Adult cervical spine deformity: a state-of-the-art review

Jackson-Fowl, Brendan; Hockley, Aaron; Naessig, Sara; Ahmad, Waleed; Pierce, Katherine; Smith, Justin S.; Ames, Christopher; Shaffrey, Christopher; Bennett-Caso, Claudia; Williamson, Tyler K.; McFarland, Kimberly; Passias, Peter G.

doi:10.1007/s43390-023-00735-5

Cited by 2 publications

(1 citation statement)

References 158 publications

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“…Importantly, in the past two decades, cervical sagittal alignment has gained more attention as an important clinical outcome in healthcare. It has been demonstrated that abnormal cervical sagittal alignment significantly influences human health and well-being, as it has been shown to be associated with pain [22], disability [23], overall functional performance [24], and quality of life [25]. Despite modern advances in technology related to imaging leading to improved diagnosis and treatment, billions of humans continue to suffer from daily spine and musculoskeletal pain and disability [11,[26][27][28][29].…”

Section: Discussionmentioning

confidence: 99%

Intra-Examiner Reliability and Validity of Sagittal Cervical Spine Mensuration Methods Using Deep Convolutional Neural Networks

Hosseini,

Mahoor,

Haas

et al. 2024

JCM

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Background: The biomechanical analysis of spine and postural misalignments is important for surgical and non-surgical treatment of spinal pain. We investigated the examiner reliability of sagittal cervical alignment variables compared to the reliability and concurrent validity of computer vision algorithms used in the PostureRay® software 2024. Methods: A retrospective database of 254 lateral cervical radiographs of patients between the ages of 11 and 86 is studied. The radiographs include clearly visualized C1–C7 vertebrae that were evaluated by a human using the software. To evaluate examiner reliability and the concurrent validity of the trained CNN performance, two blinded trials of radiographic digitization were performed by an extensively trained expert user (US) clinician with a two-week interval between trials. Then, the same clinician used the trained CNN twice to reproduce the same measures within a 2-week interval on the same 254 radiographs. Measured variables included segmental angles as relative rotation angles (RRA) C1–C7, Cobb angles C2–C7, relative segmental translations (RT) C1–C7, anterior translation C2–C7, and absolute rotation angle (ARA) C2–C7. Data were remotely extracted from the examiner’s PostureRay® system for data collection and sorted based on gender and stratification of degenerative changes. Reliability was assessed via intra-class correlations (ICC), root mean squared error (RMSE), and R2 values. Results: In comparing repeated measures of the CNN network to itself, perfect reliability was found for the ICC (1.0), RMSE (0), and R2 (1). The reliability of the trained expert US was in the excellent range for all variables, where 12/18 variables had ICCs ≥ 0.9 and 6/18 variables were 0.84 ≤ ICCs ≤ 0.89. Similarly, for the expert US, all R2 values were in the excellent range (R2 ≥ 0.7), and all RMSEs were small, being 0.42 ≤ RMSEs ≤ 3.27. Construct validity between the expert US and the CNN network was found to be in the excellent range with 18/18 ICCs in the excellent range (ICCs ≥ 0.8), 16/18 R2 values in the strong to excellent range (R2 ≥ 0.7), and 2/18 in the good to moderate range (R2 RT C6/C7 = 0.57 and R2 Cobb C6/C7 = 0.64. The RMSEs for expert US vs. the CNN network were small, being 0.37 ≤ RMSEs ≤ 2.89. Conclusions: A comparison of repeated measures within the computer vision CNN network and expert human found exceptional reliability and excellent construct validity when comparing the computer vision to the human observer.

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Section: Discussionmentioning

confidence: 99%

Intra-Examiner Reliability and Validity of Sagittal Cervical Spine Mensuration Methods Using Deep Convolutional Neural Networks

Hosseini,

Mahoor,

Haas

et al. 2024

JCM

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Utilizing machine learning to predict post-treatment outcomes in chronic non-specific neck pain patients undergoing cervical extension traction

Moustafa,

Ozsahin,

Mustapha

et al. 2024

Sci Rep

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This study explored the application of machine learning in predicting post-treatment outcomes for chronic neck pain patients undergoing a multimodal program featuring cervical extension traction (CET). Pre-treatment demographic and clinical variables were used to develop predictive models capable of anticipating modifications in cervical lordotic angle (CLA), pain and disability of 570 patients treated between 2014 and 2020. Linear regression models used pre-treatment variables of age, body mass index, CLA, anterior head translation, disability index, pain score, treatment frequency, duration and compliance. These models used the sci-kit-learn machine learning library within Python for implementing linear regression algorithms. The linear regression models demonstrated high precision and accuracy, and effectively explained 30–55% of the variability in post-treatment outcomes, the highest for the CLA. This pioneering study integrates machine learning into spinal rehabilitation. The developed models offer valuable information to customize interventions, set realistic expectations, and optimize treatment strategies based on individual patient characteristics as treated conservatively with rehabilitation programs using CET as part of multimodal care.

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Adult cervical spine deformity: a state-of-the-art review

Cited by 2 publications

References 158 publications

Intra-Examiner Reliability and Validity of Sagittal Cervical Spine Mensuration Methods Using Deep Convolutional Neural Networks

Intra-Examiner Reliability and Validity of Sagittal Cervical Spine Mensuration Methods Using Deep Convolutional Neural Networks

Utilizing machine learning to predict post-treatment outcomes in chronic non-specific neck pain patients undergoing cervical extension traction

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