Ignite Creation Date:
2025-12-24 @ 10:02 PM
Ignite Modification Date:
2025-12-29 @ 11:17 PM
Study NCT ID:
NCT07096232
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
2025-09-15
First Post:
2025-07-24
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI-Orchestrated Workflow Versus Consultant Ophthalmologist for Refractive Surgery and Keratoconus Diagnosis (AEYE Trial)
Sponsor:
Hazem Yassin Clinics
Organization:
Study Overview
Official Title:
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).
Status:
ENROLLING_BY_INVITATION
Status Verified Date:
2025-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
AEYE
Brief 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.
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.
Detailed Description:
Technical Protocol Summary
This is a diagnostic performance study evaluating AEYE (Automated Evaluation for Your Eye), an orchestrated multi-agent artificial intelligence (AI) system designed to assist ophthalmologists in the screening and management of keratoconus and refractive surgery planning. AEYE combines large language models (LLMs) with deterministic code logic to replicate and support the clinical decision-making process typically performed by anterior segment specialists.
System Architecture and Workflow
AEYE is structured as a modular pipeline of three specialized agents, each focused on a distinct diagnostic task:
History and Risk Agent: Extracts structured data from unstructured clinical records, including demographics, ocular/systemic history, medication use, and risk factors relevant to keratoconus or refractive surgery eligibility.
Imaging Analysis Agent: Processes multimodal anterior segment imaging such as Scheimpflug-based tomography (e.g., Pentacam), anterior segment optical coherence tomography (AS-OCT), and epithelial thickness mapping. It standardizes key metrics like maximum keratometry (Kmax), thinnest corneal pachymetry, anterior/posterior elevation, Belin-Ambrósio Deviation Index (BAD-D), and Pachymetric Progression Index (PPI). Each eye and each imaging file is processed independently to avoid misattribution.
Surgical Decision Agent: Integrates the outputs of the previous agents to generate a final diagnosis, assign keratoconus staging (e.g., ABCD, Amsler-Krumeich classification), and recommend next steps, such as LASIK (Laser-Assisted In Situ Keratomileusis), PRK (Photorefractive Keratectomy), SMILE (Small Incision Lenticule Extraction), phakic intraocular lenses (ICL), or corneal collagen cross-linking (CXL). The output is a structured, auditable report.
All agents are controlled by a deterministic workflow using Python scripts for data merging, schema validation, and output formatting. Structured memory is maintained using JSON objects that store the full diagnostic context per patient. The system is designed to ensure reproducibility, reduce human variability, and support explainable clinical decision-making.
Study Workflow and Scope
Fifty real-world patient cases are included, comprising both retrospective records (from January 2020 onward) and newly enrolled prospective cases. Each case includes comprehensive clinical data and anterior segment imaging. AEYE analyzes the case and generates a structured report. Separately, an experienced ophthalmologist (blinded to the AI output) reviews the same data and records their clinical decisions.
Key metrics include:
Diagnostic accuracy of keratoconus detection. Agreement in surgical eligibility assessments. Efficiency of workflow execution. Variability in results across different LLMs used in agent roles. Cases with discordant results may undergo adjudication to establish a reference standard.
Innovation and Clinical Relevance.
AEYE represents a novel application of explainable AI in ophthalmology. Its multi-agent design reflects a divide-and-conquer strategy, reducing cognitive load on any single model while enforcing clinical safety through deterministic logic. The system supports scalability, modularity, and integration into electronic health record (EHR) systems. The study will help determine whether AEYE can function as a safe, consistent, and effective assistant in complex diagnostic pathways for corneal ectatic disease.
This is a diagnostic performance study evaluating AEYE (Automated Evaluation for Your Eye), an orchestrated multi-agent artificial intelligence (AI) system designed to assist ophthalmologists in the screening and management of keratoconus and refractive surgery planning. AEYE combines large language models (LLMs) with deterministic code logic to replicate and support the clinical decision-making process typically performed by anterior segment specialists.
System Architecture and Workflow
AEYE is structured as a modular pipeline of three specialized agents, each focused on a distinct diagnostic task:
History and Risk Agent: Extracts structured data from unstructured clinical records, including demographics, ocular/systemic history, medication use, and risk factors relevant to keratoconus or refractive surgery eligibility.
Imaging Analysis Agent: Processes multimodal anterior segment imaging such as Scheimpflug-based tomography (e.g., Pentacam), anterior segment optical coherence tomography (AS-OCT), and epithelial thickness mapping. It standardizes key metrics like maximum keratometry (Kmax), thinnest corneal pachymetry, anterior/posterior elevation, Belin-Ambrósio Deviation Index (BAD-D), and Pachymetric Progression Index (PPI). Each eye and each imaging file is processed independently to avoid misattribution.
Surgical Decision Agent: Integrates the outputs of the previous agents to generate a final diagnosis, assign keratoconus staging (e.g., ABCD, Amsler-Krumeich classification), and recommend next steps, such as LASIK (Laser-Assisted In Situ Keratomileusis), PRK (Photorefractive Keratectomy), SMILE (Small Incision Lenticule Extraction), phakic intraocular lenses (ICL), or corneal collagen cross-linking (CXL). The output is a structured, auditable report.
All agents are controlled by a deterministic workflow using Python scripts for data merging, schema validation, and output formatting. Structured memory is maintained using JSON objects that store the full diagnostic context per patient. The system is designed to ensure reproducibility, reduce human variability, and support explainable clinical decision-making.
Study Workflow and Scope
Fifty real-world patient cases are included, comprising both retrospective records (from January 2020 onward) and newly enrolled prospective cases. Each case includes comprehensive clinical data and anterior segment imaging. AEYE analyzes the case and generates a structured report. Separately, an experienced ophthalmologist (blinded to the AI output) reviews the same data and records their clinical decisions.
Key metrics include:
Diagnostic accuracy of keratoconus detection. Agreement in surgical eligibility assessments. Efficiency of workflow execution. Variability in results across different LLMs used in agent roles. Cases with discordant results may undergo adjudication to establish a reference standard.
Innovation and Clinical Relevance.
AEYE represents a novel application of explainable AI in ophthalmology. Its multi-agent design reflects a divide-and-conquer strategy, reducing cognitive load on any single model while enforcing clinical safety through deterministic logic. The system supports scalability, modularity, and integration into electronic health record (EHR) systems. The study will help determine whether AEYE can function as a safe, consistent, and effective assistant in complex diagnostic pathways for corneal ectatic disease.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: