) Versus Consultant Ophthalmologist: Workflow Performance in Refractive Surgery and Keratoconus Diagnosis. Demonstrating Increased Diagnostic Accuracy (Automated Evaluation for Your Eye, AEYE).
Trial ID: NCT07096232
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-01-01
Completion date: 2025-09-30
Sponsor: Hazem Yassin Clinics
Collaborators: Ahmed I ElSayegh
Conditions: Keratoconus; Refractive Surgery; Machine Learning; Artifical Intelligence; Diagnostic Accuracy; Clinical Decision Support; Ophthalmology
Interventions: Multi-Agent AI Diagnostic Workflow (AEYE)
Locations: Hazem Yassin Clinics
Countries: Egypt
Enrollment: 50
Age range: None - None
Sex: ALL

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.

Eligibility criteria:
Inclusion Criteria:

1. Diagnosis of Refractive Error or Keratoconus:

   * Patients with a confirmed clinical diagnosis of refractive errors (myopia, hyperopia, or astigmatism) or keratoconus, as determined by a qualified ophthalmologist based on current diagnostic standards.
2. Availability of Complete Data:

   * Clinical records must include comprehensive demographic data, medical and ophthalmic history, refraction measurements, and high-quality corneal imaging (e.g., corneal topography, tomography, or OCT).
3. Eligible for Both AI and Consultant Review:

   * Patients must have data sets that allow both the multi-agent AI workflow (AEYE) and an independent consultant ophthalmologist to conduct a complete diagnostic assessment.
4. Consent:

   * For prospectively enrolled patients, written informed consent must be obtained prior to participation. For retrospective cases, waiver of consent may be granted according to local IRB/ethics committee policies.
5. No Restriction on Age or Sex:

   * There are no specific age or sex limitations for inclusion in this study. Pediatric and adult cases may be included if data are available and consent is appropriately obtained.

Clinical Documentation Requirements:

* Availability of complete ophthalmic records including:
* Uncorrected and best corrected visual acuity (UCVA, BCVA)
* Manifest refraction (sphere, cylinder, axis)
* History of contact lens use and duration
* Prior ocular surgery or trauma, if applicable
* Family history of keratoconus or corneal ectasia
* Use of medications that may influence tear film stability or corneal biomechanics
* Ocular surface disease documentation if present (e.g., dry eye, blepharitis)

Imaging and Diagnostic Data Requirements:

* Availability of at least one valid and complete set of:
* Scheimpflug-based corneal tomography (e.g., Pentacam or equivalent)
* Anterior segment OCT (AS-OCT), if performed
* Epithelial thickness mapping (optional but desirable)
* Corneal biomechanical data (optional)
* Imaging files must be:
* Of adequate quality as judged by the original technician and reviewer
* Free from significant artifacts or motion blur
* Properly labeled by eye (OD/OS) and imaging modality
* Acquired with standard scan protocols

Diagnostic Spectrum Requirements:

* Patients may fall into any of the following diagnostic categories:
* Normal cornea eligible for laser refractive surgery
* Suspect keratoconus or forme fruste keratoconus
* Established keratoconus of any stage (I-IV or ABCD)
* Post-CXL (cross-linking) cornea undergoing follow-up evaluation
* Post-keratoplasty cornea (e.g., DALK, PKP) undergoing diagnostic review
* Patients disqualified from surgery due to abnormal tomography or other contraindications.

Exclusion Criteria:

1. Incomplete or Poor Quality Data:

   * Patients with missing, incomplete, or poor-quality clinical data or corneal imaging that precludes reliable diagnosis by either AI or consultant review.
2. Ocular Comorbidities:

   * Presence of ocular diseases that could confound the diagnosis or interpretation of refractive error or keratoconus, such as advanced glaucoma, active uveitis, significant retinal pathology, or previous corneal transplantation.
3. Severe Systemic Disease Affecting the Eye:

   * Patients with systemic diseases known to affect the cornea or refraction (e.g., connective tissue disorders with corneal involvement) will be excluded to avoid confounding effects.
4. Inability or Refusal to Consent:

   * For prospectively enrolled cases, patients (or guardians, in the case of minors) who are unwilling or unable to provide informed consent will be excluded.
5. Participation in Conflicting Studies:

   * Patients currently enrolled in other interventional studies that could interfere with the diagnostic process or outcomes measured in this study.

Clinical Documentation Exclusions:

* Missing or incomplete visual acuity data.
* Absence of manifest refraction or subjective refraction data.
* Incomplete or conflicting patient history with unresolvable discrepancies.
* Absence of necessary demographic identifiers (e.g., gender, age, laterality of findings).

Imaging and Data Quality Exclusions:

* Imaging datasets with significant artifacts that preclude AI analysis.
* Files missing key parameters (e.g., Belin/Ambrósio D index, Kmax, pachymetry) required for keratoconus classification.
* Inability to determine laterality (OD/OS) for specific scans.
* Scans acquired with outdated or non-standard imaging protocols.
* Mislabeling or duplication of imaging sets across different patients.

Case-Type Exclusions:

* Cases involving active ocular infection or inflammation at the time of evaluation.
* Cases with active herpetic eye disease, corneal ulcer, or epithelial defects interfering with imaging interpretation.
* Prior laser refractive surgery unless explicitly classified and used as part of post-refractive ectasia evaluation.
* Eyes with concurrent advanced retinal pathology that may confound diagnostic interpretation (e.g., macular edema, retinal dystrophies).

Technology-Specific Exclusions:

* Imaging performed using devices not compatible with the AEYE data ingestion framework (e.g., certain older corneal topographers).
* Non-English documentation incompatible with NLP parsing in the General Ophthalmology Agent.

Other Considerations:

* AEYE must be able to process the case within its defined schema. If key data elements are absent or the case causes schema failure, it will be excluded from quantitative analysis but may be reported qualitatively as part of system performance limitations.
* If multiple attempts at AI processing result in non-deterministic or erroneous outputs, such cases will be logged but excluded from primary outcome evaluation.
* Human reviewers (consultant ophthalmologists) must have completed their diagnostic interpretation independently prior to AEYE input; any cases where human opinion was influenced by AI output will be excluded to preserve blinding.

Outcomes:
Diagnostic Accuracy of Multi-Agent AI Workflow Compared to Consultant Ophthalmologist