{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T19:54:26Z","timestamp":1789761266484,"version":"4.0.1"},"reference-count":46,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:00:00Z","timestamp":1775520000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Artificial intelligence (AI) is increasingly transforming spine surgery, with expanding applications in diagnostics, intraoperative imaging, and surgical navigation. As the field advances toward greater precision and safety, machine learning (ML) and deep learning technologies are being integrated to augment surgeon expertise and optimize operative workflows. In particular, AI-driven innovations in image acquisition and navigation are reshaping intraoperative decision-making and technical execution. This narrative review provides an overview of AI applications relevant to intraoperative imaging and navigation in spine surgery. We begin by defining key concepts in AI, ML, and deep learning and briefly outline the historical evolution of AI within spine practice. We then examine current capabilities in image recognition and automated pathology detection, emphasizing their clinical relevance. Given the central role of imaging accuracy in modern navigation-assisted procedures, we review conventional acquisition platforms, including intraoperative computed tomography (CT) systems (e.g., O-arm, GE, Airo), surface-based registration to preoperative CT (Stryker, Medtronic), and optical surface mapping technologies (e.g., 7D Surgical). Emerging AI-optimized advancements are subsequently discussed, including low-dose intraoperative CT protocols, expanded scan windows, metal artifact reduction algorithms, integration of 2D fluoroscopy with preoperative CT datasets, and 3D reconstruction derived from 2D imaging. These developments aim to improve image quality, reduce radiation exposure, and enhance navigational accuracy. By synthesizing current evidence and technological progress, this review highlights how AI-enhanced imaging systems are redefining intraoperative spine surgery and shaping the future of precision-based care. The primary purpose of this review is to outline the applications of AI and its potential for perioperative and intraoperative optimization, including radiation exposure reduction, workflow streamlining, preoperative planning, robot-assisted surgery, and navigation. The secondary purpose is to define AI, machine learning, and deep learning within the medical context, describe image and pathology recognition, and provide a historical overview of AI in orthopedic spine surgery.<\/jats:p>","DOI":"10.3390\/jcm15072779","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T09:40:31Z","timestamp":1775554831000},"page":"2779","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial Intelligence in Intraoperative Imaging and Navigation for Spine Surgery: A Narrative Review"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9556-2596","authenticated-orcid":false,"given":"Mina","family":"Girgis","sequence":"first","affiliation":[{"name":"Geisinger Northeast Orthopaedic Surgery Residency, Wilkes-Barre, PA 18702, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7099-2906","authenticated-orcid":false,"given":"Allison","family":"Kelliher","sequence":"additional","affiliation":[{"name":"Geisinger Northeast Orthopaedic Surgery Residency, Wilkes-Barre, PA 18702, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael S.","family":"Pheasant","sequence":"additional","affiliation":[{"name":"Geisinger Northeast Orthopaedic Surgery Residency, Wilkes-Barre, PA 18702, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7086-4355","authenticated-orcid":false,"given":"Alex","family":"Tang","sequence":"additional","affiliation":[{"name":"Geisinger Northeast Orthopaedic Surgery Residency, Wilkes-Barre, PA 18702, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siddharth","family":"Badve","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Geisinger Medical Center, Danville, PA 17822, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Spine Surgery, Inova Orthopedics\u2014Springfield, Alexandria, VA 22310, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yagi, M., Yamanouchi, K., Fujita, N., Funao, H., and Ebata, S. (2023). Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning. J. Clin. Med., 12.","DOI":"10.3390\/jcm12134188"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1097\/BRS.0000000000004685","article-title":"Projected Lifetime Cancer Risk Associated with Intraoperative Computed Tomography for Lumbar Spine Surgery","volume":"48","author":"Crawford","year":"2023","journal-title":"Spine"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"E7","DOI":"10.3171\/2021.10.FOCUS21467","article-title":"Workflow and performance of intraoperative CT, cone-beam CT, and robotic cone-beam CT for spinal navigation in 503 consecutive patients","volume":"52","author":"Kendlbacher","year":"2022","journal-title":"Neurosurg. Focus"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"196","DOI":"10.3171\/2013.11.SPINE13413","article-title":"The accuracy of pedicle screw placement using intraoperative image guidance systems","volume":"20","author":"Mason","year":"2014","journal-title":"J. Neurosurg. Spine"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"E1329","DOI":"10.1097\/BRS.0000000000002704","article-title":"Comparison of the Clinical Accuracy Between Point-to-Point Registration and Auto-Registration Using an Active Infrared Navigation System","volume":"43","author":"Zhao","year":"2018","journal-title":"Spine"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s00586-021-07082-x","article-title":"Comparison of radiation exposure and surgery time between an intraoperative CT with automatic surface registration and a preoperative CT with manual surface registration in navigated spinal surgeries","volume":"31","author":"Marzouk","year":"2022","journal-title":"Eur. Spine J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1186\/s40001-025-02511-9","article-title":"Applications of machine learning and deep learning in musculoskeletal medicine: A narrative review","volume":"30","author":"Feierabend","year":"2025","journal-title":"Eur. J. Med. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1227\/neu.0000000000002660","article-title":"Machine Learning in Spine Surgery: A Narrative Review","volume":"94","author":"Adida","year":"2024","journal-title":"Neurosurgery"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2018.05.005","article-title":"Automated comprehensive Adolescent Idiopathic Scoliosis assessment using MVC-Net","volume":"48","author":"Wu","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rosenberg, G.S., Cina, A., Schir\u00f3, G.R., Giorgi, P.D., Gueorguiev, B., Alini, M., Varga, P., Galbusera, F., and Gallazzi, E. (2022). Artificial Intelligence Accurately Detects Traumatic Thoracolumbar Fractures on Sagittal Radiographs. Medicina, 58.","DOI":"10.3390\/medicina58080998"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"E5","DOI":"10.3171\/2022.1.FOCUS21745","article-title":"Automated prediction of the Thoracolumbar Injury Classification and Severity Score from CT using a novel deep learning algorithm","volume":"52","author":"Doerr","year":"2022","journal-title":"Neurosurg. Focus"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"442","DOI":"10.3340\/jkns.2018.0178","article-title":"Machine Learning Model to Predict Osteoporotic Spine with Hounsfield Units on Lumbar Computed Tomography","volume":"62","author":"Nam","year":"2019","journal-title":"J. Korean Neurosurg. Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2137","DOI":"10.1007\/s00586-022-07311-x","article-title":"External validation of the deep learning system \u201cSpineNet\u201d for grading radiological features of degeneration on MRIs of the lumbar spine","volume":"31","author":"Grob","year":"2022","journal-title":"Eur. Spine J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e863","DOI":"10.1016\/j.wneu.2019.01.201","article-title":"Optical Topographic Imaging for Spinal Intraoperative Three-Dimensional Navigation in Mini-Open Approaches: A Prospective Cohort Study of Initial Preclinical and Clinical Feasibility","volume":"125","author":"Guha","year":"2019","journal-title":"World Neurosurg."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"147","DOI":"10.3171\/2018.12.SPINE181397","article-title":"Machine learning for automated 3-dimensional segmentation of the spine and suggested placement of pedicle screws based on intraoperative cone-beam computer tomography","volume":"31","author":"Buerger","year":"2019","journal-title":"J. Neurosurg. Spine"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/S0010-4825(00)00013-5","article-title":"Automated scoring of patient pain drawings using artificial neural networks: Efforts toward a low back pain triage application","volume":"30","author":"Sanders","year":"2000","journal-title":"Comput. Biol. