1
|
Nimmal Haribabu G, Basu B. Implementing Machine Learning approaches for accelerated prediction of bone strain in acetabulum of a hip joint. J Mech Behav Biomed Mater 2024; 153:106495. [PMID: 38460455 DOI: 10.1016/j.jmbbm.2024.106495] [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: 12/05/2023] [Revised: 02/10/2024] [Accepted: 03/01/2024] [Indexed: 03/11/2024]
Abstract
The Finite Element (FE) methods for biomechanical analysis involving implant design and subject parameters for musculoskeletal applications are extensively reported in literature. Such an approach is manually intensive and computationally expensive with longer simulations times. Although Artificial Intelligence (AI) based approaches are implemented to a limited extent in biomechanics, such approaches to predict bone strain in acetabulum of a hip joint, are hardly explored. In this context, the primary objective of this paper is to evaluate machine learning (ML) models in tandem with high-fidelity FEA data for the accelerated prediction of the biomechanical response in the acetabulum of the human hip joint, during the walking gait. The parameters used in the FEA study included the subject weight, number and distribution of fins on the periphery of the acetabular shell, bone condition and phases of the gait cycle. The biomechanical response has also been evaluated using three different acetabular liners, including pre-clinically validated HDPE-20% HA-20% Al2O3, highly-crosslinked ultrahigh molecular weight polyethylene (HC-UHMWPE) and ZrO2-toughened Al2O3 (ZTA). Such parametric variation in FEA analysis, involving 26 variables and a full factorial design resulted in 10,752 datasets for spatially varying bone strains. The bone condition, as opposed to subject weight, was found to play a statistically significant role in determining the strain response in the periprosthetic bone of the acetabulum. While utilising hyperparameter tuning, K-fold cross validation and statistical learning approaches, a number of ML models were trained on the FEA dataset, and the Random Forest model performed the best with a coefficient of determination (R2) value of 0.99/0.97 and Root Mean Square Error (RMSE) of 0.02/0.01 on the training/test dataset. Taken together, this study establishes the potential of ML approach as a fast surrogate of FEA for implant biomechanics analysis, in less than a minute.
Collapse
Affiliation(s)
- Gowtham Nimmal Haribabu
- Laboratory for Biomaterials Science and Translational Research, Materials Research Centre, Indian Institute of Science, Bangalore, 560012, India
| | - Bikramjit Basu
- Laboratory for Biomaterials Science and Translational Research, Materials Research Centre, Indian Institute of Science, Bangalore, 560012, India.
| |
Collapse
|
2
|
Hagedorn JM, George TK, Aiyer R, Schmidt K, Halamka J, D’Souza RS. Artificial Intelligence and Pain Medicine: An Introduction. J Pain Res 2024; 17:509-518. [PMID: 38328019 PMCID: PMC10848920 DOI: 10.2147/jpr.s429594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 12/28/2023] [Indexed: 02/09/2024] Open
Abstract
Artificial intelligence was introduced 60 years ago and has evolved immensely since that time. While artificial intelligence is found in nearly all aspects of our life, the use of artificial intelligence in the healthcare industry has only recently become apparent and more widely discussed. It is expected that artificial intelligence will allow improved disease recognition, treatment optimization, cost and time savings, product development, decision making, and marketing. For pain medicine specifically, these same benefits will be translatable and we can expect better disease recognition and treatment selection. As adoption occurs with this impressive technology, it will be imperative for the pain medicine community to be informed on proper definitions and expected use cases for artificial intelligence. Our objective was to provide pain medicine physicians an overview of artificial intelligence, including important definitions to aid understanding, and to offer potential clinical applications pertinent to the specialty.
