Effectiveness and Cost-Effectiveness Evaluations of AI-Assisted Diagnostic Software (VeriSee) for Ophthalmic Disease Screening
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 1,000
- 试验地点
- 1
- 主要终点
- Sensitivity
研究概览
简要总结
This study aims to evaluate the effectiveness of an artificial intelligence (AI)-assisted screening system in ophthalmic diagnosis. Using AI-based fundus photography, the system will assist physicians in diagnosing three common eye diseases: age-related macular degeneration and diabetic retinopathy (DR). The AI system will analyze fundus images from participants and rapidly generate detection results for ophthalmologists' reference in making final diagnoses and clinical decisions. The study will assess the clinical benefits of the AI-assisted diagnostic system, providing scientific evidence to enhance the efficiency of ophthalmic disease diagnosis and treatment.
详细描述
Artificial Intelligence (AI) has shown significant potential in medical imaging analysis and disease diagnosis, particularly in ophthalmology. Substantial advancements have been made in utilizing AI for diagnosing common ophthalmic diseases, enhancing early detection and improving patient outcomes. Early diagnosis of age-related macular degeneration (AMD) and diabetic retinopathy (DR) is crucial for effective treatment and disease management.
However, current clinical diagnoses rely heavily on ophthalmologists, leading to challenges such as low patient attendance rates and unequal distribution of diagnostic resources. To address these issues, this study will provide robust evidence to further validate the diagnostic performance of AI-assisted screening and clinical effectiveness of the VeriSee AI-assisted diagnostic system in the detection of diabetic DR and AMD.
VeriSee AMD and VeriSee DR are AI-powered medical software tools designed to screen for AMD and DR, respectively. These systems employ advanced AI algorithms to analyze color fundus photography images, assess disease conditions, and evaluate image quality. By integrating this software into clinical workflows, physicians receive instant diagnostic support, improving efficiency and accessibility in ophthalmic disease screening.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Screening
- 盲法
- None
入排标准
- 年龄范围
- 20 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •VeriSee AMD is used in non-retinal subspecialty ophthalmology clinics for adults aged 50 and above.
- •VeriSee DR is used in non-retinal subspecialty clinics for diabetic patients aged 20 and above.
排除标准
- •The patient does not agree to participate in the trial or is unable to provide informed consent.
研究组 & 干预措施
AI Intervention
Patients will undergo fundus photography screening using artificial intelligence-assisted diagnostic software (VeriSee). Ophthalmologists will independently interpret the same images, and the results will be compared with those generated by the AI.
干预措施: The VeriSee AI-assisted diagnostic system (Other)
AI Intervention
Patients will undergo fundus photography screening using artificial intelligence-assisted diagnostic software (VeriSee). Ophthalmologists will independently interpret the same images, and the results will be compared with those generated by the AI.
干预措施: Data collection from the patient's clinical history (Other)
结局指标
主要结局
Sensitivity
时间窗: From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month
The sensitivity of the index test (VeriSee) was calculated as the proportion of participants with reference standard-confirmed disease who were correctly identified as positive by the AI-assisted diagnostic software.
Specificity
时间窗: From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month
The specificity of the index test was calculated as the proportion of participants without the target condition, as determined by the reference standard, who were correctly classified as negative by the AI-assisted diagnostic tool.
Concordance
时间窗: From screening to physician-confirmed diagnosis of AMD or DR, an average of 1 month
Concordance between the AI-assisted diagnosis and the ophthalmologists' interpretation was assessed using the overall agreement rate (i.e., the percentage of cases with identical classification results).
次要结局
- Total Cost Analysis (Including Direct and Indirect Costs)(From enrollment to 12 months after screening)