Department of Ophthalmology, Navarra Institute for Health Research (IdiSNA), Clínica Universidad de Navarra, Av. de Pío XII, 36, 31008, Pamplona, Navarra, Spain.
Faculty of Medicine, Universidad de Navarra, Pamplona, Spain.
Sci Rep. 2023 Oct 16;13(1):17585. doi: 10.1038/s41598-023-44686-3.
Blepharoptosis is a recognized cause of reversible vision loss and a non-specific indicator of neurological issues, occasionally heralding life-threatening conditions. Currently, diagnosis relies on human expertise and eyelid examination, with most existing Artificial Intelligence algorithms focusing on eyelid positioning under specialized settings. This study introduces a deep learning model with convolutional neural networks to detect blepharoptosis in more realistic conditions. Our model was trained and tested using high quality periocular images from patients with blepharoptosis as well as those with other eyelid conditions. The model achieved an area under the receiver operating characteristic curve of 0.918. For validation, we compared the model's performance against nine medical experts-oculoplastic surgeons, general ophthalmologists, and general practitioners-with varied expertise. When tested on a new dataset with varied image quality, the model's performance remained statistically comparable to that of human graders. Our findings underscore the potential to enhance telemedicine services for blepharoptosis detection.
眼睑下垂是一种可导致视力可逆性丧失的公认原因，也是神经系统问题的非特异性指标，偶尔会预示危及生命的情况。目前，诊断依赖于人类专业知识和眼睑检查，大多数现有的人工智能算法主要集中在专门设置下的眼睑定位。本研究引入了一种基于卷积神经网络的深度学习模型，用于在更现实的条件下检测眼睑下垂。我们的模型使用来自眼睑下垂患者以及其他眼睑疾病患者的高质量眶周图像进行训练和测试。该模型的受试者工作特征曲线下面积为 0.918。为了验证，我们将模型的性能与 9 位医学专家——眼整形外科医生、普通眼科医生和全科医生——进行了比较，这些专家的专业知识水平各不相同。当在具有不同图像质量的新数据集上进行测试时，该模型的性能与人类评分者相比仍然具有统计学可比性。我们的研究结果强调了提高眼睑下垂检测远程医疗服务的潜力。
