{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:02:51Z","timestamp":1787025771308,"version":"build-2736575974"},"reference-count":36,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVE<\/jats:title>\n                    <jats:p>Currently, CT is considered the gold standard for the diagnosis of ossification of the posterior longitudinal ligament (OPLL). The objective of this study was to develop artificial intelligence (AI) software and a validated model for the identification and representation of cervical OPLL (C-OPLL) on MRI, obviating the need for spine CT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>A retrospective evaluation was performed of consecutive imaging studies of all adult patients who underwent both cervical CT and MRI for any clinical indication within a span of 36 months (between January 2017 and July 2020) in a single tertiary-care referral hospital. C-OPLL was identified by a panel of neurosurgeons and a neuroradiologist. MATLAB software was then used to create an AI tool for the diagnosis of C-OPLL by using a convolutional neural network method to identify features on MR images. A reader study was performed to compare the performance of the AI model to that of the diagnostic panel using standard test performance metrics. Interobserver variability was assessed using Cohen\u2019s kappa score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>Nine hundred consecutive patients were found to be eligible for radiological evaluation, yielding 65 identified C-OPLL carriers. The AI model, utilizing MR images, was able to accurately segment the vertebral bodies, PLL, and discoligamentous complex, and detect C-OPLL carriers. The AI model identified 5 additional C-OPLL patients who were not initially detected. The performance of the MRI-based AI model resulted in a sensitivity of 85%, specificity of 98%, negative predictive value of 98%, and positive predictive value of 85%. The overall accuracy of the model was 98%, with a kappa score of 0.917.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS<\/jats:title>\n                    <jats:p>The novel AI software developed in this study was highly specific for identifying C-OPLL on MRI, without the use of CT. This model may obviate the need for CT scans while maintaining adequate diagnostic accuracy. With further development, this MRI-based AI model has the potential to aid in the diagnosis of various spinal disorders and its automated layers may lay the foundation for MRI-specific diagnostic criteria for C-OPLL.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3171\/2023.3.focus2390","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T10:33:00Z","timestamp":1685615580000},"page":"E11","source":"Crossref","is-referenced-by-count":14,"title":["MRI-based detection of cervical ossification of the posterior longitudinal ligament using a novel automated machine learning diagnostic tool"],"prefix":"10.3171","volume":"54","author":[{"given":"Shachar","family":"Shemesh","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Sheba Medical Center, Ramat-Gan, affiliated with Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv;"},{"name":"Arrow Program for Medical Research Education, Sheba Medical Center, Ramat-Gan; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gil","family":"Kimchi","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Sheba Medical Center, Ramat-Gan, affiliated with Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gal","family":"Yaniv","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran","family":"Harel","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Sheba Medical Center, Ramat-Gan, affiliated with Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv;"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"issue":"4","key":"b1","series-title":"J Neurosurg Spine","first-page":"417","article-title":"A computer vision approach to identifying the manufacturer of posterior thoracolumbar instrumentation systems","volume":"38","author":"Anand A","year":"2022","unstructured":"Anand A, Flores AR, McDonald MF, Gadot R, Xu DS, Ropper AE. A computer vision approach to identifying the manufacturer of posterior thoracolumbar instrumentation systems. J Neurosurg Spine. 2022;38(4):417\u2013424.36681945"},{"issue":"2","key":"b2","series-title":"Turk Neurosurg","first-page":"167","article-title":"A systematic review on machine learning in neurosurgery: the future of decision-making in patient care","volume":"28","author":"Celtikci E","year":"2018","unstructured":"Celtikci E. A systematic review on machine learning in neurosurgery: the future of decision-making in patient care. Turk Neurosurg. 2018;28(2):167\u2013173.28481395"},{"key":"b3","series-title":"OPLL","first-page":"111","article-title":"Diagnosis of OPLL and OYL: Overview","author":"Tanaka M","year":"2006","unstructured":"Tanaka M, Kanazawa A, Yonenobu K. Diagnosis of OPLL and OYL: Overview. In: Yonenobu K, Nakamura K, Toyama Y, eds. OPLL. Springer; 2006:111\u2013113."