Deep Learning Model to Predict Postoperative Visual Acuity from Preoperative Multimedia Ophthalmic Data

IF 0.8 Q4 ENGINEERING, BIOMEDICAL Advanced Biomedical Engineering Pub Date : 2020-01-01 DOI:10.14326/abe.9.241
Ryo Otsuki, Osamu Sugiyama, Yuki Mori, M. Miyake, S. Hiragi, Goshiro Yamamoto, L. Santos, Yuta Nakanishi, Yoshikatsu Hosoda, H. Tamura, S. Matsumoto, A. Tsujikawa, T. Kuroda
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引用次数: 2

Abstract

Age-related macular degeneration (AMD) causes visual acuity (VA) loss in people aged ≥ 50 years. Common treatments include intravitreal injection of anti-vascular endothelial growth factor agents such as aflibercept. However, lack of response in some patients makes prediction of posttreatment VA difficult. In this paper, we propose a deep neural network model to predict posttreatment VA using pretreatment medical imaging and patient profile data. The proposed model works with image data (optical coherence tomography and color fundus photograph) and patient profile data including gender, age, affected side and pretreatment decimal visual acuity. The model was tested by comparing mean square errors (MSE) between actual and predicted visual acuity obtained from input of image data alone, input of patient profile data alone, and input of both types of data. When examining the concatenation effectiveness of input of both types of data, the outcomes of concatenation conditions 100:100 and 500:500 were compared. For concatenation condition 100:100, MSE was 0.081 for input of image data alone, 0.052 for input of patient profile data alone, and 0.058 for input of both types of data. For concatenation condition 500:500, the MSE values were 0.081, 0.052, and 0.047, respective-ly. The model proposed provides highly accurate prediction of posttreatment VA and indication of recovery to physicians and patients. The method can handle incomplete images and patient profile data usually collected from patients before treatment.
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利用术前多媒体眼科数据预测术后视力的深度学习模型
年龄相关性黄斑变性(AMD)在年龄≥50岁的人群中导致视力(VA)下降。常见的治疗方法包括玻璃体内注射抗血管内皮生长因子药物,如阿布西普。然而,一些患者缺乏反应使得治疗后VA难以预测。在本文中,我们提出了一个深度神经网络模型来预测治疗后的VA,该模型使用预处理医学图像和患者资料。该模型适用于图像数据(光学相干断层扫描和彩色眼底照片)和患者个人资料,包括性别、年龄、受累侧和预处理后的十进制视力。通过比较单独输入图像数据、单独输入患者轮廓数据以及同时输入两种数据获得的实际与预测视力的均方误差(MSE)对模型进行检验。在检查两种类型数据输入的连接有效性时,比较了连接条件100:100和500:500的结果。在连接条件为100:100时,仅输入图像数据的MSE为0.081,仅输入患者轮廓数据的MSE为0.052,两种数据同时输入的MSE为0.058。对于连接条件500:500,MSE值分别为0.081、0.052和0.047。该模型为医生和患者提供了高度准确的治疗后VA预测和康复指征。该方法可以处理不完整的图像和通常从患者治疗前收集的患者轮廓数据。
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来源期刊
Advanced Biomedical Engineering
Advanced Biomedical Engineering ENGINEERING, BIOMEDICAL-
CiteScore
1.40
自引率
10.00%
发文量
15
审稿时长
15 weeks
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