Collapse
Affiliation(s)
- Jonathan M Hagedorn
- Department of Anesthesiology and Perioperative Medicine, Division of Pain Medicine, Mayo Clinic, Rochester, MN, USA
| | - Tony K George
- Department of Physical Medicine and Rehabilitation, University Orthopedic Associates, Somerset, NJ, USA
| | - Rohit Aiyer
- Integrative Sports & Spine, Long Beach, CA, USA
| | - Keith Schmidt
- Comprehensive Pain Management Program, St. Alexius Medical Center, Hoffman Estates, IL, USA
| | - John Halamka
- Mayo Clinic Platform, Mayo Clinic, Rochester, MN, USA
| | - Ryan S D’Souza
- Department of Anesthesiology and Perioperative Medicine, Division of Pain Medicine, Mayo Clinic, Rochester, MN, USA
| |
Collapse
|
3
|
Alavi SE, Gholami M, Shahmabadi HE, Reher P. Resorbable GBR Scaffolds in Oral and Maxillofacial Tissue Engineering: Design, Fabrication, and Applications. J Clin Med 2023; 12:6962. [PMID: 38002577 PMCID: PMC10672220 DOI: 10.3390/jcm12226962] [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: 10/12/2023] [Revised: 11/02/2023] [Accepted: 11/04/2023] [Indexed: 11/26/2023] Open
Abstract
Guided bone regeneration (GBR) is a promising technique in bone tissue engineering that aims to replace lost or injured bone using resorbable scaffolds. The promotion of osteoblast adhesion, migration, and proliferation is greatly aided by GBR materials, and surface changes are critical in imitating the natural bone structure to improve cellular responses. Moreover, the interactions between bioresponsive scaffolds, growth factors (GFs), immune cells, and stromal progenitor cells are essential in promoting bone regeneration. This literature review comprehensively discusses various aspects of resorbable scaffolds in bone tissue engineering, encompassing scaffold design, materials, fabrication techniques, and advanced manufacturing methods, including three-dimensional printing. In addition, this review explores surface modifications to replicate native bone structures and their impact on cellular responses. Moreover, the mechanisms of bone regeneration are described, providing information on how immune cells, GFs, and bioresponsive scaffolds orchestrate tissue healing. Practical applications in clinical settings are presented to underscore the importance of these principles in promoting tissue integration, healing, and regeneration. Furthermore, this literature review delves into emerging areas of metamaterials and artificial intelligence applications in tissue engineering and regenerative medicine. These interdisciplinary approaches hold immense promise for furthering bone tissue engineering and improving therapeutic outcomes, leading to enhanced patient well-being. The potential of combining material science, advanced manufacturing, and cellular biology is showcased as a pathway to advance bone tissue engineering, addressing a variety of clinical needs and challenges. By providing this comprehensive narrative, a detailed, up-to-date account of resorbable scaffolds' role in bone tissue engineering and their transformative potential is offered.
Collapse
Affiliation(s)
- Seyed Ebrahim Alavi
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4215, Australia; (S.E.A.); (M.G.)
| | - Max Gholami
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4215, Australia; (S.E.A.); (M.G.)
| | - Hasan Ebrahimi Shahmabadi
- Immunology of Infectious Diseases Research Center, Research Institute of Basic Medical Sciences, Rafsanjan University of Medical Sciences, Rafsanjan 7717933777, Iran;
| | - Peter Reher
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4215, Australia; (S.E.A.); (M.G.)
| |
Collapse
|
4
|
Pais AI, Belinha J, Alves JL. Advances in Computational Techniques for Bio-Inspired Cellular Materials in the Field of Biomechanics: Current Trends and Prospects. MATERIALS (BASEL, SWITZERLAND) 2023; 16:ma16113946. [PMID: 37297080 DOI: 10.3390/ma16113946] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/12/2023] [Accepted: 05/20/2023] [Indexed: 06/12/2023]
Abstract
Cellular materials have a wide range of applications, including structural optimization and biomedical applications. Due to their porous topology, which promotes cell adhesion and proliferation, cellular materials are particularly suited for tissue engineering and the development of new structural solutions for biomechanical applications. Furthermore, cellular materials can be effective in adjusting mechanical properties, which is especially important in the design of implants where low stiffness and high strength are required to avoid stress shielding and promote bone growth. The mechanical response of such scaffolds can be improved further by employing functional gradients of the scaffold's porosity and other approaches, including traditional structural optimization frameworks; modified algorithms; bio-inspired phenomena; and artificial intelligence via machine learning (or deep learning). Multiscale tools are also useful in the topological design of said materials. This paper provides a state-of-the-art review of the aforementioned techniques, aiming to identify current and future trends in orthopedic biomechanics research, specifically implant and scaffold design.
