Askarian Behnam, Askarian Amin, Tabei Fatemehsadat, Chong Jo Woon
College of Engineering, West Texas A&M University, Canyon, TX 79016, USA.
Askarian Clinic, Shiraz 71877-75778, Iran.
Sensors (Basel). 2025 Feb 21;25(5):1316. doi: 10.3390/s25051316.
Keratoconus (KC) is a progressive eye disease and a major cause of vision impairment and blindness worldwide. Early diagnosis is crucial for effective management, yet conventional diagnostic methods rely on expensive and bulky imaging devices, limiting accessibility, especially in resource-constrained settings. This paper introduces a novel smartphone-based approach for the early detection of KC, leveraging screen-projected Placido disc patterns and an advanced image processing framework. Unlike traditional corneal topographers, our method utilizes a unique Placido disc projection technique and a machine learning-based classification model to analyze corneal irregularities with high precision. With a sensitivity of 96.08%, specificity of 97.96%, and overall accuracy of 97% on our dataset, the proposed system demonstrates exceptional diagnostic reliability. By transforming a standard smartphone into an effective screening tool, this innovation provides an affordable, portable, and user-friendly solution for early KC detection, bridging the gap in eye care accessibility and reducing the global burden of undiagnosed keratoconus.
圆锥角膜（KC）是一种进行性眼病，是全球视力损害和失明的主要原因。早期诊断对于有效治疗至关重要，但传统的诊断方法依赖于昂贵且笨重的成像设备，限制了其可及性，尤其是在资源有限的环境中。本文介绍了一种基于智能手机的新型圆锥角膜早期检测方法，该方法利用屏幕投射的普拉西多盘图案和先进的图像处理框架。与传统的角膜地形图仪不同，我们的方法采用独特的普拉西多盘投影技术和基于机器学习的分类模型，以高精度分析角膜不规则性。在我们的数据集中，该系统的灵敏度为96.08%，特异性为97.96%，总体准确率为97%，显示出卓越的诊断可靠性。通过将标准智能手机转变为有效的筛查工具，这一创新为圆锥角膜的早期检测提供了一种经济实惠、便于携带且用户友好的解决方案，弥合了眼部护理可及性方面的差距，减轻了全球未诊断圆锥角膜的负担。
