Literature DB >> 23797500

Automatic Cobb angle determination from radiographic images.

Tri Arief Sardjono1, Michael H F Wilkinson, Albert G Veldhuizen, Peter M A van Ooijen, Ketut E Purnama, Gijsbertus J Verkerke.   

Abstract

STUDY
DESIGN: Automatic measurement of Cobb angle in patients with scoliosis.
OBJECTIVE: To test the accuracy of an automatic Cobb angle determination method from frontal radiographical images. SUMMARY OF BACKGROUND DATA: Thirty-six frontal radiographical images of patients with scoliosis.
METHODS: A modified charged particle model is used to determine the curvature on radiographical spinal images. Three curve fitting methods, piece-wise linear, splines, and polynomials, each with 3 variants were used and evaluated for the best fit. The Cobb angle was calculated out of these curve fit lines and compared with a manually determined Cobb angle. The best-automated method is determined on the basis of the lowest mean absolute error and standard deviation, and the highest R2.
RESULTS: The error of the manual Cobb angle determination among the 3 observers, determined as the mean of the standard deviations of all sets of measurements, was 3.37°. For the automatic method, the best piece-wise linear method is the 3-segments method. The best spline method is the 10-steps method. The best polynomial method is poly 6. Overall, the best automatic methods are the piece-wise linear method using 3 segments and the polynomial method using poly 6, with a mean absolute error of 4,26° and 3,91° a standard deviation of 3,44° and 3,60°, and a R2 of 0.9124 and 0.9175. The standard measurement error is significantly lower than the upper bound found in the literature (11.8°).
CONCLUSION: The automatic Cobb angle method seemed to be better than the manual methods described in the literature. The piece-wise linear method using 3 segments and the polynomial method using poly 6 yield the 2 best results because the mean absolute error, standard deviation, and R2 are the best of all methods. LEVEL OF EVIDENCE: 3.

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Mesh:

Year:  2013        PMID: 23797500     DOI: 10.1097/BRS.0b013e3182a0c7c3

Source DB:  PubMed          Journal:  Spine (Phila Pa 1976)        ISSN: 0362-2436            Impact factor:   3.468


  10 in total

1.  Reproducibility and repeatability of a new computerized software for sagittal spinopelvic and scoliosis curvature radiologic measurements: Keops(®).

Authors:  C Maillot; E Ferrero; D Fort; C Heyberger; J-C Le Huec
Journal:  Eur Spine J       Date:  2015-02-28       Impact factor: 3.134

2.  Evaluation of a computer-aided method for measuring the Cobb angle on chest X-rays.

Authors:  Yaling Pan; Qiaoran Chen; Tongtong Chen; Hanqi Wang; Xiaolei Zhu; Zhihui Fang; Yong Lu
Journal:  Eur Spine J       Date:  2019-08-24       Impact factor: 3.134

3.  Automated Measurement of Lumbar Lordosis on Radiographs Using Machine Learning and Computer Vision.

Authors:  Brian H Cho; Deepak Kaji; Zoe B Cheung; Ivan B Ye; Ray Tang; Amy Ahn; Oscar Carrillo; John T Schwartz; Aly A Valliani; Eric K Oermann; Varun Arvind; Daniel Ranti; Li Sun; Jun S Kim; Samuel K Cho
Journal:  Global Spine J       Date:  2019-08-13

Review 4.  A Review of the Methods on Cobb Angle Measurements for Spinal Curvature.

Authors:  Chen Jin; Shengru Wang; Guodong Yang; En Li; Zize Liang
Journal:  Sensors (Basel)       Date:  2022-04-24       Impact factor: 3.847

5.  A novel tool to provide predictable alignment data irrespective of source and image quality acquired on mobile phones: what engineers can offer clinicians.

Authors:  Teng Zhang; Chuang Zhu; Qiaoyun Lu; Jun Liu; Ashish Diwan; Jason Pui Yin Cheung
Journal:  Eur Spine J       Date:  2020-01-02       Impact factor: 3.134

6.  Comparison of 3D and 2D characterization of spinal geometry from biplanar X-rays: a large cohort study.

Authors:  Zongshan Hu; Claudio Vergari; Laurent Gajny; Zhen Liu; Tsz-Ping Lam; Zezhang Zhu; Yong Qiu; Gene C W Man; Kwong-Hang Yeung; Winnie C W Chu; Jack C Y Cheng; Wafa Skalli
Journal:  Quant Imaging Med Surg       Date:  2021-07

7.  Use of the smartphone for end vertebra selection in scoliosis.

Authors:  Murad Pepe; Onur Kocadal; Abdullah Iyigun; Zafer Gunes; Ertugrul Aksahin; Cem Nuri Aktekin
Journal:  Acta Orthop Traumatol Turc       Date:  2017-01-08       Impact factor: 1.511

Review 8.  A Survey of Methods and Technologies Used for Diagnosis of Scoliosis.

Authors:  Ilona Karpiel; Adam Ziębiński; Marek Kluszczyński; Daniel Feige
Journal:  Sensors (Basel)       Date:  2021-12-16       Impact factor: 3.576

9.  An Application of Artificial Intelligence to Diagnostic Imaging of Spine Disease: Estimating Spinal Alignment From Moiré Images.

Authors:  Kota Watanabe; Yoshimitsu Aoki; Morio Matsumoto
Journal:  Neurospine       Date:  2019-12-31

10.  The Effect of Aging on Cervical Parameters in a Normative North American Population.

Authors:  Justin Iorio; Virginie Lafage; Renaud Lafage; Jensen K Henry; Dan Stein; Lawrence G Lenke; Munish Gupta; Michael P Kelly; Brenda Sides; Han Jo Kim
Journal:  Global Spine J       Date:  2018-03-27
  10 in total

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