Collapse
Affiliation(s)
- A I Pais
- Institute of Science and Innovation in Mechanical and Industrial Engineering (INEGI), 4200-465 Porto, Portugal
| | - J Belinha
- Department of Mechanical Engineering, ISEP, Polytechnic University of Porto, 4200-465 Porto, Portugal
| | - J L Alves
- Institute of Science and Innovation in Mechanical and Industrial Engineering (INEGI), 4200-465 Porto, Portugal
- Department of Mechanical Engineering, FEUP, University of Porto, 4200-465 Porto, Portugal
| |
Collapse
|
5
|
A Machine-Learning-Based Approach for Predicting Mechanical Performance of Semi-Porous Hip Stems. J Funct Biomater 2023; 14:jfb14030156. [PMID: 36976080 PMCID: PMC10054603 DOI: 10.3390/jfb14030156] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/07/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023] Open
Abstract
Novel designs of porous and semi-porous hip stems attempt to alleviate complications such as aseptic loosening, stress shielding, and eventual implant failure. Various designs of hip stems are modeled to simulate biomechanical performance using finite element analysis; however, these models are computationally expensive. Therefore, the machine learning approach is incorporated with simulated data to predict the new biomechanical performance of new designs of hip stems. Six types of algorithms based on machine learning were employed to validate the simulated results of finite element analysis. Afterwards, new designs of semi-porous stems with outer dense layers of 2.5 and 3 mm and porosities of 10–80% were used to predict the stiffness of the stems, stresses in outer dense layers, stresses in porous sections, and factor of safety under physiological loads using machine learning algorithms. It was determined that decision tree regression is the top-performing machine learning algorithm as per the used simulation data in terms of the validation mean absolute percentage error which equals 19.62%. It was also found that ridge regression produces the most consistent test set trend as compared with the original simulated finite element analysis results despite relying on a relatively small data set. These predicted results employing trained algorithms provided the understanding that changing the design parameters of semi-porous stems affects the biomechanical performance without carrying out finite element analysis.
Collapse
|
6
|
Computational assessment of growth of connective tissues around textured hip stem subjected to daily activities after THA. Med Biol Eng Comput 2023; 61:525-540. [PMID: 36534373 DOI: 10.1007/s11517-022-02729-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 12/09/2022] [Indexed: 12/23/2022]
Abstract
Longer-term stability of uncemented femoral stem depends on ossification at bone-implant interface. Although attempts have been made to assess the amount of bone growth using finite element (FE) analysis in combination with a mechanoregulatory algorithm, there has been little research on tissue differentiation patterns on hip stems with proximal macro-textures. The primary goal of this investigation is to qualitatively compare the formation of connective tissues around a femoral implant with/without macro-textures on its proximal surfaces. This study also predicts formation of different tissue phenotypes and their spatio-temporal distribution around a macro-textured femoral stem under routine activities. Results from the study show that non-textured implants (80 to 94%) encourage fibroplasia compared to that in textured implants (71 to 85.38%) under similar routine activity, which might trigger aseptic loosening of implant. Formation of bone was more on medio-lateral sides and towards proximal regions of Gruen zones 2 and 6, which was found to be in line with clinical observations. Fibroplasia was higher under stair climbing (85 to 91%) compared to that under normal walking (71 to 85.38%). This study suggests that stair climbing, although falls under recommended activity, might be detrimental to patient compared to normal walking in the initial rehabilitation period.
