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Shimizu H, Enda K, Koyano H, Shimizu T, Shimodan S, Sato K, Ogawa T, Tanaka S, Iwasaki N, Takahashi D. Bimodal machine learning model for unstable hips in infants: integration of radiographic images with automatically-generated clinical measurements. Sci Rep 2024; 14:17826. [PMID: 39090235 PMCID: PMC11294347 DOI: 10.1038/s41598-024-68484-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Accepted: 07/24/2024] [Indexed: 08/04/2024] Open
Abstract
Bimodal convolutional neural networks (CNNs) are frequently combined with patient information or several medical images to enhance the diagnostic performance. However, the technologies that integrate automatically generated clinical measurements within the images are scarce. Hence, we developed a bimodal model that produced automatic algorithm for clinical measurement (aaCM) from radiographic images and integrated the model with CNNs. In this multicenter research project, the diagnostic performance of the model was investigated with 813 radiographic hip images of infants at risk of developmental dysplasia of the hips (232 and 581 images of unstable and stable hips, respectively), with the ground truth defined by provocative examinations. The results indicated that the accuracy of aaCM was equal or higher than that of specialists, and the bimodal model showed better diagnostic performance than LightGBM, XGBoost, SVM, and single CNN models. aaCM can provide expert's knowledge in a high level, and our proposed bimodal model has better performance than the state-of-art models.
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Affiliation(s)
- Hirokazu Shimizu
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
- Department of Cancer Pathology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Ken Enda
- Department of Cancer Pathology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Hidenori Koyano
- Graduate School of Biomedical Science and Engineering, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Tomohiro Shimizu
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Shun Shimodan
- Department of Orthopaedic Surgery, Kushiro City General Hospital, Kushiro, Hokkaido, Japan
| | - Komei Sato
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Takuya Ogawa
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Shinya Tanaka
- Department of Cancer Pathology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido, Japan
| | - Norimasa Iwasaki
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Daisuke Takahashi
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan.
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Ewertowski NP, Schleich C, Abrar DB, Hosalkar HS, Bittersohl B. Automated measurement of alpha angle on 3D-magnetic resonance imaging in femoroacetabular impingement hips: a pilot study. J Orthop Surg Res 2022; 17:370. [PMID: 35907886 PMCID: PMC9338591 DOI: 10.1186/s13018-022-03256-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 07/16/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Femoroacetabular impingement (FAI) syndrome is an established pre-osteoarthritic condition. Diagnosis is based on both clinical and radiographic parameters. An abnormal manually calculated alpha angle in magnetic resonance imaging (MRI) is traditionally utilized to diagnose abnormal femoral head-neck offset. This pilot study aimed to assess the feasibility of automated alpha angle measurements in patients with FAI syndrome, and to compare automated with manual measurements data with regard to the time and effort needed in each method. METHODS Alpha angles were measured with manual and automated techniques, using postprocessing software in nineteen hip MRIs of FAI syndrome patients. Two observers conducted manual measurements. Intra- and inter-observer reproducibility and correlation of manual and automated alpha angle measurements were calculated using intra-class correlation (ICC) analysis. Both techniques were compared regarding the time taken (in minutes) and effort required, measured as the amount of mouse button presses performed. RESULTS The first observer's intra-observer reproducibility was good (ICC 0.77; p < 0.001) while the second observer's was good-to-excellent (ICC 0.93; p < 0.001). Inter-observer reproducibility between both observers in the first (ICC 0.45; p < 0.001) and second (ICC 0.56; p < 0.001) manual alpha angle assessment was moderate. The intra-class correlation coefficients between manual and automated alpha angle measurements were ICC = 0.24 (p = 0.052; observer 1, 1st measurement), ICC = 0.32 (p = 0.015; observer 1, 2nd measurement), ICC = 0.50 (p < 0.001; observer 2, 1st measurement), and ICC = 0.45 (p < 0.001; observer 2, 2nd measurement). Average runtime for automatic processing of the image data for the automated assessment was 16.6 ± 1.9 min. Automatic alpha angle measurements took longer (time difference: 14.6 ± 3.9 min; p < 0.001) but required less effort (difference in button presses: 231 ± 23; p < 0.001). While the automatic processing is running, the user can perform other tasks. CONCLUSIONS This pilot study demonstrates that objective and reliable automated alpha angle measurement of MRIs in FAI syndrome hips is feasible. Trial registration The Ethics Committee of the University of Düsseldorf approved our study (Registry-ID: 2017084398).
