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Tabarestani TQ, Salven DS, Sykes DAW, Bardeesi AM, Bartlett AM, Wang TY, Paturu MR, Dibble CF, Shaffrey CI, Ray WZ, Chi JH, Wiggins WF, Abd-El-Barr MM. Using Novel Segmentation Technology to Define Safe Corridors for Minimally Invasive Posterior Lumbar Interbody Fusion. Oper Neurosurg (Hagerstown) 2023:01787389-990000000-01010. [PMID: 38149852 DOI: 10.1227/ons.0000000000001046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 11/15/2023] [Indexed: 12/28/2023] Open
Abstract
BACKGROUND AND OBJECTIVES There has been a rise in minimally invasive methods to access the intervertebral disk space posteriorly given their decreased tissue destruction, lower blood loss, and earlier return to work. Two such options include the percutaneous lumbar interbody fusion through the Kambin triangle and the endoscopic transfacet approach. However, without accurate preoperative visualization, these approaches carry risks of damaging surrounding structures, especially the nerve roots. Using novel segmentation technology, our goal was to analyze the anatomic borders and relative sizes of the safe triangle, trans-Kambin, and the transfacet corridors to assist surgeons in planning a safe approach and determining cannula diameters. METHODS The areas of the safe triangle, Kambin, and transfacet corridors were measured using commercially available software (BrainLab, Munich, Germany). For each approach, the exiting nerve root, traversing nerve roots, theca, disk, and vertebrae were manually segmented on 3-dimensional T2-SPACE magnetic resonance imaging using a region-growing algorithm. The triangles' borders were delineated ensuring no overlap between the area and the nerves. RESULTS A total of 11 patients (65.4 ± 12.5 years, 33.3% female) were retrospectively reviewed. The Kambin, safe, and transfacet corridors were measured bilaterally at the operative level. The mean area (124.1 ± 19.7 mm2 vs 83.0 ± 11.7 mm2 vs 49.5 ± 11.4 mm2) and maximum permissible cannula diameter (9.9 ± 0.7 mm vs 6.8 ± 0.5 mm vs 6.05 ± 0.7 mm) for the transfacet triangles were significantly larger than Kambin and the traditional safe triangles, respectively (P < .001). CONCLUSION We identified, in 3-dimensional, the borders for the transfacet corridor: the traversing nerve root extending inferiorly until the caudal pedicle, the theca medially, and the exiting nerve root superiorly. These results illustrate the utility of preoperatively segmenting anatomic landmarks, specifically the nerve roots, to help guide decision-making when selecting the optimal operative approach.
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Affiliation(s)
- Troy Q Tabarestani
- Department of Neurosurgery, Duke University School of Medicine, Durham, North Carolina, USA
| | - David S Salven
- Department of Neurosurgery, Duke University School of Medicine, Durham, North Carolina, USA
| | - David A W Sykes
- Department of Neurosurgery, Duke University School of Medicine, Durham, North Carolina, USA
| | - Anas M Bardeesi
- Department of Neurosurgery, Duke University Hospital, Durham, North Carolina, USA
| | - Alyssa M Bartlett
- Department of Neurosurgery, Duke University School of Medicine, Durham, North Carolina, USA
| | - Timothy Y Wang
- Department of Neurosurgery, Duke University Hospital, Durham, North Carolina, USA
| | - Mounica R Paturu
- Department of Neurosurgery, Duke University Hospital, Durham, North Carolina, USA
| | - Christopher F Dibble
- Department of Neurosurgery, Duke University Hospital, Durham, North Carolina, USA
| | | | - Wilson Z Ray
- Department of Neurosurgery, Washington University, St. Louis, Missouri, USA
| | - John H Chi
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Walter F Wiggins
- Department of Radiology, Duke University Hospital, Durham, North Carolina, USA
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Jia S, Weng Y, Wang K, Qi H, Yang Y, Ma C, Lu WW, Wu H. Performance evaluation of an AI-based preoperative planning software application for automatic selection of pedicle screws based on computed tomography images. Front Surg 2023; 10:1247527. [PMID: 37753530 PMCID: PMC10518392 DOI: 10.3389/fsurg.2023.1247527] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 08/21/2023] [Indexed: 09/28/2023] Open
Abstract
