Decentralized convolutional neural network for evaluating spinal deformity with spinopelvic parameters. (December 2020)

• Record Type: Journal Article
• Authors: Chae, Dong-sik; Nguyen, Thong Phi; Park, Sung-Jun; Kang, Kyung-Yil; Won, Chanhee; Yoon, Jonghun
• Is Part Of: Computer methods and programs in biomedicine. Volume 197(2020)
• Publication Date: 2020-12
• Subjects: Radiology -- Spinopelvic -- Artificial intelligent -- Orthopaedic -- Convolutional neural network; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105699
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Proposing an intelligent method for measuring spinopelvic parameters, including LLA, LSA, PTA, PIA and SSA, which are specific to spinal deformity status. Utilizing a decentralized convolutional neural network to increase the accuracy of measurement, which was verified by comparison with pure CNN-based approach. Performance of the proposed method is validated with standard references consisting of manually measured values by experienced doctors. Abstract: Low back pain which is caused by the abnormal spinal alignment is one of the most common musculoskeletal symptom and, consequently, is the reason for not only reduction of productivity but also personal suffering. In clinical diagnosis for this disease, estimating adult spinal deformity is required as an indispensable procedure in highlighting abnormal values to output timely warnings and providing precise geometry dimensions for therapeutic therapies. This paper presents an automated method for precisely measuring spinopelvic parameters using a decentralized convolutional neural network as an efficient replacement for current manual process which not only requires experienced surgeons but also shows limitation in ability to process large numbers of images to accommodate the explosion of big data technologies. The proposed method is based on gradually narrowing the regions of interest (ROIs) for feature extraction and leads the model to mainly focus on the necessary geometry characteristics represented as keypoints. According to keypoints obtained, parameters representing the spinal deformity are calculated, which consistency with manual measurement was validated by 40 test cases and, potentially, provided 1.45 o mean absolute values of deviation for PTA as the minimum and 3.51 o in case of LSA as maximum.