Institute of Traditional Chinese Medicine, Ningxia Medical University, Yinchuan 750000, China.
Weifang Engineering Vocational University, Weifang, Shandong Province 262500, China.
Comput Math Methods Med. 2022 Jul 29;2022:7902786. doi: 10.1155/2022/7902786. eCollection 2022.
Diabetes mellitus is a common chronic noncommunicable disease, the main manifestation of which is the long-term high blood sugar level in patients due to metabolic disorders. However, due to excessive reliance on the clinical experience of ophthalmologists, our diagnosis takes a long time, and it is prone to missed diagnosis and misdiagnosis. In recent years, with the development of deep learning, its application in the auxiliary diagnosis of diabetic retinopathy has become possible. How to use the powerful feature extraction ability of deep learning algorithm to realize the mining of massive medical data is of great significance. Therefore, under the action of computer-aided technology, this paper processes and analyzes the retinal images of the fundus through traditional image processing and convolutional neural network-related methods, so as to achieve the role of assisting clinical treatment. Based on the admission records of diabetic patients after data analysis and feature processing, this paper uses an improved convolutional neural network algorithm to establish a model for predicting changes in diabetic conditions. The model can assist doctors to judge the patient's treatment effect by using it based on the case records of inpatient diagnosis and treatment and to predict the risk of readmission of inpatients after discharge. It also can help to judge the effectiveness of the treatment plan. The results of the study show that the model proposed in this paper has a lower probability of misjudging patients with poor recovery as good recovery, and the prediction is more accurate.
糖尿病是一种常见的慢性非传染性疾病，其主要表现为患者由于代谢紊乱而长期处于高血糖水平。然而，由于过分依赖眼科医生的临床经验，我们的诊断时间较长，容易出现漏诊和误诊。近年来，随着深度学习的发展，其在糖尿病视网膜病变的辅助诊断中的应用成为可能。如何利用深度学习算法强大的特征提取能力来挖掘海量医疗数据具有重要意义。因此，在计算机辅助技术的作用下，本文通过传统图像处理和卷积神经网络相关方法对眼底视网膜图像进行处理和分析，从而实现辅助临床治疗的作用。基于数据分析和特征处理后的糖尿病患者的入院记录，本文使用改进的卷积神经网络算法为预测糖尿病病情变化建立了一个模型。该模型可以通过使用住院诊断和治疗的病例记录，帮助医生判断患者的治疗效果，并预测出院后住院患者再次入院的风险，还可以帮助判断治疗方案的有效性。研究结果表明，本文提出的模型对恢复不佳的患者误判为恢复良好的概率较低，预测更为准确。
