Department of Ophthalmology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany.
TeleMedC GmbH, Raboisen 32, 20095, Hamburg, Germany.
BMC Ophthalmol. 2024 Feb 1;24(1):51. doi: 10.1186/s12886-024-03306-y.
Artificial intelligence (AI) has the potential to increase the affordability and accessibility of eye disease screening, especially with the recent approval of AI-based diabetic retinopathy (DR) screening programs in several countries.
This study investigated the performance, feasibility, and user experience of a seamless hardware and software solution for screening chronic eye diseases in a real-world clinical environment in Germany. The solution integrated AI grading for DR, age-related macular degeneration (AMD), and glaucoma, along with specialist auditing and patient referral decision. The study comprised several components: (1) evaluating the entire system solution from recruitment to eye image capture and AI grading for DR, AMD, and glaucoma; (2) comparing specialist's grading results with AI grading results; (3) gathering user feedback on the solution.
A total of 231 patients were recruited, and their consent forms were obtained. The sensitivity, specificity, and area under the curve for DR grading were 100.00%, 80.10%, and 90.00%, respectively. For AMD grading, the values were 90.91%, 78.79%, and 85.00%, and for glaucoma grading, the values were 93.26%, 76.76%, and 85.00%. The analysis of all false positive cases across the three diseases and their comparison with the final referral decisions revealed that only 17 patients were falsely referred among the 231 patients. The efficacy analysis of the system demonstrated the effectiveness of the AI grading process in the study's testing environment. Clinical staff involved in using the system provided positive feedback on the disease screening process, particularly praising the seamless workflow from patient registration to image transmission and obtaining the final result. Results from a questionnaire completed by 12 participants indicated that most found the system easy, quick, and highly satisfactory. The study also revealed room for improvement in the AMD model, suggesting the need to enhance its training data. Furthermore, the performance of the glaucoma model grading could be improved by incorporating additional measures such as intraocular pressure.
The implementation of the AI-based approach for screening three chronic eye diseases proved effective in real-world settings, earning positive feedback on the usability of the integrated platform from both the screening staff and auditors. The auditing function has proven valuable for obtaining efficient second opinions from experts, pointing to its potential for enhancing remote screening capabilities.
Institutional Review Board of the Hamburg Medical Chamber (Ethik-Kommission der Ärztekammer Hamburg): 2021-10574-BO-ff.
人工智能（AI）有可能提高眼病筛查的可负担性和可及性，尤其是在最近几个国家批准了基于 AI 的糖尿病视网膜病变（DR）筛查计划之后。
本研究在德国的真实临床环境中，调查了一种用于筛查慢性眼病的无缝硬件和软件解决方案的性能、可行性和用户体验。该解决方案集成了用于 DR、年龄相关性黄斑变性（AMD）和青光眼的 AI 分级，以及专家审核和患者转诊决策。该研究包括以下几个部分：（1）评估从招募到 DR、AMD 和青光眼的眼部图像采集和 AI 分级的整个系统解决方案；（2）比较专家分级结果和 AI 分级结果；（3）收集对解决方案的用户反馈。
共招募了 231 名患者，并获得了他们的同意书。DR 分级的灵敏度、特异性和曲线下面积分别为 100.00%、80.10%和 90.00%。AMD 分级的数值分别为 90.91%、78.79%和 85.00%，青光眼分级的数值分别为 93.26%、76.76%和 85.00%。对三种疾病的所有假阳性病例进行分析，并将其与最终转诊决策进行比较，结果显示，在 231 名患者中，只有 17 名患者被错误转诊。系统的功效分析表明，AI 分级过程在研究的测试环境中是有效的。参与使用该系统的临床工作人员对疾病筛查过程给予了积极的反馈，特别是对从患者登记到图像传输和获得最终结果的无缝工作流程表示赞赏。对 12 名参与者完成的问卷的结果表明，大多数人认为该系统简单、快速且非常满意。研究还表明 AMD 模型需要改进，建议增强其训练数据。此外，通过纳入眼压等额外措施，可以提高青光眼模型分级的性能。
在真实环境中实施基于 AI 的方法筛查三种慢性眼病是有效的，筛查工作人员和审核人员对集成平台的可用性均给予了积极反馈。审核功能对于从专家那里获得高效的二次意见非常有价值，这表明它有可能增强远程筛查能力。
汉堡医师协会伦理委员会（Ethik-Kommission der Ärztekammer Hamburg）：2021-10574-BO-ff。
