收稿日期: 2024-12-12
网络出版日期: 2025-03-31
基金资助
2024年政府资助临床医学优秀人才培养项目(ZF2024216)
Research progress on the application of deep learning in lumbar spine disease
Received date: 2024-12-12
Online published: 2025-03-31
胡高凯 , 牛亚楠 , 龚玉康 , 胡阳 , 徐瑞轩 , 高文山 . 深度学习在腰椎疾病诊断、手术规划及术后预测中的应用研究进展[J]. 实用医学杂志, 2025 , 41(6) : 921 -928 . DOI: 10.3969/j.issn.1006-5725.2025.06.023
Deep learning （DL） is a machine learning technique that emulates the human brain's functionality through multi?layered neural network models， enabling it to learn and extract features from data， thereby facilitating the automatic processing and learning of complex tasks. DL has achieved numerous significant breakthroughs in areas such as image recognition， speech recognition， and natural language processing， becoming one of the most prominent technologies in the field of artificial intelligence. With the rapid advancement of DL technology， its application in the medical field has yielded remarkable outcomes， offering new possibilities for the diagnosis and treatment of lumbar diseases. This review aims to elucidate the application and research progress of DL in diagnosing， planning surgeries， and predicting postoperative efficacy for lumbar spine diseases.
| 1 | MAZUROWSKI M A， BUDA M， SAHA A， et al. Deep learning in radiology： An overview of the concepts and a survey of the state of the art with focus on MRI［J］. J Magn Reson Imaging，2019，49（4）：939-954. doi:10.1002/jmri.26534 |
| 2 | HUANG J， SHEN H， WU J， et al. Spine Explorer： A deep learning based fully automated program for efficient and reliable quantifications of the vertebrae and discs on sagittal lumbar spine MR images［J］. Spine J，2020， 20 （4）： 590-599. doi:10.1016/j.spinee.2019.11.010 |
| 3 | 冯鹏程，曹圣伟，覃兵，等. 基于深度学习的人体腰椎MRI图像自动分割［J］. 生命科学仪器，2023，21（5）：53-57. |
| 4 | 李新彤，姚宁，闫东，等. 基于V-Net卷积神经网络深度学习模型自动分割腰椎CT图像中的椎旁肌［J］. 中国医学影像技术，2023，39（6）：890-894. |
| 5 | LIAWRUNGRUEANG W， KIM P， KOTHEERANURAK V， et al. Automatic Detection， Classification， and Grading of Lumbar Intervertebral Disc Degeneration Using an Artificial Neural Network Model［J］. Diagnostics （Basel），2023，13（4）：663. doi:10.3390/diagnostics13040663 |
| 6 | POJSKIC M， BISSON E， OERTEL J， et al. Lumbar disc herniation： Epidemiology， clinical and radiologic diagnosis WFNS spine committee recommendations［J］. World Neurosurg X，2024，22：100279. doi:10.1016/j.wnsx.2024.100279 |
| 7 | PRISILLA A A， GUO Y L， JAN Y K， et al. An approach to the diagnosis of lumbar disc herniation using deep learning models［J］. Front Bioeng Biotechnol，2023，11：1247112. doi:10.3389/fbioe.2023.1247112 |
| 8 | 韦祎，赵经纬，印宏坤，等. 基于Unet的深度学习模型在腰椎间盘突出诊断中的应用研究［J］. 中国数字医学，2023，18（12）：58-63. |
| 9 | PAN Q， ZHANG K， HE L， et al. Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks： Method Development Study［J］. JMIR Med Inform， 2021，9（5）：e14755. doi:10.2196/14755 |
| 10 | SUSTERSIC T， RANKOVIC V， MILOVANOVIC V， et al. A Deep Learning Model for Automatic Detection and Classification of Disc Herniation in Magnetic Resonance Images［J］. IEEE J Biomed Health Inform，2022，26（12）：6036-6046. doi:10.1109/jbhi.2022.3209585 |
| 11 | ZHANG W， CHEN Z， SU Z，et al. Deep learning‐based detection and classification of lumbar disc herniation on magnetic resonance images［J］. JOR Spine， 2023， 6（3） ： e1276. doi:10.1002/jsp2.1276 |
| 12 | HALLINAN J T P D， ZHU L， YANG K， et al. Deep Learning Model for Automated Detection and Classification of Central Canal， Lateral Recess， and Neural Foraminal Stenosis at Lumbar Spine MRI［J］. Radiology，2021，300（1）：130-138. doi:10.1148/radiol.2021204289 |
| 13 | ZHANG T， ZHU C， ZHAO Y， et al. Deep learning model to classify and monitor idiopathic scoliosis in adolescents using a single smartphone photograph［J］. JAMA Network Open， 2023， 6（8）： e2330617. doi:10.1001/jamanetworkopen.2023.30617 |
