Wang Wo, Zheng Xiujuan, Lyu Zhiqing, Li Ni, Chen Jun
Department of Automation, College of Electrical Engineering, Sichuan University, Chengdu 610065, P. R. China.
Key Laboratory of Information and Automation Technology in Sichuan Province, Chengdu 610065, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Oct 25;41(5):1003-1011. doi: 10.7507/1001-5515.202310072.
Glaucoma stands as the leading irreversible cause of blindness worldwide. Regular visual field examinations play a crucial role in both diagnosing and treating glaucoma. Predicting future visual field changes can assist clinicians in making timely interventions to manage the progression of this disease. To integrate temporal and spatial features from past visual field examination results and enhance visual field prediction, a convolutional long short-term memory (ConvLSTM) network was employed to construct a predictive model. The predictive performance of the ConvLSTM model was validated and compared with other methods using a dataset of perimetry tests from the Humphrey field analyzer at the University of Washington (UWHVF). Compared to traditional methods, the ConvLSTM model demonstrated higher prediction accuracy. Additionally, the relationship between visual field series length and prediction performance was investigated. In predicting the visual field using the previous three visual field results of past 1.56.0 years, it was found that the ConvLSTM model performed better, achieving a mean absolute error of 2.255 dB, a root mean squared error of 3.457 dB, and a coefficient of determination of 0.960. The experimental results show that the proposed method effectively utilizes existing visual field examination results to achieve more accurate visual field prediction for the next 0.52.0 years. This approach holds promise in assisting clinicians in diagnosing and treating visual field progression in glaucoma patients.
青光眼是全球不可逆失明的主要原因。定期进行视野检查在青光眼的诊断和治疗中都起着至关重要的作用。预测未来视野变化有助于临床医生及时进行干预，以控制这种疾病的进展。为了整合过去视野检查结果的时间和空间特征并增强视野预测，采用了卷积长短期记忆（ConvLSTM）网络来构建预测模型。使用华盛顿大学汉弗莱视野分析仪（UWHVF）的视野测试数据集对ConvLSTM模型的预测性能进行了验证，并与其他方法进行了比较。与传统方法相比，ConvLSTM模型表现出更高的预测准确性。此外，还研究了视野序列长度与预测性能之间的关系。在使用过去1.5至6.0年的前三个视野结果预测视野时，发现ConvLSTM模型表现更好，平均绝对误差为2.255 dB，均方根误差为3.457 dB，决定系数为0.960。实验结果表明，所提出的方法有效地利用了现有的视野检查结果，能够对未来0.5至2.0年的视野进行更准确的预测。这种方法有望帮助临床医生诊断和治疗青光眼患者的视野进展。