Introduction Recent neurosurgical applications based on artificial intelligence (AI) have demonstrated its potential in surgical planning and anatomical measurement. We aimed to evaluate the performance of an AI planning software application on screw length/diameter selection and insertion accuracy in comparison with freehand surgery. Methods A total of 45 patients with 208 pedicle screw placements on thoracolumbar segments were included in this analysis. The novel AI planning software was developed based on a deep learning model. AI-based pedicle screw placements were selected on the basis of preoperative computed tomography (CT) data, and freehand surgery screw placements were observed based on postoperative CT data. The performance of AI pedicle screw placements was evaluated on the components of screw length, diameter, and Gertzbein grade in comparison with the results achieved by freehand surgery. Results Among 208 pedicle screw placements, the average screw length/diameters selected by the AI model and used in freehand surgery were 48.65 ± 5.99 mm/7.39 ± 0.42 mm and 44.78 ± 2.99 mm/6.1 ± 0.27 mm, respectively. Among AI screw placements, 85.1% were classified as Gertzbein Grade A (no cortical pedicle breach); among free-hand surgery placements, 64.9% were classified as Gertzbein Grade A. Conclusion The novel AI planning software application could provide an accessible and safe pedicle screw placement strategy in comparison with traditional freehand pedicle screw placement strategies. The choices of pedicle screw dimensional parameters made by the model, including length and diameter, may provide potential inspiration for real clinical discretion.
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Affiliation(s)
- Shanhang Jia
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
- Spine Center, China International Neuroscience Institute (CHINA-INI), Beijing, China
| | - Yuanzhi Weng
- Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Department of Orthopaedics and Traumatology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Pharmaceutical Materials Science and Translational Medicine, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen, China
| | - Kai Wang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
- Spine Center, China International Neuroscience Institute (CHINA-INI), Beijing, China
| | - Huan Qi
- Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Department of Orthopaedics and Traumatology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Yuhua Yang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Chi Ma
- Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Department of Orthopaedics and Traumatology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Weijia William Lu
- Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Department of Orthopaedics and Traumatology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Pharmaceutical Materials Science and Translational Medicine, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen, China
| | - Hao Wu
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
- Spine Center, China International Neuroscience Institute (CHINA-INI), Beijing, China
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Guo N, Tian J, Wang L, Sun K, Mi L, Ming H, Zhe Z, Sun F. Discussion on the possibility of multi-layer intelligent technologies to achieve the best recover of musculoskeletal injuries: Smart materials, variable structures, and intelligent therapeutic planning. Front Bioeng Biotechnol 2022; 10:1016598. [PMID: 36246357 PMCID: PMC9561816 DOI: 10.3389/fbioe.2022.1016598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/14/2022] [Indexed: 11/16/2022] Open
Abstract
Although intelligent technologies has facilitated the development of precise orthopaedic, simple internal fixation, ligament reconstruction or arthroplasty can only relieve pain of patients in short-term. To achieve the best recover of musculoskeletal injuries, three bottlenecks must be broken through, which includes scientific path planning, bioactive implants and personalized surgical channels building. As scientific surgical path can be planned and built by through AI technology, 4D printing technology can make more bioactive implants be manufactured, and variable structures can establish personalized channels precisely, it is possible to achieve satisfied and effective musculoskeletal injury recovery with the progress of multi-layer intelligent technologies (MLIT).
