{"title":"Deep Learning for Lumbar Disc Herniation Diagnosis and Treatment Decision-Making Using Magnetic Resonance Imagings: A Retrospective Study.","authors":"Yuanlong He, Zhong He, Yong Qiu, Zheng Liu, Aibing Huang, Chunmao Chen, Jian Bian","doi":"10.1016/j.wneu.2025.123728","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":23906,"journal":{"name":"World neurosurgery","volume":" ","pages":"123728"},"PeriodicalIF":1.9000,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"World neurosurgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.wneu.2025.123728","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Deep Learning for Lumbar Disc Herniation Diagnosis and Treatment Decision-Making Using Magnetic Resonance Imagings: A Retrospective Study.
Background: 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.
Methods: 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.
Results: 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.
Conclusions: 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.
期刊介绍：
World Neurosurgery has an open access mirror journal World Neurosurgery: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review.
The journal''s mission is to:
-To provide a first-class international forum and a 2-way conduit for dialogue that is relevant to neurosurgeons and providers who care for neurosurgery patients. The categories of the exchanged information include clinical and basic science, as well as global information that provide social, political, educational, economic, cultural or societal insights and knowledge that are of significance and relevance to worldwide neurosurgery patient care.
-To act as a primary intellectual catalyst for the stimulation of creativity, the creation of new knowledge, and the enhancement of quality neurosurgical care worldwide.
-To provide a forum for communication that enriches the lives of all neurosurgeons and their colleagues; and, in so doing, enriches the lives of their patients.
Topics to be addressed in World Neurosurgery include: EDUCATION, ECONOMICS, RESEARCH, POLITICS, HISTORY, CULTURE, CLINICAL SCIENCE, LABORATORY SCIENCE, TECHNOLOGY, OPERATIVE TECHNIQUES, CLINICAL IMAGES, VIDEOS
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