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1590\/S0102-36162011000200014","article-title":"Computational analysis based on artificial neural networks for aiding in diagnosing osteoarthritis of the lumbar spine","volume":"46","author":"Veronezi","year":"2011","journal-title":"Rev. Bras. Ortop. (Engl. Ed.)"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e14755","DOI":"10.2196\/14755","article-title":"Automatically Diagnosing Disk Bulge and Disk Herniation with Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study","volume":"9","author":"Pan","year":"2021","journal-title":"JMIR Med. Inform."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1148\/radiol.2021204289","article-title":"Deep Learning Model for Automated Detection and Classification of Central Canal, Lateral Recess, and Neural Foraminal Stenosis at Lumbar Spine MRI","volume":"300","author":"Hallinan","year":"2021","journal-title":"Radiology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"38","DOI":"10.14444\/3038","article-title":"Reliability of the Planned Pedicle Screw Trajectory versus the Actual Pedicle Screw Trajectory using Intra-operative 3D CT and Image Guidance","volume":"10","author":"Miller","year":"2016","journal-title":"Int. J. Spine Surg."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2339","DOI":"10.1007\/s00586-017-5453-4","article-title":"Accuracy of pedicle screw insertion by AIRO\u00ae intraoperative CT in complex spinal deformity assessed by a new classification based on technical complexity of screw insertion","volume":"27","author":"Rajasekaran","year":"2018","journal-title":"Eur. Spine J."},{"key":"ref_22","unstructured":"GE HealthCare (2025, December 21). Routine CBCT Imaging in Spine Surgery with OEC 3D. Available online: https:\/\/www.gehealthcare.co.uk\/insights\/article\/routine-cbct-imaging-in-spine-surgery-with-oec-3d."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1227\/NEU.0000000000000587","article-title":"Computed Tomography-Based Image-Guided System in Spinal Surgery","volume":"11","author":"Costa","year":"2015","journal-title":"Oper. Neurosurg."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2919","DOI":"10.1097\/BRS.0b013e3181b77b19","article-title":"Intraoperative Computed Tomography with Integrated Navigation System in Spinal Stabilizations","volume":"34","author":"Zausinger","year":"2009","journal-title":"Spine"},{"key":"ref_25","unstructured":"(2025, August 11). O-armTM Surgical Imaging System. Available online: https:\/\/europe.medtronic.com\/content\/dam\/medtronic-wide\/public\/western-europe\/medical-specialties\/spine-surgery\/o-arm-4-3-o2-brochure-en.pdf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"055008","DOI":"10.1088\/1361-6560\/abde96","article-title":"Long-length tomosynthesis and 3D-2D registration for intraoperative assessment of spine instrumentation","volume":"66","author":"Zhang","year":"2021","journal-title":"Phys. Med. Biol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3483","DOI":"10.1002\/mp.13652","article-title":"Known-component 3D image reconstruction for improved intraoperative imaging in spine surgery: A clinical pilot study","volume":"46","author":"Zhang","year":"2019","journal-title":"Med. Phys."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1007\/s10278-020-00324-2","article-title":"Evaluation of Software-Based Metal Artifact Reduction in Intraoperative 3D Imaging of the Spine Using a Mobile Cone Beam CT","volume":"33","author":"Privalov","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s00586-004-0797-y","article-title":"Surface-based registration accuracy of CT-based image-guided spine surgery","volume":"14","author":"Tamura","year":"2005","journal-title":"Eur. Spine J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.spinee.2004.10.048","article-title":"Accuracy of single-time, multilevel registration in image-guided spinal surgery","volume":"5","author":"Papadopoulos","year":"2005","journal-title":"Spine J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"101641","DOI":"10.1016\/j.inat.2022.101641","article-title":"First reported use of machine vision image guided system for unstable thoracolumbar fusion: Technical case report","volume":"30","author":"Malacon","year":"2022","journal-title":"Interdiscip. Neurosurg."