• Yuanlong He, Zhong He, Yong Qiu, Zheng Liu, Aibing Huang, Chunmao Chen, and Jian Bian.
• Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, The Affiliated Drum Tower Clinical College of Nanjing Medical University; Department of Spine Surgery, The Affiliated Taizhou People's Hospital of Nanjing Medical University.
• World Neurosurg. 2025 Jan 27: 123728123728.

BackgroundLumbar 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.MethodsWe retrospectively analyzed MRI images from patients assessed for surgery by specialists. We then trained deep learning convolutional neural networks (CNNs) 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.ResultsOur 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.ConclusionCNNs 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.Copyright © 2025. Published by Elsevier Inc.

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