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Affiliation(s)
- Nastassja Pamela Ewertowski
- Department for Orthopedics and Trauma Surgery, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
| | | | - Daniel Benjamin Abrar
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
| | - Harish S Hosalkar
- Paradise Valley Hospital, San Diego, CA, USA.,Tri-City Medical Center, Oceanside, CA, USA.,Sharp Grossmont Hospital, La Mesa, CA, USA.,Scripps Hospital, San Diego, CA, USA
| | - Bernd Bittersohl
- Department for Orthopedics and Trauma Surgery, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany.
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Duan L, Sun H, Liu D, Tan Y, Guo Y, Chen J, Ding X. Automatic Femoral Deformity Analysis Based on the Constrained Local Models and Hough Forest. J Digit Imaging 2022; 35:162-172. [PMID: 35013828 PMCID: PMC8921433 DOI: 10.1007/s10278-021-00550-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 10/15/2021] [Accepted: 11/12/2021] [Indexed: 11/30/2022] Open
Abstract
Clinically, Taylor spatial frame (TSF) is usually used to correct femoral deformity. The first step in correction is to analyze skeletal deformities and measure the center of rotation of angulation (CORA). Since the above work needs to be done manually, the doctor's workload is heavy. Therefore, an automatic femoral deformity analysis system was proposed. Firstly, the Hough forest and constrained local models were trained on the femur image set. Then, the position and size of the femur in the X-ray image were detected by the trained Hough forest. Furthermore, the position and size were served as the initial values of the trained constrained local models to fit the femoral contour. Finally, the anatomical axis line of the proximal femur and the anatomical axis line of the distal femur could be drawn according to the fitting results. According to these lines, CORA can be found. Compared with manual measurement by doctors, the average error of the hip joint orientation line was 1.7°, the standard deviation was 1.75, the average error of the anatomic axis line of the proximal femur was 2.9°, and the standard deviation was 3.57. The automatic femoral deformity analysis system meets the accuracy requirements of orthopedics and can significantly reduce the workload of doctors.
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Affiliation(s)
- Lunhui Duan
- School of Artificial Intelligence and Data Science, Hebei University of Technology, No. 8 Guangrong Road, Hong Qiao, Tianjin, 300130, China
| | - Hao Sun
- School of Artificial Intelligence and Data Science, Hebei University of Technology, No. 8 Guangrong Road, Hong Qiao, Tianjin, 300130, China.
| | - Delong Liu
- School of Artificial Intelligence and Data Science, Hebei University of Technology, No. 8 Guangrong Road, Hong Qiao, Tianjin, 300130, China
| | - Yinglun Tan
- School of Artificial Intelligence and Data Science, Hebei University of Technology, No. 8 Guangrong Road, Hong Qiao, Tianjin, 300130, China
| | - Yue Guo
- Rehabilitation Hospital, National Research Center for Rehabilitation Technical Aids, No. 1 Ronghua Middle Road, Da Xing, Beijing, 100176, China.,Key Laboratory of Human Motion Analysis and Rehabilitation Technology of the Ministry of Civil Affairs, No. 1 Ronghua Middle Road, Da Xing, Beijing, 100176, China
| | - Jianwen Chen
- Rehabilitation Hospital, National Research Center for Rehabilitation Technical Aids, No. 1 Ronghua Middle Road, Da Xing, Beijing, 100176, China.,Key Laboratory of Human Motion Analysis and Rehabilitation Technology of the Ministry of Civil Affairs, No. 1 Ronghua Middle Road, Da Xing, Beijing, 100176, China
| | - Xiaojing Ding
- Tianjin Beichen Hospital, No. 7 Beiyi Road, Bei Chen, Tianjin, 300400, China
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