He Yuanlong, He Zhong, Qiu Yong, Liu Zheng, Huang Aibing, Chen Chunmao, Bian Jian
Department of Spine Surgery, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, Jiangsu, China; Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, Jiangsu, China.
Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Nanjing, Jiangsu, China.
World Neurosurg. 2025 Mar;195:123728. doi: 10.1016/j.wneu.2025.123728. Epub 2025 Feb 26.
Lumbar disc herniation (LDH) is a common cause of back and leg pain. Diagnosis relies on clinical history, physical exam, and imaging, with magnetic resonance imaging (MRI) being an important reference standard. While artificial intelligence (AI) has been explored for MRI image recognition in LDH, existing methods often focus solely on disc herniation presence.
We retrospectively analyzed MRI images from patients assessed for surgery by specialists. We then trained deep learning convolutional neural networks to detect LDH on MRI images. This study compared pure AI, pure human, and AI-assisted approaches for diagnosis accuracy and decision time. Statistical analysis evaluated each method's effectiveness.
Our approach demonstrated the potential of deep learning to aid LDH diagnosis and treatment. The AI-assisted group achieved the highest accuracy (94.7%), outperforming both pure AI and pure human approaches. AI integration reduced decision time without compromising accuracy.
Convolutional neural networks effectively assist specialists in initial LDH diagnosis and treatment decisions based on MRI images. This synergy between AI and human expertise improves diagnostic accuracy and efficiency, highlighting the value of AI-assisted diagnosis in clinical practice.
腰椎间盘突出症（LDH）是腰腿痛的常见原因。诊断依赖于临床病史、体格检查和影像学检查，磁共振成像（MRI）是重要的参考标准。虽然人工智能（AI）已被用于探索LDH的MRI图像识别，但现有方法通常仅专注于椎间盘突出的存在情况。
我们回顾性分析了由专家评估手术的患者的MRI图像。然后我们训练深度学习卷积神经网络以在MRI图像上检测LDH。本研究比较了纯AI、纯人工和AI辅助方法在诊断准确性和决策时间方面的表现。统计分析评估了每种方法的有效性。
我们的方法展示了深度学习辅助LDH诊断和治疗的潜力。AI辅助组达到了最高的准确率（94.7%），优于纯AI和纯人工方法。AI整合减少了决策时间且不影响准确性。
卷积神经网络有效地协助专家基于MRI图像进行LDH的初步诊断和治疗决策。AI与人类专业知识之间的这种协同作用提高了诊断准确性和效率，突出了AI辅助诊断在临床实践中的价值。