},{"issue":"4","key":"b4","series-title":"Int Orthop","first-page":"797","article-title":"Ossification of the posterior longitudinal ligament in the cervical spine: a review","volume":"43","author":"Boody BS","year":"2019","unstructured":"Boody BS, Lendner M, Vaccaro AR. Ossification of the posterior longitudinal ligament in the cervical spine: a review. Int Orthop. 2019;43(4):797\u2013805.30116867"},{"issue":"1","key":"b5","series-title":"BMC Musculoskelet Disord","first-page":"823","article-title":"Surgical decision-making for ossification of the posterior longitudinal ligament versus other types of degenerative cervical myelopathy: anterior versus posterior approaches","volume":"21","author":"Kwok SSS","year":"2020","unstructured":"Kwok SSS, Cheung JPY. Surgical decision-making for ossification of the posterior longitudinal ligament versus other types of degenerative cervical myelopathy: anterior versus posterior approaches. BMC Musculoskelet Disord. 2020;21(1):823.33292175"},{"issue":"4","key":"b6","series-title":"Biomol Ther (Seoul)","first-page":"342","article-title":"Molecular pathophysiology of ossification of the posterior longitudinal ligament (OPLL)","volume":"27","author":"Nam DC","year":"2019","unstructured":"Nam DC, Lee HJ, Lee CJ, Hwang SC. Molecular pathophysiology of ossification of the posterior longitudinal ligament (OPLL). Biomol Ther (Seoul). 2019;27(4):342\u2013348.31042677"},{"issue":"26","key":"b7","series-title":"Spine (Phila Pa 1976)","first-page":"E990","article-title":"A new concept for making decisions regarding the surgical approach for cervical ossification of the posterior longitudinal ligament: the K-line","volume":"33","author":"Fujiyoshi T","year":"2008","unstructured":"Fujiyoshi T, Yamazaki M, Kawabe J,  A new concept for making decisions regarding the surgical approach for cervical ossification of the posterior longitudinal ligament: the K-line. Spine (Phila Pa 1976). 2008;33(26):E990\u2013E993.19092610"},{"key":"b8","series-title":"OPLL","author":"Yonenobu K","year":"2006","unstructured":"Yonenobu K, Nakamura K, Toyama Y, eds. OPLL. Springer; 2006."},{"issue":"3","key":"b9","series-title":"Neurosurg Focus","first-page":"E1","article-title":"Ossification of the posterior longitudinal ligament: a review","volume":"30","author":"Saetia K","year":"2011","unstructured":"Saetia K, Cho D, Lee S, Kim DH, Kim SD. Ossification of the posterior longitudinal ligament: a review. Neurosurg Focus. 2011;30(3):E1.21434817"},{"issue":"4","key":"b10","series-title":"J Comput Tomogr","first-page":"317","article-title":"Computed tomography of calcification and ossification of posterior longitudinal ligament of the spine","volume":"8","author":"Firooznia H","year":"1984","unstructured":"Firooznia H, Rafii M, Golimbu C, Tyler I, Benjamin VM, Pinto RS. 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Symmetry. 2018;10(10):519."},{"issue":"1","key":"b15","series-title":"Neurospine","first-page":"33","article-title":"Ossification of the posterior longitudinal ligament in cervical spine: prevalence, management, and prognosis","volume":"15","author":"Wu JC","year":"2018","unstructured":"Wu JC, Chen YC, Huang WC. Ossification of the posterior longitudinal ligament in cervical spine: prevalence, management, and prognosis. Neurospine. 2018;15(1):33\u201341.29656627"},{"issue":"3","key":"b16","series-title":"Neurosurg Focus","first-page":"E5","article-title":"Ossification of the posterior longitudinal ligament in the cervical spine: an 11-year comprehensive national epidemiology study","volume":"30","author":"Wu JC","year":"2011","unstructured":"Wu JC, Liu L, Chen YC, Huang WC, Chen TJ, Cheng H. Ossification of the posterior longitudinal ligament in the cervical spine: an 11-year comprehensive national epidemiology study. 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Sensors (Basel). 2022;22(20):8076.36298426"},{"issue":"4","key":"b1","series-title":"J Neurosurg Spine","first-page":"417","article-title":"A computer vision approach to identifying the manufacturer of posterior thoracolumbar instrumentation systems","volume":"38","author":"Anand A","year":"2022","unstructured":"Anand A, Flores AR, McDonald MF, Gadot R, Xu DS, Ropper AE. A computer vision approach to identifying the manufacturer of posterior thoracolumbar instrumentation systems. J Neurosurg Spine. 2022;38(4):417\u2013424.36681945"},{"issue":"2","key":"b2","series-title":"Turk Neurosurg","first-page":"167","article-title":"A systematic review on machine learning in neurosurgery: the future of decision-making in patient care","volume":"28","author":"Celtikci E","year":"2018","unstructured":"Celtikci E. A systematic review on machine learning in neurosurgery: the future of decision-making in patient care. 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Molecular pathophysiology of ossification of the posterior longitudinal ligament (OPLL). Biomol Ther (Seoul). 