早产儿视网膜病变(retinopathy of prematurity,ROP)是早产儿和低体重儿的一种病理性血管增生性疾病,且是儿童致盲的首位因素.因此,对早产儿进行视网膜病变筛查并及时干预至关重要.目前,深度学习在医学图像分析领域逐渐取得新的突破,展现出其巨大的应用潜力,为提高早产儿视网膜病变的诊断效率和准确性提供了新的可能性.深度学习通过端对端的方式可有效地提高对医学图像分类性能,从而辅助儿科医师对早产儿视网膜病变进行精准高效的诊断.在早产儿视网膜病变的诊断中,深度学习可应用于疾病的诊断和严重程度的定量评估,通过训练深度学习模型可实现对病变的精准识别和定位,为临床医生提供更全面的诊断信息,提高诊疗水平.这种技术不仅可以减轻儿科医师的工作压力,还可提高诊断的准确性和效率,为患儿提供更好的治疗方案.深度学习与早产儿视网膜病变图像分析相结合,能在保障一定准确性的同时提高此病的筛查效率,降低筛查成本.本文就深度学习在早产儿视网膜附加病变诊断、分期检测、分区检测和严重程度定量评估等方面的应用进行综述,旨在为早产儿视网膜病变的智能诊断与治疗提供参考,为后续进一步的应用研究提供思路.
Application progress of deep learning algorithm in retinopathy of prematurity
Retinopathy of prematurity (ROP) is a pathological vascular proliferative disease in premature and low birth weight infants,and is the leading cause of childhood blindness. Therefore,it is crucial to screen and intervene promptly for retinopathy inpremature infants. Currently, deep learning has made significant breakthroughs in the field of medical image analysis, demonstrating its enormous potential for improving the diagnostic efficiency and accuracy of retinopathy of prematurity. Deep learning mainly improves the performance of medical image classification in an end-to-end manner. Therefore, deep learning can assist pediatricians in the accurate and efficient diagnosis of retinopathy of prematurity and reduce the work pressure of pediatricians. In the diagnosis of retinopathy of prematurity,deep learning can be applied to the diagnosis of the disease and the quantitative assessment of its severity. By training deep learning models,accurate identification and localization of lesions can be achieved, providing more comprehensive diagnostic information to clinical doctors and improving the level of diagnosis and treatment. This technology not only alleviates the workload of pediatric physicians but also enhances the accuracy and efficiency of diagnosis,providing better treatment options for pediatric patients. The combination of deep learning and image analysis can be able to provide a faster and more accurate diagnosis and treatment for retinopathy of prematurity. This paper summarizes the application of deep learning in the diagnosis of plus disease,stage detection,partition detection,and quantitative assessment of severity of retinal additional lesions in premature infants,aiming to provide a reference for the intelligent diagnosis and treatment of retinopathy of prematurity and to provide insights for further application research.
retinopathy of prematurityfundus imagescreeningdeep learningconvolutional neural network
万方数据
维普
