Prediction of Cobb Angle Using Deep Learning Algorithm with Three-Dimensional Depth Sensor Considering the Influence of Garment in Idiopathic Scoliosis
Adolescent idiopathic scoliosis (AIS) is the most common pediatric spinal deformity. Early detection of deformity and timely intervention, such as brace treatment, can help inhibit progressive changes. A three-dimensional (3D) depth-sensor imaging system with a convolutional neural network was previ...
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| Format: | Article |
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MDPI AG
2023-01-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/12/2/499 |
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| author | Yoko Ishikawa Terufumi Kokabu Katsuhisa Yamada Yuichiro Abe Hiroyuki Tachi Hisataka Suzuki Takashi Ohnishi Tsutomu Endo Daisuke Ukeba Katsuro Ura Masahiko Takahata Norimasa Iwasaki Hideki Sudo |
| author_facet | Yoko Ishikawa Terufumi Kokabu Katsuhisa Yamada Yuichiro Abe Hiroyuki Tachi Hisataka Suzuki Takashi Ohnishi Tsutomu Endo Daisuke Ukeba Katsuro Ura Masahiko Takahata Norimasa Iwasaki Hideki Sudo |
| author_sort | Yoko Ishikawa |
| collection | DOAJ |
| description | Adolescent idiopathic scoliosis (AIS) is the most common pediatric spinal deformity. Early detection of deformity and timely intervention, such as brace treatment, can help inhibit progressive changes. A three-dimensional (3D) depth-sensor imaging system with a convolutional neural network was previously developed to predict the Cobb angle. The purpose of the present study was to (1) evaluate the performance of the deep learning algorithm (DLA) in predicting the Cobb angle and (2) assess the predictive ability depending on the presence or absence of clothing in a prospective analysis. We included 100 subjects with suspected AIS. The correlation coefficient between the actual and predicted Cobb angles was 0.87, and the mean absolute error and root mean square error were 4.7° and 6.0°, respectively, for Adam’s forward bending without underwear. There were no significant differences in the correlation coefficients between the groups with and without underwear in the forward-bending posture. The performance of the DLA with a 3D depth sensor was validated using an independent external validation dataset. Because the psychological burden of children and adolescents on naked body imaging is an unignorable problem, scoliosis examination with underwear is a valuable alternative in clinics or schools. |
| first_indexed | 2024-03-09T12:11:40Z |
| format | Article |
| id | doaj.art-4c09c6a343634346af790ee43d5c20f0 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-09T12:11:40Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-4c09c6a343634346af790ee43d5c20f02023-11-30T22:50:47ZengMDPI AGJournal of Clinical Medicine2077-03832023-01-0112249910.3390/jcm12020499Prediction of Cobb Angle Using Deep Learning Algorithm with Three-Dimensional Depth Sensor Considering the Influence of Garment in Idiopathic ScoliosisYoko Ishikawa0Terufumi Kokabu1Katsuhisa Yamada2Yuichiro Abe3Hiroyuki Tachi4Hisataka Suzuki5Takashi Ohnishi6Tsutomu Endo7Daisuke Ukeba8Katsuro Ura9Masahiko Takahata10Norimasa Iwasaki11Hideki Sudo12Department of Orthopaedic Surgery, Hokkaido University Hospital, N15W7, Sapporo 060-8638, Hokkaido, JapanDepartment of Orthopaedic Surgery, Hokkaido University Hospital, N15W7, Sapporo 060-8638, Hokkaido, JapanDepartment of Orthopaedic Surgery, Hokkaido University Hospital, N15W7, Sapporo 060-8638, Hokkaido, JapanDepartment of Orthopaedic Surgery, Eniwa Hospital, 2-1-1 Kogane-chuo, Eniwa 061-1449, Hokkaido, JapanDepartment of Orthopaedic Surgery, Hokkaido University Hospital, N15W7, Sapporo 060-8638, Hokkaido, JapanDepartment of Orthopaedic Surgery, Hokkaido University Hospital, N15W7, Sapporo 060-8638, Hokkaido, JapanDepartment of Orthopaedic