Muhsin Zahra J, Qahwaji Rami, AlShawabkeh Mo'ath, AlRyalat Saif Aldeen, Al Bdour Muawyah, Al-Taee Majid
Department of Computer Science, University of Bradford, Bradford, BD7 1DP, UK.
Al-Taif Eye Center, Sulaiman Al Hadidi Street, Amman, Jordan.
Eye Vis (Lond). 2024 Jul 8;11(1):28. doi: 10.1186/s40662-024-00394-1.
This study proposes a decision support system created in collaboration with machine learning experts and ophthalmologists for detecting keratoconus (KC) severity. The system employs an ensemble machine model and minimal corneal measurements.
A clinical dataset is initially obtained from Pentacam corneal tomography imaging devices, which undergoes pre-processing and addresses imbalanced sampling through the application of an oversampling technique for minority classes. Subsequently, a combination of statistical methods, visual analysis, and expert input is employed to identify Pentacam indices most correlated with severity class labels. These selected features are then utilized to develop and validate three distinct machine learning models. The model exhibiting the most effective classification performance is integrated into a real-world web-based application and deployed on a web application server. This deployment facilitates evaluation of the proposed system, incorporating new data and considering relevant human factors related to the user experience.
The performance of the developed system is experimentally evaluated, and the results revealed an overall accuracy of 98.62%, precision of 98.70%, recall of 98.62%, F1-score of 98.66%, and F2-score of 98.64%. The application's deployment also demonstrated precise and smooth end-to-end functionality.
The developed decision support system establishes a robust basis for subsequent assessment by ophthalmologists before potential deployment as a screening tool for keratoconus severity detection in a clinical setting.
本研究提出了一种与机器学习专家和眼科医生合作创建的用于检测圆锥角膜（KC）严重程度的决策支持系统。该系统采用集成机器学习模型和最少的角膜测量数据。
最初从Pentacam角膜断层扫描成像设备获取临床数据集，对其进行预处理，并通过对少数类应用过采样技术来解决采样不均衡问题。随后，采用统计方法、视觉分析和专家意见相结合的方式，确定与严重程度分类标签最相关的Pentacam指标。然后利用这些选定的特征来开发和验证三种不同的机器学习模型。将表现出最有效分类性能的模型集成到基于网络的实际应用程序中，并部署在网络应用服务器上。这种部署便于对所提出的系统进行评估，纳入新数据并考虑与用户体验相关的相关人为因素。
对所开发系统的性能进行了实验评估，结果显示总体准确率为98.62%，精确率为98.70%，召回率为98.62%，F1分数为98.66%，F2分数为98.64%。该应用程序的部署还展示了精确且流畅的端到端功能。
所开发的决策支持系统为眼科医生后续评估奠定了坚实基础，有望作为临床环境中圆锥角膜严重程度检测的筛查工具进行潜在部署。
