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Deep learning–based identification of spine growth potential on EOS radiographs.

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Title: Deep learning–based identification of spine growth potential on EOS radiographs.
Authors: Xie, Lin-Zhen1,2,3 (AUTHOR), Dou, Xin-Yu1,4 (AUTHOR), Ge, Teng-Hui1,2,3 (AUTHOR), Han, Xiao-Guang1,2,3 (AUTHOR), Zhang, Qi1,2,3 (AUTHOR), Wang, Qi-Long1,2,3 (AUTHOR), Chen, Shuo1 (AUTHOR), He, Da1,2,3 (AUTHOR) heda_spine@163.com, Tian, Wei1,2,3 (AUTHOR) tianwei_spine@sina.com
Source: European Radiology. May2024, Vol. 34 Issue 5, p2849-2860. 12p.
Subjects: Spine, Radiographs, Judgment (Psychology), Medical protocols
Abstract: Objectives: To develop an automatic computer-based method that can help clinicians in assessing spine growth potential based on EOS radiographs. Methods: We developed a deep learning–based (DL) algorithm that can mimic the human judgment process to automatically determine spine growth potential and the Risser sign based on full-length spine EOS radiographs. A total of 3383 EOS cases were collected and used for the training and test of the algorithm. Subsequently, the completed DL algorithm underwent clinical validation on an additional 440 cases and was compared to the evaluations of four clinicians. Results: Regarding the Risser sign, the weighted kappa value of our DL algorithm was 0.933, while that of the four clinicians ranged from 0.909 to 0.930. In the assessment of spine growth potential, the kappa value of our DL algorithm was 0.944, while the kappa values of the four clinicians were 0.916, 0.934, 0.911, and 0.920, respectively. Furthermore, our DL algorithm obtained a slightly higher accuracy (0.973) and Youden index (0.952) compared to the best values achieved by the four clinicians. In addition, the speed of our DL algorithm was 15.2 ± 0.3 s/40 cases, much faster than the inference speeds of the clinicians, ranging from 177.2 ± 28.0 s/40 cases to 241.2 ± 64.1 s/40 cases. Conclusions: Our algorithm demonstrated comparable or even better performance compared to clinicians in assessing spine growth potential. This stable, efficient, and convenient algorithm seems to be a promising approach to assist doctors in clinical practice and deserves further study. Clinical relevance statement: This method has the ability to quickly ascertain the spine growth potential based on EOS radiographs, and it holds promise to provide assistance to busy doctors in certain clinical scenarios. Key Points: • In the clinic, there is no available computer-based method that can automatically assess spine growth potential. • We developed a deep learning–based method that could automatically ascertain spine growth potential. • Compared with the results of the clinicians, our algorithm got comparable results. [ABSTRACT FROM AUTHOR]
Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Abstract
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  Data: Objectives: To develop an automatic computer-based method that can help clinicians in assessing spine growth potential based on EOS radiographs. Methods: We developed a deep learning–based (DL) algorithm that can mimic the human judgment process to automatically determine spine growth potential and the Risser sign based on full-length spine EOS radiographs. A total of 3383 EOS cases were collected and used for the training and test of the algorithm. Subsequently, the completed DL algorithm underwent clinical validation on an additional 440 cases and was compared to the evaluations of four clinicians. Results: Regarding the Risser sign, the weighted kappa value of our DL algorithm was 0.933, while that of the four clinicians ranged from 0.909 to 0.930. In the assessment of spine growth potential, the kappa value of our DL algorithm was 0.944, while the kappa values of the four clinicians were 0.916, 0.934, 0.911, and 0.920, respectively. Furthermore, our DL algorithm obtained a slightly higher accuracy (0.973) and Youden index (0.952) compared to the best values achieved by the four clinicians. In addition, the speed of our DL algorithm was 15.2 ± 0.3 s/40 cases, much faster than the inference speeds of the clinicians, ranging from 177.2 ± 28.0 s/40 cases to 241.2 ± 64.1 s/40 cases. Conclusions: Our algorithm demonstrated comparable or even better performance compared to clinicians in assessing spine growth potential. This stable, efficient, and convenient algorithm seems to be a promising approach to assist doctors in clinical practice and deserves further study. Clinical relevance statement: This method has the ability to quickly ascertain the spine growth potential based on EOS radiographs, and it holds promise to provide assistance to busy doctors in certain clinical scenarios. Key Points: • In the clinic, there is no available computer-based method that can automatically assess spine growth potential. • We developed a deep learning–based method that could automatically ascertain spine growth potential. • Compared with the results of the clinicians, our algorithm got comparable results. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: May2024
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