An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection

科布 柯布角 脊柱侧凸 医学 均方误差 相关系数 算法 皮尔逊积矩相关系数 人工智能 射线照相术 随机森林 平均绝对百分比误差 统计 数学 口腔正畸科 外科 计算机科学 遗传学 生物
作者
Terufumi Kokabu,Satoshi Kanai,Noriaki Kawakami,Koki Uno,Toshiaki Kotani,Teppei Suzuki,Hiroyuki Tachi,Yoichi M. Ito,Norimasa Iwasaki,Hideki Sudo
出处
期刊:The Spine Journal [Elsevier]
卷期号:21 (6): 980-987 被引量:30
标识
DOI:10.1016/j.spinee.2021.01.022
摘要

BACKGROUND CONTEXTTimely intervention in growing individuals, such as brace treatment, relies on early detection of adolescent idiopathic scoliosis (AIS). To this end, several screening methods have been implemented. However, these methods have limitations in predicting the Cobb angle.PURPOSEThis study aimed to evaluate the performance of a three-dimensional depth sensor imaging system with a deep learning algorithm, in predicting the Cobb angle in AIS.STUDY DESIGNRetrospective analysis of prospectively collected, consecutive, nonrandomized series of patients at five scoliosis centers in Japan.PATIENT SAMPLEOne hundred and-sixty human subjects suspected to have AIS were included.OUTCOME MEASURESPatient demographics, radiographic measurements, and predicted Cobb angle derived from the deep learning algorithm were the outcome measures for this study.METHODSOne hundred and sixty data files were shuffled into five datasets with 32 data files at random (dataset 1, 2, 3, 4, and 5) and five-fold cross validation was performed. The relationships between the actual and predicted Cobb angles were calculated using Pearson's correlation coefficient analyses. The prediction performances of the network models were evaluated using mean absolute error and root mean square error between the actual and predicted Cobb angles. The shuffling into five datasets and five-fold cross validation was conducted ten times. There were no study-specific biases related to conflicts of interest.RESULTSThe correlation between the actual and the mean predicted Cobb angles was 0.91. The mean absolute error and root mean square error were 4.0° and 5.4°, respectively. The accuracy of the mean predicted Cobb angle was 94% for identifying a Cobb angle of ≥10° and 89% for that of ≥20°.CONCLUSIONSThe three-dimensional depth sensor imaging system with its newly innovated convolutional neural network for regression is objective and has significant ability to predict the Cobb angle in children and adolescents. This system is expected to be used for screening scoliosis in clinics or physical examination at schools.
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