Conquering the Cobb Angle: A Deep Learning Algorithm for Automated, Hardware-Invariant Measurement of Cobb Angle on Radiographs in Patients with Scoliosis.

Autor:	Suri A; From the Department of Radiology and Orthopedic Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa., Tang S; From the Department of Radiology and Orthopedic Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa., Kargilis D; From the Department of Radiology and Orthopedic Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa., Taratuta E; From the Department of Radiology and Orthopedic Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa., Kneeland BJ; From the Department of Radiology and Orthopedic Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa., Choi G; From the Department of Radiology and Orthopedic Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa., Agarwal A; From the Department of Radiology and Orthopedic Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa., Anabaraonye N; From the Department of Radiology and Orthopedic Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa., Xu W; From the Department of Radiology and Orthopedic Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa., Parente JB; From the Department of Radiology and Orthopedic Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa., Terry A; From the Department of Radiology and Orthopedic Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa., Kalluri A; From the Department of Radiology and Orthopedic Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa., Song K; From the Department of Radiology and Orthopedic Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa., Rajapakse CS; From the Department of Radiology and Orthopedic Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa.
Jazyk:	angličtina
Zdroj:	Radiology. Artificial intelligence [Radiol Artif Intell] 2023 Jun 21; Vol. 5 (4), pp. e220158. Date of Electronic Publication: 2023 Jun 21 (Print Publication: 2023).
DOI:	10.1148/ryai.220158
Abstrakt:	Scoliosis is a disease estimated to affect more than 8% of adults in the United States. It is diagnosed with use of radiography by means of manual measurement of the angle between maximally tilted vertebrae on a radiograph (ie, the Cobb angle). However, these measurements are time-consuming, limiting their use in scoliosis surgical planning and postoperative monitoring. In this retrospective study, a pipeline (using the SpineTK architecture) was developed that was trained, validated, and tested on 1310 anterior-posterior images obtained with a low-dose stereoradiographic scanning system and radiographs obtained in patients with suspected scoliosis to automatically measure Cobb angles. The images were obtained at six centers (2005-2020). The algorithm measured Cobb angles on hold-out internal ( n = 460) and external ( n = 161) test sets with less than 2° error (intraclass correlation coefficient, 0.96) compared with ground truth measurements by two experienced radiologists. Measurements, produced in less than 0.5 second, did not differ significantly ( P = .05 cutoff) from ground truth measurements, regardless of the presence or absence of surgical hardware ( P = .80), age ( P = .58), sex ( P = .83), body mass index ( P = .63), scoliosis severity ( P = .44), or image type (low-dose stereoradiographic image vs radiograph; P = .51) in the patient. These findings suggest that the algorithm is highly robust across different clinical characteristics. Given its automated, rapid, and accurate measurements, this network may be used for monitoring scoliosis progression in patients. Keywords: Cobb Angle, Convolutional Neural Network, Deep Learning Algorithms, Pediatrics, Machine Learning Algorithms, Scoliosis, Spine Supplemental material is available for this article . © RSNA, 2023. Competing Interests: Disclosures of conflicts of interest: A.S. No relevant relationships. S.T. No relevant relationships. D.K. No relevant relationships. E.T. No relevant relationships. B.J.K. No relevant relationships. G.C. No relevant relationships. A.A. No relevant relationships. N.A. No relevant relationships. W.X. No relevant relationships. J.B.P. No relevant relationships. A.T. No relevant relationships. A.K. No relevant relationships. K.S. No relevant relationships. C.S.R. No relevant relationships. (© 2023 by the Radiological Society of North America, Inc.)
Databáze:	MEDLINE
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