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Li X, Gu X, Jiang Z, Duan H, Zhou J, Chang Y, Lu K, Chen B. Statistical modeling: Assessing the anatomic variability of knee joint space width. J Biomech 2023; 147:111420. [PMID: 36652892 DOI: 10.1016/j.jbiomech.2022.111420] [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: 07/11/2022] [Revised: 12/02/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Population-based knee joint space width (JSW) assessments are promising for the prevention and early diagnosis of osteoarthritis. This study aimed to establish the statistical shape and alignment model (SSAM) of knee joints for assessing anatomic variation in knee JSW in the healthy Chinese male population. CT scans of asymptomatic knee joints of healthy male participants (n = 107) were collected for manual segmentation to create mesh samples. The as-scanned positional error was reduced by a standard processing flow of deformable mesh registration. Principal component analysis (PCA) was performed to create a tibiofemoral SSAM that was trained on all mesh samples. The anatomic variability of the JSW in the healthy Chinese male population was then assessed using the SSAM with regression analysis and 3D analysis by color-coded mapping. Almost all PCA modes had a linear influence on the anatomic variation of the medial and lateral JSW. The JSW variability within the SSAM was mainly explained by mode 1 (45.1 % of variation), demonstrating that this mode had the greatest influence on JSW variation. 3D assessment of the JSW showed that the minimum medial JSW varied from 2.76 to 3.23 mm, and its site shifted a short distance on the medial tibial plateau. The root-mean-square fitting and generalization errors of the SSAM were below 1 mm. This study will benefit the design and optimization of prosthetic devices, and may be applicable to the prevention and early diagnosis of osteoarthritis.
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Affiliation(s)
- Xiaohu Li
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Xuelian Gu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Ziang Jiang
- Department of Orthopaedic Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
| | - Huabing Duan
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Jincheng Zhou
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Yihao Chang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Ke Lu
- Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University, Jiangsu 215300, China.
| | - Bo Chen
- Department of Orthopaedics, Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai 200025, China.
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Bokhimi X. Learning the Use of Artificial Intelligence in Heterogeneous Catalysis. FRONTIERS IN CHEMICAL ENGINEERING 2021. [DOI: 10.3389/fceng.2021.740270] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
We describe the use of artificial intelligence techniques in heterogeneous catalysis. This description is intended to give readers some clues for the use of these techniques in their research or industrial processes related to hydrodesulfurization. Since the description corresponds to supervised learning, first of all, we give a brief introduction to this type of learning, emphasizing the variables X and Y that define it. For each description, there is a particular emphasis on highlighting these variables. This emphasis will help define them when one works on a new application. The descriptions that we present relate to the construction of learning machines that infer adsorption energies, surface areas, adsorption isotherms of nanoporous materials, novel catalysts, and the sulfur content after hydrodesulfurization. These learning machines can predict adsorption energies with mean absolute errors of 0.15 eV for a diverse chemical space. They predict more precise surface areas of porous materials than the BET technique and can calculate their isotherms much faster than the Monte Carlo method. These machines can also predict new catalysts by learning from the catalytic behavior of materials generated through atomic substitutions. When the machines learn from the variables associated with a hydrodesulfurization process, they can predict the sulfur content in the final product.
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