2019;27(4):342\u2013348.31042677"},{"issue":"26","key":"b7","series-title":"Spine (Phila Pa 1976)","first-page":"E990","article-title":"A new concept for making decisions regarding the surgical approach for cervical ossification of the posterior longitudinal ligament: the K-line","volume":"33","author":"Fujiyoshi T","year":"2008","unstructured":"Fujiyoshi T, Yamazaki M, Kawabe J,  A new concept for making decisions regarding the surgical approach for cervical ossification of the posterior longitudinal ligament: the K-line. Spine (Phila Pa 1976). 2008;33(26):E990\u2013E993.19092610"},{"key":"b8","series-title":"OPLL","author":"Yonenobu K","year":"2006","unstructured":"Yonenobu K, Nakamura K, Toyama Y, eds. OPLL. Springer; 2006."},{"issue":"3","key":"b9","series-title":"Neurosurg Focus","first-page":"E1","article-title":"Ossification of the posterior longitudinal ligament: a review","volume":"30","author":"Saetia K","year":"2011","unstructured":"Saetia K, Cho D, Lee S, Kim DH, Kim SD. Ossification of the posterior longitudinal ligament: a review. Neurosurg Focus. 2011;30(3):E1.21434817"},{"issue":"4","key":"b10","series-title":"J Comput Tomogr","first-page":"317","article-title":"Computed tomography of calcification and ossification of posterior longitudinal ligament of the spine","volume":"8","author":"Firooznia H","year":"1984","unstructured":"Firooznia H, Rafii M, Golimbu C, Tyler I, Benjamin VM, Pinto RS. Computed tomography of calcification and ossification of posterior longitudinal ligament of the spine. J Comput Tomogr. 1984;8(4):317\u2013324.6437740"},{"key":"b11","series-title":"OPLL","first-page":"127","article-title":"Diagnostic imaging of cervical ossification of the posterior longitudinal ligament","author":"Nagata K","year":"2006","unstructured":"Nagata K, Sato K. Diagnostic imaging of cervical ossification of the posterior longitudinal ligament. In: Yonenobu K, Nakamura K, Toyama Y, eds. OPLL. Springer; 2006:127\u2013143."},{"issue":"3","key":"b12","series-title":"Korean J Spine","first-page":"205","article-title":"Diagnosis of cervical OPLL in lateral radiograph and MRI: is it reliable?","volume":"9","author":"Kang MS","year":"2012","unstructured":"Kang MS, Lee JW, Zhang HY, Cho YE, Park YM. Diagnosis of cervical OPLL in lateral radiograph and MRI: is it reliable? Korean J Spine. 2012;9(3):205\u2013208.25983816"},{"issue":"1","key":"b13","series-title":"Sci Rep","first-page":"7361","article-title":"The effect of threshold level on bone segmentation of cranial base structures from CT and CBCT images","volume":"10","author":"Friedli L","year":"2020","unstructured":"Friedli L, Kloukos D, Kanavakis G, Halazonetis D, Gkantidis N. The effect of threshold level on bone segmentation of cranial base structures from CT and CBCT images. Sci Rep. 2020;10(1):7361."},{"issue":"10","key":"b14","series-title":"Symmetry","first-page":"519","article-title":"Synthetic medical images using F&BGAN for improved lung nodules classification by multi-scale VGG16","volume":"10","author":"Zhao D","year":"2018","unstructured":"Zhao D, Zhu D, Lu J, Luo Y, Zhang G. Synthetic medical images using F&BGAN for improved lung nodules classification by multi-scale VGG16. Symmetry. 2018;10(10):519."},{"issue":"1","key":"b15","series-title":"Neurospine","first-page":"33","article-title":"Ossification of the posterior longitudinal ligament in cervical spine: prevalence, management, and prognosis","volume":"15","author":"Wu JC","year":"2018","unstructured":"Wu JC, Chen YC, Huang WC. Ossification of the posterior longitudinal ligament in cervical spine: prevalence, management, and prognosis. Neurospine. 2018;15(1):33\u201341.29656627"},{"issue":"3","key":"b16","series-title":"Neurosurg Focus","first-page":"E5","article-title":"Ossification of the posterior longitudinal ligament in the cervical spine: an 11-year comprehensive national epidemiology study","volume":"30","author":"Wu JC","year":"2011","unstructured":"Wu JC, Liu L, Chen YC, Huang WC, Chen TJ, Cheng H. Ossification of the posterior longitudinal ligament in the cervical spine: an 11-year comprehensive national epidemiology study. Neurosurg Focus. 2011;30(3):E5.21434821"},{"issue":"23","key":"b17","series-title":"Appl Sci","first-page":"11185","article-title":"An improved VGG16 model for pneumonia image classification","volume":"11","author":"Jiang ZP","year":"2021","unstructured":"Jiang ZP, Liu YY, Shao ZE, Huang KW. An improved VGG16 model for pneumonia image classification. Appl Sci. 2021;11(23):11185."},{"issue":"20","key":"b18","series-title":"Sensors (Basel)","first-page":"8076","article-title":"Construction of VGG16 Convolution Neural Network (VGG16_CNN) classifier with NestNet-based segmentation paradigm for brain metastasis classification","volume":"22","author":"Alshammari A","year":"2022","unstructured":"Alshammari A. Construction of VGG16 Convolution Neural Network (VGG16_CNN) classifier with NestNet-based segmentation paradigm for brain metastasis classification. 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