Laura Carman , Thor F. Besier , Nynke B. Rooks , Julie Choisne
{"title":"利用稀疏地标预测儿科下肢骨骼几何形状的关节形状模型","authors":"Laura Carman , Thor F. Besier , Nynke B. Rooks , Julie Choisne","doi":"10.1016/j.jbiomech.2024.112211","DOIUrl":null,"url":null,"abstract":"<div><p>Creating musculoskeletal models in a paediatric population currently involves either creating an image-based model from medical imaging data or a generic model using linear scaling. Image-based models provide a high level of accuracy but are time-consuming and costly to implement, on the other hand, linear scaling of an adult template musculoskeletal model is faster and common practice, but the output errors are significantly higher. An articulated shape model incorporates pose and shape to predict geometry for use in musculoskeletal models based on existing information from a population to provide both a fast and accurate method. From a population of 333 children aged 4–18 years old, we have developed an articulated shape model of paediatric lower limb bones to predict bone geometry from eight bone landmarks commonly used for motion capture. Bone surface root mean squared errors were found to be 2.63 ± 0.90 mm, 1.97 ± 0.61 mm, and 1.72 ± 0.51 mm for the pelvis, femur, and tibia/fibula, respectively. Linear scaling produced bone surface errors of 4.79 ± 1.39 mm, 4.38 ± 0.72 mm, and 4.39 ± 0.86 mm for the pelvis, femur, and tibia/fibula, respectively. Clinical bone measurement errors were low across all bones predicted using the articulated shape model, which outperformed linear scaling for all measurements. However, the model failed to accurately capture torsional measures (femoral anteversion and tibial torsion). Overall, the articulated shape model was shown to be a fast and accurate method to predict lower limb bone geometry in a paediatric population, superior to linear scaling.</p></div>","PeriodicalId":15168,"journal":{"name":"Journal of biomechanics","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0021929024002896/pdfft?md5=2f0de1d37ca4928f8713854c676ddc77&pid=1-s2.0-S0021929024002896-main.pdf","citationCount":"0","resultStr":"{\"title\":\"An articulated shape model to predict paediatric lower limb bone geometry using sparse landmarks\",\"authors\":\"Laura Carman , Thor F. Besier , Nynke B. Rooks , Julie Choisne\",\"doi\":\"10.1016/j.jbiomech.2024.112211\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Creating musculoskeletal models in a paediatric population currently involves either creating an image-based model from medical imaging data or a generic model using linear scaling. Image-based models provide a high level of accuracy but are time-consuming and costly to implement, on the other hand, linear scaling of an adult template musculoskeletal model is faster and common practice, but the output errors are significantly higher. An articulated shape model incorporates pose and shape to predict geometry for use in musculoskeletal models based on existing information from a population to provide both a fast and accurate method. From a population of 333 children aged 4–18 years old, we have developed an articulated shape model of paediatric lower limb bones to predict bone geometry from eight bone landmarks commonly used for motion capture. Bone surface root mean squared errors were found to be 2.63 ± 0.90 mm, 1.97 ± 0.61 mm, and 1.72 ± 0.51 mm for the pelvis, femur, and tibia/fibula, respectively. Linear scaling produced bone surface errors of 4.79 ± 1.39 mm, 4.38 ± 0.72 mm, and 4.39 ± 0.86 mm for the pelvis, femur, and tibia/fibula, respectively. Clinical bone measurement errors were low across all bones predicted using the articulated shape model, which outperformed linear scaling for all measurements. However, the model failed to accurately capture torsional measures (femoral anteversion and tibial torsion). Overall, the articulated shape model was shown to be a fast and accurate method to predict lower limb bone geometry in a paediatric population, superior to linear scaling.</p></div>\",\"PeriodicalId\":15168,\"journal\":{\"name\":\"Journal of biomechanics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0021929024002896/pdfft?md5=2f0de1d37ca4928f8713854c676ddc77&pid=1-s2.0-S0021929024002896-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of biomechanics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0021929024002896\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of biomechanics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0021929024002896","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOPHYSICS","Score":null,"Total":0}
An articulated shape model to predict paediatric lower limb bone geometry using sparse landmarks
Creating musculoskeletal models in a paediatric population currently involves either creating an image-based model from medical imaging data or a generic model using linear scaling. Image-based models provide a high level of accuracy but are time-consuming and costly to implement, on the other hand, linear scaling of an adult template musculoskeletal model is faster and common practice, but the output errors are significantly higher. An articulated shape model incorporates pose and shape to predict geometry for use in musculoskeletal models based on existing information from a population to provide both a fast and accurate method. From a population of 333 children aged 4–18 years old, we have developed an articulated shape model of paediatric lower limb bones to predict bone geometry from eight bone landmarks commonly used for motion capture. Bone surface root mean squared errors were found to be 2.63 ± 0.90 mm, 1.97 ± 0.61 mm, and 1.72 ± 0.51 mm for the pelvis, femur, and tibia/fibula, respectively. Linear scaling produced bone surface errors of 4.79 ± 1.39 mm, 4.38 ± 0.72 mm, and 4.39 ± 0.86 mm for the pelvis, femur, and tibia/fibula, respectively. Clinical bone measurement errors were low across all bones predicted using the articulated shape model, which outperformed linear scaling for all measurements. However, the model failed to accurately capture torsional measures (femoral anteversion and tibial torsion). Overall, the articulated shape model was shown to be a fast and accurate method to predict lower limb bone geometry in a paediatric population, superior to linear scaling.
期刊介绍:
The Journal of Biomechanics publishes reports of original and substantial findings using the principles of mechanics to explore biological problems. Analytical, as well as experimental papers may be submitted, and the journal accepts original articles, surveys and perspective articles (usually by Editorial invitation only), book reviews and letters to the Editor. The criteria for acceptance of manuscripts include excellence, novelty, significance, clarity, conciseness and interest to the readership.
Papers published in the journal may cover a wide range of topics in biomechanics, including, but not limited to:
-Fundamental Topics - Biomechanics of the musculoskeletal, cardiovascular, and respiratory systems, mechanics of hard and soft tissues, biofluid mechanics, mechanics of prostheses and implant-tissue interfaces, mechanics of cells.
-Cardiovascular and Respiratory Biomechanics - Mechanics of blood-flow, air-flow, mechanics of the soft tissues, flow-tissue or flow-prosthesis interactions.
-Cell Biomechanics - Biomechanic analyses of cells, membranes and sub-cellular structures; the relationship of the mechanical environment to cell and tissue response.
-Dental Biomechanics - Design and analysis of dental tissues and prostheses, mechanics of chewing.
-Functional Tissue Engineering - The role of biomechanical factors in engineered tissue replacements and regenerative medicine.
-Injury Biomechanics - Mechanics of impact and trauma, dynamics of man-machine interaction.
-Molecular Biomechanics - Mechanical analyses of biomolecules.
-Orthopedic Biomechanics - Mechanics of fracture and fracture fixation, mechanics of implants and implant fixation, mechanics of bones and joints, wear of natural and artificial joints.
-Rehabilitation Biomechanics - Analyses of gait, mechanics of prosthetics and orthotics.
-Sports Biomechanics - Mechanical analyses of sports performance.