Huang Junzhang, Shen Ning, Tan Yuexiang, Tang Yongzhong, Ding Zhendong
Department of General Surgery, Lianjiang Traditional Chinese Medicine Hospital, Lianjiang City, Zhanjiang, Guangdong Province, China.
Department of Anesthesiology, Huadu District People's Hospital of Guangzhou, Guangzhou, Guangdong Province, China.
Medicine (Baltimore). 2025 Jun 27;104(26):e43082. doi: 10.1097/MD.0000000000043082.
Diagnosis of hydrocephalus involves a careful check of the patient's history and thorough neurological assessment. The traditional diagnosis has predominantly depended on the professional judgment of physicians based on clinical experience, but with the advancement of precision medicine and individualized treatment, such experience-based methods are no longer sufficient to keep pace with current clinical requirements. To fit this adjustment, the medical community actively devotes itself to data-driven intelligent diagnostic solutions. Building a prognosis prediction model for hydrocephalus has thus become a new focus, among which intelligent prediction systems supported by deep learning offer new technical advantages for clinical diagnosis and treatment decisions. Over the past several years, algorithms of deep learning have demonstrated conspicuous advantages in medical image analysis. Studies revealed that the accuracy rate of the diagnosis of hydrocephalus by magnetic resonance imaging can reach 90% through convolutional neural networks, while their sensitivity and specificity are also better than these of traditional methods. With the extensive use of medical technology in terms of deep learning, its successful use in modeling hydrocephalus prognosis has also drawn extensive attention and recognition from scholars. This review explores the application of deep learning in hydrocephalus diagnosis and prognosis, focusing on image-based, biochemical, and structured data models. Highlighting recent advancements, challenges, and future trajectories, the study emphasizes deep learning's potential to enhance personalized treatment and improve outcomes.
脑积水的诊断需要仔细检查患者病史并进行全面的神经学评估。传统诊断主要依赖医生基于临床经验的专业判断，但随着精准医学和个体化治疗的发展，这种基于经验的方法已不足以满足当前临床需求。为适应这一调整，医学界积极致力于数据驱动的智能诊断解决方案。因此，建立脑积水的预后预测模型已成为新的焦点，其中深度学习支持的智能预测系统为临床诊断和治疗决策提供了新的技术优势。在过去几年中，深度学习算法在医学图像分析中展现出显著优势。研究表明，通过卷积神经网络，磁共振成像诊断脑积水的准确率可达90%，其敏感性和特异性也优于传统方法。随着深度学习在医学技术方面的广泛应用，其在脑积水预后建模中的成功应用也引起了学者们的广泛关注和认可。本综述探讨深度学习在脑积水诊断和预后中的应用，重点关注基于图像、生化和结构化数据的模型。该研究突出了近期进展、挑战和未来发展方向，强调了深度学习在增强个性化治疗和改善治疗效果方面的潜力。
