Zhu Shaojun, Liu Xiangjun, Lu Ying, Zheng Bo, Wu Maonian, Yao Xue, Yang Weihua, Gong Yan
School of Information Engineering, Huzhou University, Huzhou, China.
Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen, China.
Front Neurosci. 2024 May 14;18:1339075. doi: 10.3389/fnins.2024.1339075. eCollection 2024.
Conventional approaches to diagnosing common eye diseases using B-mode ultrasonography are labor-intensive and time-consuming, must requiring expert intervention for accuracy. This study aims to address these challenges by proposing an intelligence-assisted analysis five-classification model for diagnosing common eye diseases using B-mode ultrasound images.
This research utilizes 2064 B-mode ultrasound images of the eye to train a novel model integrating artificial intelligence technology.
The ConvNeXt-L model achieved outstanding performance with an accuracy rate of 84.3% and a Kappa value of 80.3%. Across five classifications (no obvious abnormality, vitreous opacity, posterior vitreous detachment, retinal detachment, and choroidal detachment), the model demonstrated sensitivity values of 93.2%, 67.6%, 86.1%, 89.4%, and 81.4%, respectively, and specificity values ranging from 94.6% to 98.1%. F1 scores ranged from 71% to 92%, while AUC values ranged from 89.7% to 97.8%.
Among various models compared, the ConvNeXt-L model exhibited superior performance. It effectively categorizes and visualizes pathological changes, providing essential assisted information for ophthalmologists and enhancing diagnostic accuracy and efficiency.
使用B超诊断常见眼病的传统方法劳动强度大、耗时久，且为保证准确性需要专家干预。本研究旨在通过提出一种用于使用B超图像诊断常见眼病的智能辅助分析五分类模型来应对这些挑战。
本研究利用2064张眼部B超图像训练一个集成人工智能技术的新型模型。
ConvNeXt-L模型表现出色，准确率为84.3%，Kappa值为80.3%。在五个分类（无明显异常、玻璃体混浊、玻璃体后脱离、视网膜脱离和脉络膜脱离）中，该模型的敏感性值分别为93.2%、67.6%、86.1%、89.4%和81.4%，特异性值在94.6%至98.1%之间。F1分数在71%至92%之间，而AUC值在89.7%至97.8%之间。
在比较的各种模型中，ConvNeXt-L模型表现出卓越性能。它有效地对病理变化进行分类和可视化，为眼科医生提供重要的辅助信息，提高诊断准确性和效率。
