Building Research With Artificial Intelligence in Neuro-Ophthalmology
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 693
- 试验地点
- 1
- 主要终点
- Diagnostic performance of the Artificial Intelligence algorithm in detecting multiple neuro-ophthalmologic and neurologic conditions from retinal imaging.
研究概览
简要总结
The research team, recognized as a world leader in Artificial Intelligence for neuro-ophthalmology, has shown that it is possible to diagnose certain neuro-ophthalmologic or neurologic disorders from a single retinal fundus image (Milea et al, New England Journal of Medicine, 2020). However, clinical practice requires identifying a broader spectrum of diseases (inflammatory, ischemic, hereditary, neurodegenerative) within the same analysis.
The main objective is to develop, through a new algorithm capable of classifying multiple disorders from a smaller set of conventional retinal images.
This project meets a significant public health need: the global shortage of neuro-ophthalmologists. It aims to provide healthcare professionals with a rapid triage tool to detect serious and treatable conditions, enabling timely intervention.
The study will include patients with clearly defined neuro-ophthalmologic or neurologic conditions, confirmed diagnoses, and retinal imaging. Clinical, paraclinical, and imaging data collected during standard care will be used, with strict anonymization according to legal and institutional requirements.
Specific Objectives :
- Evaluate the performance of a diagnostic classification algorithm trained on retinal images.
- Assess the ability to detect multiple pathologies from a single retinal image.
- Support the development of advanced computer vision tools for medical diagnostics.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients with well-defined neuro-ophthalmologic or neurologic conditions, including different forms of optic neuropathies and various neurodegenerative diseases.
- •Patients with a robust reference diagnosis confirmed by clinical experts.
- •Patients with available retinal fundus images collected during routine care.
排除标准
- •Patients without a confirmed diagnosis or unclear clinical classification.
- •Patients without retinal fundus images or with images that are completely unreadable.
- •Patients whose data cannot be anonymized according to legal and institutional protocols.
结局指标
主要结局
Diagnostic performance of the Artificial Intelligence algorithm in detecting multiple neuro-ophthalmologic and neurologic conditions from retinal imaging.
时间窗: baseline
Evaluation of the algorithm's sensitivity, specificity, and area under the receiver operating caracteristics curve for classifying multiple neuro-ophthalmologic and neurologic pathologies using retinal fundus photography and Optical Coherence Tomography images, compared with expert-established reference diagnoses.
次要结局
未报告次要终点