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Affiliation(s)
- Na Guo
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
- Institute of Precision Medicine, Tsinghua University, Beijing, China
| | - Jiawen Tian
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
- Institute of Precision Medicine, Tsinghua University, Beijing, China
| | - Litao Wang
- College of Engineering, China Agricultural University, Beijing, China
| | - Kai Sun
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Lixin Mi
- Musculoskeletal Department, Beijing Rehabilitation Hospital, Beijing, China
| | - Hao Ming
- Orthopaedics, Chinese PLA General Hospital, Beijing, China
| | - Zhao Zhe
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Fuchun Sun
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
- Institute of Precision Medicine, Tsinghua University, Beijing, China
- *Correspondence: Fuchun Sun,
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Schreiter J, Schott D, Schwenderling L, Hansen C, Heinrich F, Joeres F. AR-Supported Supervision of Conditional Autonomous Robots: Considerations for Pedicle Screw Placement in the Future. J Imaging 2022; 8:jimaging8100255. [PMID: 36286350 PMCID: PMC9605344 DOI: 10.3390/jimaging8100255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 12/03/2022] Open
Abstract
Robotic assistance is applied in orthopedic interventions for pedicle screw placement (PSP). While current robots do not act autonomously, they are expected to have higher autonomy under surgeon supervision in the mid-term. Augmented reality (AR) is promising to support this supervision and to enable human–robot interaction (HRI). To outline a futuristic scenario for robotic PSP, the current workflow was analyzed through literature review and expert discussion. Based on this, a hypothetical workflow of the intervention was developed, which additionally contains the analysis of the necessary information exchange between human and robot. A video see-through AR prototype was designed and implemented. A robotic arm with an orthopedic drill mock-up simulated the robotic assistance. The AR prototype included a user interface to enable HRI. The interface provides data to facilitate understanding of the robot’s ”intentions”, e.g., patient-specific CT images, the current workflow phase, or the next planned robot motion. Two-dimensional and three-dimensional visualization illustrated patient-specific medical data and the drilling process. The findings of this work contribute a valuable approach in terms of addressing future clinical needs and highlighting the importance of AR support for HRI.
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Affiliation(s)
- Josefine Schreiter
- Faculty of Computer Science & Research Campus STIMULATE, University of Magdeburg, 39106 Magdeburg, Germany
| | - Danny Schott
- Faculty of Computer Science & Research Campus STIMULATE, University of Magdeburg, 39106 Magdeburg, Germany
| | - Lovis Schwenderling
- Faculty of Computer Science & Research Campus STIMULATE, University of Magdeburg, 39106 Magdeburg, Germany
| | - Christian Hansen
- Faculty of Computer Science & Research Campus STIMULATE, University of Magdeburg, 39106 Magdeburg, Germany
- Correspondence:
| | - Florian Heinrich
- Faculty of Computer Science & Research Campus STIMULATE, University of Magdeburg, 39106 Magdeburg, Germany
| | - Fabian Joeres
- Faculty of Computer Science & Research Campus STIMULATE, University of Magdeburg, 39106 Magdeburg, Germany
- Innovation Center Computer-Assisted Surgery (ICCAS), Faculty of Medicine, Leipzig University, 04103 Leipzig, Germany
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Karandikar P, Massaad E, Hadzipasic M, Kiapour A, Joshi RS, Shankar GM, Shin JH. Machine Learning Applications of Surgical Imaging for the Diagnosis and Treatment of Spine Disorders: Current State of the Art. Neurosurgery 2022; 90:372-382. [PMID: 35107085 DOI: 10.1227/neu.0000000000001853] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 11/10/2021] [Indexed: 01/18/2023] Open
Abstract
Recent developments in machine learning (ML) methods demonstrate unparalleled potential for application in the spine. The ability for ML to provide diagnostic faculty, produce novel insights from existing capabilities, and augment or accelerate elements of surgical planning and decision making at levels equivalent or superior to humans will tremendously benefit spine surgeons and patients alike. In this review, we aim to provide a clinically relevant outline of ML-based technology in the contexts of spinal deformity, degeneration, and trauma, as well as an overview of commercial-level and precommercial-level surgical assist systems and decisional support tools. Furthermore, we briefly discuss potential applications of generative networks before highlighting some of the limitations of ML applications. We conclude that ML in spine imaging represents a significant addition to the neurosurgeon's armamentarium-it has the capacity to directly address and manifest clinical needs and improve diagnostic and procedural quality and safety-but is yet subject to challenges that must be addressed before widespread implementation.
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Affiliation(s)
- Paramesh Karandikar
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- T.H. Chan School of Medicine, University of Massachusetts, Worcester, Massachusetts, USA
| | - Elie Massaad
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Muhamed Hadzipasic
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ali Kiapour
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Rushikesh S Joshi
- Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan, USA
| | - Ganesh M Shankar
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - John H Shin
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
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