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"S28","DOI":"10.14444\/8274","article-title":"Visible Light Navigation in Spine Surgery: My Experience with My First 150 Cases","volume":"16","author":"Stewart","year":"2022","journal-title":"Int. J. Spine Surg."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"E8","DOI":"10.3171\/2021.10.FOCUS21506","article-title":"CT-to-fluoroscopy registration versus scan-and-plan registration for robot-assisted insertion of lumbar pedicle screws","volume":"52","author":"Khan","year":"2022","journal-title":"Neurosurg. Focus"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1177\/2192568219879083","article-title":"Assessment of Surgical Procedural Time, Pedicle Screw Accuracy, and Clinician Radiation Exposure of a Novel Robotic Navigation System Compared with Conventional Open and Percutaneous Freehand Techniques: A Cadaveric Investigation","volume":"10","author":"Vaccaro","year":"2020","journal-title":"Global Spine J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"E1311","DOI":"10.1097\/BRS.0000000000002154","article-title":"Low-Dose Radiation 3D Intraoperative Imaging: How Low Can We Go? An O-Arm, CT Scan, Cadaveric Study","volume":"42","author":"Sarwahi","year":"2017","journal-title":"Spine"},{"key":"ref_36","unstructured":"U.S. Food and Drug Administration (2025, April 11). O-Arm O2 Imaging System, version 4.3.0; 510(k) Premarket Notification (K240465), Available online: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf24\/K240465.pdf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"E417","DOI":"10.1097\/BRS.0000000000001846","article-title":"CT Radiation Dose Reduction in Robot-assisted Pediatric Spinal Surgery","volume":"42","author":"Sensakovic","year":"2017","journal-title":"Spine"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"E11","DOI":"10.3171\/2019.8.FOCUS19621","article-title":"Reliable navigation registration in cranial and spine surgery based on intraoperative computed tomography","volume":"47","author":"Carl","year":"2019","journal-title":"Neurosurg. Focus"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Luchmann, D., Jecklin, S., Cavalcanti, N.A., Laux, C.J., Massalimova, A., Esfandiari, H., Farshad, M., and F\u00fcrnstahl, P. (2024). Spinal navigation with AI-driven 3D-reconstruction of fluoroscopy images: An ex-vivo feasibility study. BMC Musculoskelet. Disord., 25.","DOI":"10.1186\/s12891-024-08052-2"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"109999","DOI":"10.1016\/j.ejrad.2021.109999","article-title":"MRI-based synthetic CT of the lumbar spine: Geometric measurements for surgery planning in comparison with CT","volume":"144","author":"Chen","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"235","DOI":"10.3171\/2025.6.SPINE25213","article-title":"Safety and accuracy of cervical pedicle screw navigation using artificial intelligence\u2013generated, MRI-based synthetic CT versus conventional CT","volume":"44","author":"Lafranca","year":"2025","journal-title":"J. Neurosurg. Spine"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1007\/s00586-025-09151-x","article-title":"The safety and accuracy of radiation-free spinal navigation using a short, scoliosis-specific BoneMRI-protocol, compared to CT","volume":"35","author":"Lafranca","year":"2026","journal-title":"Eur. Spine J."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"De Jesus Encarnacion Ramirez, M., Chmutin, G., Nurmukhametov, R., Soto, G.R., Kannan, S., Piavchenko, G., Nikolenko, V., Efe, I.E., Romero, A.R., and Mukengeshay, J.N. (2024). Integrating Augmented Reality in Spine Surgery: Redefining Precision with New Technologies. Brain Sci., 14.","DOI":"10.3390\/brainsci14070645"},{"key":"ref_44","first-page":"e100177","article-title":"Artificial Intelligence and Augmented Reality in Orthopedic Surgery: A Narrative Review of Current Applications and Future Directions","volume":"17","year":"2025","journal-title":"Cureus"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"100787","DOI":"10.1016\/j.xnsj.2025.100787","article-title":"Implementation of artificial intelligence (AI) in ASD treatment","volume":"24","author":"Chatzis","year":"2025","journal-title":"N. Am. Spine Soc. J. (NASSJ)"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Jecklin, S., Jancik, C., Farshad, M., F\u00fcrnstahl, P., and Esfandiari, H. (2022). X23D\u2014Intraoperative 3D Lumbar Spine Shape Reconstruction Based on Sparse Multi-View X-ray Data. J. 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