| 14 | ZHANG B， YU K， NING Z， et al. Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening： A multicenter retrospective cohort study［J］. Bone， 2020，140：115561. doi:10.1016/j.bone.2020.115561 |
| 15 | XU F， XIONG Y， YE G， et al. Deep learning-based artificial intelligence model for classification of vertebral compression fractures： A multicenter diagnostic study［J］. Front Endocrinol （Lausanne）， 2023，14：1025749. doi:10.3389/fendo.2023.1025749 |
| 16 | SHI W， XU T， YANG H， et al. Attention Gate Based Dual-Pathway Network for Vertebra Segmentation of X-Ray Spine Images［J］. IEEE J Biomed Health Inform，2022，26（8）：3976-3987. doi:10.1109/jbhi.2022.3158968 |
| 17 | 冯世庆. 计算机视觉与腰椎退行性疾病［J］. 山东大学学报（医学版），2023，61（3）：1-6. |
| 18 | MORBéE L， CHEN M， HERREGODS N， et al. MRI-based synthetic CT of the lumbar spine： Geometric measurements for surgery planning in comparison with CT［J］. Eur J Radiol，2021，144：109999. doi:10.1016/j.ejrad.2021.109999 |
| 19 | LEWANDROWSKI K U， MURALEEDHARAN N， EDDY S A， et al. Reliability analysis of deep learning algorithms for reporting of routine lumbar MRI scans［J］. Int J Spine Surg， 2020， 14（s3）： S98-S107. doi:10.14444/7131 |
| 20 | MAO R Q， LAN L， KAY J， et al. Immersive Virtual Reality for Surgical Training： A Systematic Review［J］. J Surg Res，2021，268：40-58. doi:10.1016/j.jss.2021.06.045 |
| 21 | BHANDARI M， ZEFFIRO T， REDDIBOINA M，et al. Artificial intelligence and robotic surgery： Current perspective and future directions［J］. Curr Opin Urol，2020，30（1）：48-54. doi:10.1097/mou.0000000000000692 |
| 22 | YANG X， WANG L， YANG Q， et al. Neurological Safety of Endoscopic Transforaminal Lumbar Interbody Fusion： A Magnetic Resonance Neurography Study［J］. Spine （Phila Pa 1976），2023，48（5）：344-349. doi:10.1097/brs.0000000000004496 |
| 23 | LI T， WU G， DONG Y， et al. Kambin's triangle-related data based on magnetic resonance neurography and its role in percutaneous transforaminal endoscopic lumbar interbody fusion［J］. J Orthop Surg Res，2022，17（1）：543. doi:10.1186/s13018-022-03428-3 |
| 24 | LIPPROSS S， JüNEMANN K P， OSMONOV D， et al. Robot assisted spinal surgery- A technical report on the use of DaVinci in orthopaedics［J］. J Orthop，2019，19：50-53. doi:10.1016/j.jor.2019.11.045 |
| 25 | ZHANG Q， HAN X G， XU Y F， et al. Robot-Assisted Versus Fluoroscopy-Guided Pedicle Screw Placement in Transforaminal Lumbar Interbody Fusion for Lumbar Degenerative Disease［J］. World Neurosurg，2019，125：e429-e434. doi:10.1016/j.wneu.2019.01.097 |
| 26 | MATUR AV， PALMISCIANO P， DUAH H O， et al. Robotic and navigated pedicle screws are safer and more accurate than fluoroscopic freehand screws： A systematic review and meta-analysis［J］. Spine J， 2023，23（2）：197-208. doi:10.1016/j.spinee.2022.10.006 |
| 27 | ABEL F， AVRUMOVA F， GOLDMAN S N， et al. Robotic-navigated assistance in spine surgery［J］. Bone Joint J，2023，105-B（5）：543-550. doi:10.1302/0301-620x.105b5.bjj-2022-0810.r3 |
| 28 | LI Y， CHEN L， LIU Y， et al. Accuracy and safety of robot-assisted cortical bone trajectory screw placement： A comparison of robot-assisted technique with fluoroscopy-assisted approach［J］. BMC Musculoskelet Disord，2022，23（1）：328. doi:10.1186/s12891-022-05206-y |
| 29 | LEE N J， BUCHANAN I A， ZUCKERMANN S L， et al. What Is the Comparison in Robot Time per Screw， Radiation Exposure， Robot Abandonment， Screw Accuracy， and Clinical Outcomes Between Percutaneous and Open Robot-Assisted Short Lumbar Fusion？： A Multicenter， Propensity-Matched Analysis of 310 Patients［J］. Spine （Phila Pa 1976），2022，47（1）：42-48. doi:10.1097/brs.0000000000004132 |
| 30 | SIEMIONOW K B， KATCHKO K M， LEWICKI P， et al. Augmented reality and artificial intelligence-assisted surgical navigation： Technique and cadaveric feasibility study［J］. J Craniovertebr Junction Spine，2020， 11（2）：81-85. doi:10.4103/jcvjs.jcvjs_48_20 |
| 31 | GHAEDNIA H， FOURMAN M S， LANS A， et al. Augmented and virtual reality in spine surgery， current applications and future potentials［J］. Spine J， 2021， 21（10）： 1617-1625. doi:10.1016/j.spinee.2021.03.018 |