Collapse
|
7
|
Artificial intelligence and machine learning as a viable solution for hip implant failure diagnosis-Review of literature and in vitro case study. Med Biol Eng Comput 2023; 61:1239-1255. [PMID: 36701013 DOI: 10.1007/s11517-023-02779-1] [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: 08/24/2022] [Accepted: 01/09/2023] [Indexed: 01/27/2023]
Abstract
The digital health industry is experiencing fast-paced research which can provide digital care programs and technologies to enhance the competence of healthcare delivery. Orthopedic literature also confirms the applicability of artificial intelligence (AI) and machine learning (ML) models to medical diagnosis and clinical decision-making. However, implant monitoring after primary surgery often happens with a wellness visit or when a patient complains about it. Neglecting implant design and other technical errors in this scenario, unmonitored circumstances, and lack of post-surgery monitoring may ultimately lead to the implant system's failure and leave us with the only option of high-risk revision surgery. Preventive maintenance seems to be a good choice to identify the onset of an irreversible prosthesis failure. Considering all these aspects for hip implant monitoring, this paper explores existing studies linking ML models and intelligent systems for hip implant diagnosis. This paper explores the feasibility of an alternative continuous monitoring technique for post-surgery implant monitoring backed by an in vitro ML case study. Tribocorrosion and acoustic emission (AE) data are considered based on their efficacy in determining irreversible alteration of implant material to prevent total failures. This study also facilitates the relevance of developing an artificially intelligent implant monitoring methodology that can function with daily patient activities and how it can influence the digital orthopedic diagnosis. AI-based non-invasive hip implant monitoring system enabling point-of-care testing.
Collapse
|
8
|
Wu J, Zhang Y, Lyu Y, Cheng L. On the Various Numerical Techniques for the Optimization of Bone Scaffold. MATERIALS (BASEL, SWITZERLAND) 2023; 16:974. [PMID: 36769983 PMCID: PMC9917976 DOI: 10.3390/ma16030974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/09/2023] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
As the application of bone scaffolds becomes more and more widespread, the requirements for the high performance of bone scaffolds are also increasing. The stiffness and porosity of porous structures can be adjusted as needed, making them good candidates for repairing damaged bone tissues. However, the development of porous bone structures is limited by traditional manufacturing methods. Today, the development of additive manufacturing technology has made it very convenient to manufacture bionic porous bone structures as needed. In the present paper, the current state-of-the-art optimization techniques for designing the scaffolds and the settings of different optimization methods are introduced. Additionally, various design methods for bone scaffolds are reviewed. Furthermore, the challenges in designing high performance bone scaffolds and the future developments of bone scaffolds are also presented.
Collapse
Affiliation(s)
- Jiongyi Wu
- Department of Engineering Mechanics, Dalian University of Technology, No. 2 Linggong Road, Dalian 116024, China
| | - Youwei Zhang
- Department of Engineering Mechanics, Dalian University of Technology, No. 2 Linggong Road, Dalian 116024, China
| | - Yongtao Lyu
- Department of Engineering Mechanics, Dalian University of Technology, No. 2 Linggong Road, Dalian 116024, China
- State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, No. 2 Linggong Road, Dalian 116024, China
| | - Liangliang Cheng
- Department of Orthopeadics, Affiliated Zhongshan Hospital of Dalian University, No. 6 Jiefang Street, Dalian 116001, China
| |
Collapse
|
9
|
Farhadi F, Barnes MR, Sugito HR, Sin JM, Henderson ER, Levy JJ. Applications of artificial intelligence in orthopaedic surgery. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:995526. [PMID: 36590152 PMCID: PMC9797865 DOI: 10.3389/fmedt.2022.995526] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
The practice of medicine is rapidly transforming as a result of technological breakthroughs. Artificial intelligence (AI) systems are becoming more and more relevant in medicine and orthopaedic surgery as a result of the nearly exponential growth in computer processing power, cloud based computing, and development, and refining of medical-task specific software algorithms. Because of the extensive role of technologies such as medical imaging that bring high sensitivity, specificity, and positive/negative prognostic value to management of orthopaedic disorders, the field is particularly ripe for the application of machine-based integration of imaging studies, among other applications. Through this review, we seek to promote awareness in the orthopaedics community of the current accomplishments and projected uses of AI and ML as described in the literature. We summarize the current state of the art in the use of ML and AI in five key orthopaedic disciplines: joint reconstruction, spine, orthopaedic oncology, trauma, and sports medicine.
