Zhou Zhan, Li Bingbing, Su Jinyu, Fan Xianming, Chen Liang, Tang Song, Zheng Jianqing, Zhang Tong, Meng Zhiyong, Chen Zhimeng, Deng Hongwei, Hu Jianmin, Zhao Jun
Shenzhen Eye Hospital Affiliated to Jinan University, Shenzhen Eye Institute, Shenzhen, China.
The Second Affiliated Hospital of Fujian Medical University, Fujian Province University Engineering Research Center of Assistive Technology for Visual Impairment, Quanzhou, China.
Ann Transl Med. 2020 Jun;8(11):703. doi: 10.21037/atm.2020.02.162.
This study aimed to simulate the visual field (VF) effects of patients with VF defects using deep learning and computer vision technology.
We collected 3,660 Humphrey visual fields (HVFs) as data samples, including 3,263 reliable 24-2 HVFs. The convolutional neural network (CNN) analyzed and converted the grayscale map of reliable samples into structured data. The artificial intelligence (AI) simulations were developed using computer vision technology. In statistical analyses, the pilot study determined 687 reliable samples to conduct clinical trials, and the two independent sample t-tests were used to calculate the difference of the cumulative gray values. Three volunteers evaluated the matching degree of shape and position between the grayscale map and the AI simulation, which was graded from 0 to100 scores. Based on the average ranking, the proportion of good and excellent grades was determined, and thus the reliability of the AI simulations was assessed.
The reliable samples in the experimental data consisted of 1,334 normal samples and 1,929 abnormal samples. Based on the existing mature CNN model, the fully connected layer was integrated to analyze the VF damage parameters of the input images, and the prediction accuracy of the damage type of the VF defects was up to 89%. By mapping the area and damage information in the VF damage parameter quintuple data set into the real scene image and adjusting the darkening effect according to the damage parameter, the visual effects in patients were simulated in the real scene image. In the clinical validation, there was no statistically significant difference in the cumulative gray value (P>0.05). The good and excellent proportion of the average scores reached 96.0%, thus confirming the accuracy of the AI model.
An AI model with high accuracy was established to simulate the visual effects in patients with VF defects.
本研究旨在利用深度学习和计算机视觉技术模拟视野（VF）缺损患者的视野效应。
我们收集了3660份汉弗莱视野（HVF）作为数据样本，其中包括3263份可靠的24-2 HVF。卷积神经网络（CNN）对可靠样本的灰度图进行分析，并将其转换为结构化数据。利用计算机视觉技术进行人工智能（AI）模拟。在统计分析中，初步研究确定了687份可靠样本进行临床试验，并使用两个独立样本t检验来计算累积灰度值的差异。三名志愿者评估灰度图与AI模拟之间形状和位置的匹配程度，评分范围为0至100分。根据平均排名确定优良等级的比例，从而评估AI模拟的可靠性。
实验数据中的可靠样本包括1334份正常样本和1929份异常样本。基于现有的成熟CNN模型，集成全连接层以分析输入图像的视野损伤参数，视野缺损损伤类型的预测准确率高达89%。通过将视野损伤参数五元组数据集中的面积和损伤信息映射到真实场景图像中，并根据损伤参数调整暗化效果，在真实场景图像中模拟患者的视觉效果。在临床验证中，累积灰度值无统计学显著差异（P>0.05）。平均评分的优良比例达到96.0%，从而证实了AI模型的准确性。
建立了一个高精度的AI模型来模拟视野缺损患者的视觉效果。