| 32 | JO Y Y， JANG J H， KWON J， et al. Predicting intraoperative hypotension using deep learning with waveforms of arterial blood pressure， electroencephalogram， and electrocardiogram： Retrospective study［J］. PLoS one， 2022， 17（8）： e0272055. doi:10.1371/journal.pone.0272055 |
| 33 | RUSHTON A， HENEGHAN N R， HEYMANS M W， et al. Clinical course of pain and disability following primary lumbar discectomy： Systematic review and meta-analysis［J］. Eur Spine J， 2020，29（7）：1660-1670. doi:10.1007/s00586-019-06272-y |
| 34 | HOPKINS B S， MAZMUDAR A， DRISCOLL C， et al. Using artificial intelligence （AI） to predict postoperative surgical site infection： A retrospective cohort of 4046 posterior spinal fusions［J］. Clin Neurol Neurosurg， 2020，192：105718. doi:10.1016/j.clineuro.2020.105718 |
| 35 | ITO S， NAKASHIMA H， YOSHII T， et al. Deep learning-based prediction model for postoperative complications of cervical posterior longitudinal ligament ossification［J］. Eur Spine J， 2023，32（11）：3797-3806. doi:10.1007/s00586-023-07562-2 |
| 36 | BERG B， GOROSITO M A， FJELD O， et al. Machine Learning Models for Predicting Disability and Pain Following Lumbar Disc Herniation Surgery［J］. JAMA Netw Open， 2024，7（2）：e2355024. doi:10.1001/jamanetworkopen.2023.55024 |
| 37 | CHEN Y， LIN F， WANG K， et al. Development of a predictive model for 1-year postoperative recovery in patients with lumbar disk herniation based on deep learning and machine learning［J］. Front Neurol， 2024， 15： 1255780. doi:10.3389/fneur.2024.1255780 |
| 38 | KHOR S， LAVALLEE D， CIZIK A， et al. Development and validation of a prediction model for pain and functional outcomes after lumbar spine surgery［J］. JAMA Surg，2018，153（12）：e183804. doi:10.1001/jamasurg.2018.0072 |
| 39 | CINNERA A M， MORONE G， IOSA M， et al. Artificial neural network analysis of factors affecting functional independence recovery in patients with lumbar stenosis after neurosurgery treatment： An observational cohort study［J］. J Orthop， 2024， 55： 38-43. doi:10.1016/j.jor.2024.04.003 |
| 40 | HORNUNG A L， BARAJAS J N， RUDISILL S S， et al. Prediction of lumbar disc herniation resorption in symptomatic patients： A prospective， multi-imaging and clinical phenotype study［J］. Spine J，2023，23（2）：247-260. doi:10.1016/j.spinee.2022.10.003 |
| 41 | HOPKINS B S， MAZMUDAR A， DRISCOLL C， et al. Using artificial intelligence （AI） to predict postoperative surgical site infection： A retrospective cohort of 4046 posterior spinal fusions［J］. Clin Neurol Neurosurg，2020，192：105718. doi:10.1016/j.clineuro.2020.105718 |
| 42 | RYU S M， SEO S W， LEE S H，et al. Novel prognostication of patients with spinal and pelvic chondrosarcoma using deep survival neural networks［J］. BMC Med Inform Decis Mak，2020，20（1）：3. doi:10.1186/s12911-019-1008-4 |
| 43 | KIM J K， WANG M X， CHANG M C，et al. Deep Learning Algorithm Trained on Lumbar Magnetic Resonance Imaging to Predict Outcomes of Transforaminal Epidural Steroid Injection for Chronic Lumbosacral Radicular Pain［J］. Pain Physician，2022，25（8）：587-592. |
| 44 | JUJJAVARAPU C， SURI P， PEJAVER V， et al. Predicting decompression surgery by applying multimodal deep learning to patients' structured and unstructured health data［J］. BMC Med Inform Decis Mak，2023，23（1）：2. doi:10.1186/s12911-022-02096-x |
| 45 | MCDONNELL J M， EVANS S R， MCCARTHY L， et al. The diagnostic and prognostic value of artificial intelligence and artificial neural networks in spinal surgery： A narrative review［J］. Bone Joint J， 2021， 103（9）： 1442-1448. doi:10.1302/0301-620x.103b9.bjj-2021-0192.r1 |
| 46 | Azad T D， Vattipally V N， Ames C P. Personalizing adult spinal deformity surgery through multimodal artificial intelligence［J］.Acta Orthopaedica Traumatologica Turcica， 2024， 58（2）：80-82. doi:10.5152/j.aott.2024.23215 |
/
| 〈 |
|
〉 |