Collapse
Affiliation(s)
- Faraz Farhadi
- Geisel School of Medicine, Dartmouth College, Hanover, NH, United States,Radiology and Imaging Sciences, National Institutes of Health (NIH), Bethesda, United States,Correspondence: Faraz Farhadi Joshua J. Levy
| | - Matthew R. Barnes
- Geisel School of Medicine, Dartmouth College, Hanover, NH, United States
| | - Harun R. Sugito
- Geisel School of Medicine, Dartmouth College, Hanover, NH, United States
| | - Jessica M. Sin
- Department of Radiology, Dartmouth Health, Lebanon, United States
| | - Eric R. Henderson
- Department of Orthopaedics, Dartmouth Health, Lebanon, United States
| | - Joshua J. Levy
- Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, NH, United States,Correspondence: Faraz Farhadi Joshua J. Levy
| |
Collapse
|
10
|
Kamel Shehata MEM, Mustapha K, Shehata E. Finite Element and Multivariate Random Forests Modelling for Stress Shield Attenuation in Customized Hip Implants. FORCES IN MECHANICS 2022. [DOI: 10.1016/j.finmec.2022.100151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
11
|
Ghosh R, Chanda S, Chakraborty D. Application of finite element analysis to tissue differentiation and bone remodelling approaches and their use in design optimization of orthopaedic implants: A review. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3637. [PMID: 35875869 DOI: 10.1002/cnm.3637] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 06/26/2022] [Accepted: 07/21/2022] [Indexed: 06/15/2023]
Abstract
Post-operative bone growth and long-term bone adaptation around the orthopaedic implants are simulated using the mechanoregulation based tissue-differentiation and adaptive bone remodelling algorithms, respectively. The primary objective of these algorithms was to assess biomechanical feasibility and reliability of orthopaedic implants. This article aims to offer a comprehensive review of the developments in mathematical models of tissue-differentiation and bone adaptation and their applications in studies involving design optimization of orthopaedic implants over three decades. Despite the different mechanoregulatory models developed, existing literature confirm that none of the models can be highly regarded or completely disregarded over each other. Not much development in mathematical formulations has been observed from the current state of knowledge due to the lack of in vivo studies involving clinically relevant animal models, which further retarded the development of such models to use in translational research at a fast pace. Future investigations involving artificial intelligence (AI), soft-computing techniques and combined tissue-differentiation and bone-adaptation studies involving animal subjects for model verification are needed to formulate more sophisticated mathematical models to enhance the accuracy of pre-clinical testing of orthopaedic implants.