Surgery, Hokkaido University Hospital, N15W7, Sapporo 060-8638, Hokkaido, JapanDepartment of Orthopaedic Surgery, Hokkaido University Hospital, N15W7, Sapporo 060-8638, Hokkaido, JapanDepartment of Orthopaedic Surgery, Hokkaido University Hospital, N15W7, Sapporo 060-8638, Hokkaido, JapanDepartment of Orthopaedic Surgery, Hokkaido University Hospital, N15W7, Sapporo 060-8638, Hokkaido, JapanDepartment of Orthopaedic Surgery, Hokkaido University Hospital, N15W7, Sapporo 060-8638, Hokkaido, JapanDepartment of Orthopaedic Surgery, Hokkaido University Hospital, N15W7, Sapporo 060-8638, Hokkaido, JapanDepartment of Orthopaedic Surgery, Hokkaido University Hospital, N15W7, Sapporo 060-8638, Hokkaido, JapanAdolescent idiopathic scoliosis (AIS) is the most common pediatric spinal deformity. Early detection of deformity and timely intervention, such as brace treatment, can help inhibit progressive changes. A three-dimensional (3D) depth-sensor imaging system with a convolutional neural network was previously developed to predict the Cobb angle. The purpose of the present study was to (1) evaluate the performance of the deep learning algorithm (DLA) in predicting the Cobb angle and (2) assess the predictive ability depending on the presence or absence of clothing in a prospective analysis. We included 100 subjects with suspected AIS. The correlation coefficient between the actual and predicted Cobb angles was 0.87, and the mean absolute error and root mean square error were 4.7° and 6.0°, respectively, for Adam’s forward bending without underwear. There were no significant differences in the correlation coefficients between the groups with and without underwear in the forward-bending posture. The performance of the DLA with a 3D depth sensor was validated using an independent external validation dataset. Because the psychological burden of children and adolescents on naked body imaging is an unignorable problem, scoliosis examination with underwear is a valuable alternative in clinics or schools.https://www.mdpi.com/2077-0383/12/2/499adolescent idiopathic scoliosisdeep learning algorithmthree-dimensional depth sensorunderwear |
| spellingShingle | Yoko Ishikawa Terufumi Kokabu Katsuhisa Yamada Yuichiro Abe Hiroyuki Tachi Hisataka Suzuki Takashi Ohnishi Tsutomu Endo Daisuke Ukeba Katsuro Ura Masahiko Takahata Norimasa Iwasaki Hideki Sudo Prediction of Cobb Angle Using Deep Learning Algorithm with Three-Dimensional Depth Sensor Considering the Influence of Garment in Idiopathic Scoliosis Journal of Clinical Medicine adolescent idiopathic scoliosis deep learning algorithm three-dimensional depth sensor underwear |
| title | Prediction of Cobb Angle Using Deep Learning Algorithm with Three-Dimensional Depth Sensor Considering the Influence of Garment in Idiopathic Scoliosis |
| title_full | Prediction of Cobb Angle Using Deep Learning Algorithm with Three-Dimensional Depth Sensor Considering the Influence of Garment in Idiopathic Scoliosis |
| title_fullStr | Prediction of Cobb Angle Using Deep Learning Algorithm with Three-Dimensional Depth Sensor Considering the Influence of Garment in Idiopathic Scoliosis |
| title_full_unstemmed | Prediction of Cobb Angle Using Deep Learning Algorithm with Three-Dimensional Depth Sensor Considering the Influence of Garment in Idiopathic Scoliosis |
| title_short | Prediction of Cobb Angle Using Deep Learning Algorithm with Three-Dimensional Depth Sensor Considering the Influence of Garment in Idiopathic Scoliosis |
| title_sort | prediction of cobb angle using deep learning algorithm with three dimensional depth sensor considering the influence of garment in idiopathic scoliosis |
| topic | adolescent idiopathic scoliosis deep learning algorithm three-dimensional depth sensor underwear |
| url | https://www.mdpi.com/2077-0383/12/2/499 |
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