Song Wheyming, Lai Ing-Chou
Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407102, Taiwan.
Department of Ophthalmology, Chiayi Chang Gung Memorial Hospital, Puzi City 61363, Taiwan.
Bioengineering (Basel). 2025 May 13;12(5):516. doi: 10.3390/bioengineering12050516.
Glaucoma is a leading cause of irreversible blindness worldwide; therefore, detection of this disease in its early stage is crucial. However, previous efforts to identify early-stage glaucoma have faced challenges, including insufficient accuracy, sensitivity, and specificity. This study presents a concatenated artificial intelligence model that combines two types of input features: fundus images and quantitative retinal thickness parameters derived from macular and peri-papillary retinal nerve fiber layer (RNFL) thickness measurements. These features undergo an intelligent transformation, referred to as "smart preprocessing", to enhance their utility. The model employs two classification approaches: a convolutional neural network approach for processing image features and an artificial neural network approach for analyzing quantitative retinal thickness parameters. To maximize performance, hyperparameters were fine-tuned using a robust methodology for the design of experiments. The proposed AI model demonstrated outstanding performance in early-stage glaucoma detection, outperforming existing models; its accuracy, sensitivity, specificity, precision, and F1-Score all exceeding 0.90.
青光眼是全球不可逆性失明的主要原因；因此，在疾病早期进行检测至关重要。然而，此前识别早期青光眼的努力面临诸多挑战，包括准确性、敏感性和特异性不足。本研究提出了一种串联人工智能模型，该模型结合了两种类型的输入特征：眼底图像以及从黄斑和视乳头周围视网膜神经纤维层（RNFL）厚度测量得出的定量视网膜厚度参数。这些特征会经历一种被称为“智能预处理”的智能变换，以增强其效用。该模型采用两种分类方法：一种用于处理图像特征的卷积神经网络方法，以及一种用于分析定量视网膜厚度参数的人工神经网络方法。为了使性能最大化，使用稳健的实验设计方法对超参数进行了微调。所提出的人工智能模型在早期青光眼检测中表现出色，优于现有模型；其准确性、敏感性、特异性、精确率和F1分数均超过0.90。