Collapse
Affiliation(s)
- Rajdeep Ghosh
- Composite Structures and Fracture Mechanics Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India
| | - Souptick Chanda
- Biomechanics and Simulations Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India
- Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Guwahati, Guwahati, Assam, India
| | - Debabrata Chakraborty
- Composite Structures and Fracture Mechanics Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India
| |
Collapse
|
12
|
Lu Y, Huo Y, Yang Z, Niu Y, Zhao M, Bosiakov S, Li L. Influence of the parameters of the convolutional neural network model in predicting the effective compressive modulus of porous structure. Front Bioeng Biotechnol 2022; 10:985688. [PMID: 36185439 PMCID: PMC9520359 DOI: 10.3389/fbioe.2022.985688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 08/19/2022] [Indexed: 11/23/2022] Open
Abstract
In recent years, the convolutional neural network (CNN) technique has emerged as an efficient new method for designing porous structure, but a CNN model generally contains a large number of parameters, each of which could influence the predictive ability of the CNN model. Furthermore, there is no consensus on the setting of each parameter in the CNN model. Therefore, the present study aimed to investigate the sensitivity of the parameters in the CNN model for the prediction of the mechanical property of porous structures. 10,500 samples of porous structure were randomly generated, and their effective compressive moduli obtained from finite element analysis were used as the ground truths to construct and train a CNN model. 8,000 of the samples were used to train the CNN model, 2000 samples were used for the cross-validation of the CNN model and the remaining 500 new structures, which did not participate in the CNN training process, were used to test the predictive power of the CNN model. The sensitivity of the number of convolutional layers, the number of convolution kernels, the number of pooling layers, the number of fully connected layers and the optimizer in the CNN model were then investigated. The results showed that the optimizer has the largest influence on the training speed, while the fully connected layer has the least impact on the training speed. Additionally, the pooling layer has the largest impact on the predictive ability while the optimizer has the least impact on the predictive ability. In conclusion, the parameters of the CNN model play an important role in the performance of the CNN model and the parameter sensitivity analysis can help optimize the CNN model to increase the computational efficiency.
Collapse
Affiliation(s)
- Yongtao Lu
- Department of Engineering Mechanics, Dalian University of Technology, Dalian, China
- DUT-BSU Joint Institute, Dalian University of Technology, Dalian, China
| | - Yi Huo
- Department of Engineering Mechanics, Dalian University of Technology, Dalian, China
| | - Zhuoyue Yang
- Xi’an Aerospace Propulsion Institute, Xi’an, China
| | - Yibiao Niu
- Department of Engineering Mechanics, Dalian University of Technology, Dalian, China
| | - Ming Zhao
- Department of Engineering Mechanics, Dalian University of Technology, Dalian, China
| | - Sergei Bosiakov
- Faculty of Mechanics and Mathematics, Belarusian State University, Minsk, Belarus
| | - Lei Li
- Department of Vascular Surgery, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
- *Correspondence: Lei Li,
| |
Collapse
|
13
|
Choudhury S, Rana M, Chakraborty A, Majumder S, Roy S, RoyChowdhury A, Datta S. Design of patient specific basal dental implant using Finite Element method and Artificial Neural Network technique. Proc Inst Mech Eng H 2022; 236:1375-1387. [PMID: 35880901 DOI: 10.1177/09544119221114729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The bone conditions of mandibular bone vary from patient to patient, and as a result, a patient-specific dental implant needs to be designed. The basal dental implant is implanted in the cortical region of the bone since the top surface of the bone narrows down because of aging. Taguchi designs of experiments technique are used in which 25 optimum solid models of basal dental implants are modeled with variable geometrical parameters, viz. thread length, diameter, and pitch. In the solid models the implants are placed in the cortical part of the 3D models of cadaveric mandibles, that are prepared from CT data using image processing software. Patient-specific bone conditions are varied according to the strong, weak, and normal basal bone. A compressive force of 200 N is applied on the top surface of these implants and using finite element analysis software, the microstrain on the peri-implant bone ranges from 1000 to 4000 depending on the various bone conditions. According to the finite element data, it can be concluded that weak bone microstrain is comparatively high compared with normal and strong bone conditions. A surrogate artificial neural network model is prepared from the finite element analysis data. Surrogate model assisted genetic algorithm is used to find the optimum patient-specific basal dental implant for a better osseointegration-friendly mechanical environment.
Collapse
Affiliation(s)
- Sandeep Choudhury
- Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India
| | - Masud Rana
- Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India
| | - Arindam Chakraborty
- Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India
| | - Santanu Majumder
- Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India
| | - Sandipan Roy
- Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
| | - Amit RoyChowdhury
- Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India
| | - Shubhabrata Datta
- Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
| |
Collapse
|
14
|
Basu B, Gowtham N, Xiao Y, Kalidindi SR, Leong KW. Biomaterialomics: Data science-driven pathways to develop fourth-generation biomaterials. Acta Biomater 2022; 143:1-25. [PMID: 35202854 DOI: 10.1016/j.actbio.2022.02.027] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 02/16/2022] [Accepted: 02/17/2022] [Indexed: 12/12/2022]
Abstract
Conventional approaches to developing biomaterials and implants require intuitive tailoring of manufacturing protocols and biocompatibility assessment. This leads to longer development cycles, and high costs. To meet existing and unmet clinical needs, it is critical to accelerate the production of implantable biomaterials, implants and biomedical devices. Building on the Materials Genome Initiative, we define the concept 'biomaterialomics' as the integration of multi-omics data and high-dimensional analysis with artificial intelligence (AI) tools throughout the entire pipeline of biomaterials development. The Data Science-driven approach is envisioned to bring together on a single platform, the computational tools, databases, experimental methods, machine learning, and advanced manufacturing (e.g., 3D printing) to develop the fourth-generation biomaterials and implants, whose clinical performance will be predicted using 'digital twins'. While analysing the key elements of the concept of 'biomaterialomics', significant emphasis has been put forward to effectively utilize high-throughput biocompatibility data together with multiscale physics-based models, E-platform/online databases of clinical studies, data science approaches, including metadata management, AI/ Machine Learning (ML) algorithms and uncertainty predictions. Such integrated formulation will allow one to adopt cross-disciplinary approaches to establish processing-structure-property (PSP) linkages. A few published studies from the lead author's research group serve as representative examples to illustrate the formulation and relevance of the 'Biomaterialomics' approaches for three emerging research themes, i.e. patient-specific implants, additive manufacturing, and bioelectronic medicine. The increased adaptability of AI/ML tools in biomaterials science along with the training of the next generation researchers in data science are strongly recommended. STATEMENT OF SIGNIFICANCE: This leading opinion review paper emphasizes the need to integrate the concepts and algorithms of the data science with biomaterials science. Also, this paper emphasizes the need to establish a mathematically rigorous cross-disciplinary framework that will allow a systematic quantitative exploration and curation of critical biomaterials knowledge needed to drive objectively the innovation efforts within a suitable uncertainty quantification framework, as embodied in 'biomaterialomics' concept, which integrates multi-omics data and high-dimensional analysis with artificial intelligence (AI) tools, like machine learning. The formulation of this approach has been demonstrated for patient-specific implants, additive manufacturing, and bioelectronic medicine.
Collapse
|
15
|
Multi-Objective Optimization of Low Reynolds Number Airfoil Using Convolutional Neural Network and Non-Dominated Sorting Genetic Algorithm. AEROSPACE 2022. [DOI: 10.3390/aerospace9010035] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
The airfoil is the prime component of flying vehicles. For low-speed flights, low Reynolds number airfoils are used. The characteristic of low Reynolds number airfoils is a laminar separation bubble and an associated drag rise. This paper presents a framework for the design of a low Reynolds number airfoil. The contributions of the proposed research are twofold. First, a convolutional neural network (CNN) is designed for the aerodynamic coefficient prediction of low Reynolds number airfoils. Data generation is discussed in detail and XFOIL is selected to obtain aerodynamic coefficients. The performance of the CNN is evaluated using different learning rate schedulers and adaptive learning rate optimizers. The trained model can predict the aerodynamic coefficients with high accuracy. Second, the trained model is used with a non-dominated sorting genetic algorithm (NSGA-II) for multi-objective optimization of the low Reynolds number airfoil at a specific angle of attack. A similar optimization is performed using NSGA-II directly calling XFOIL, to obtain the aerodynamic coefficients. The Pareto fronts of both optimizations are compared, and it is concluded that the proposed CNN can replicate the actual Pareto in considerably less time.
Collapse
|
16
|
Suwardi A, Wang F, Xue K, Han MY, Teo P, Wang P, Wang S, Liu Y, Ye E, Li Z, Loh XJ. Machine Learning-Driven Biomaterials Evolution. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2102703. [PMID: 34617632 DOI: 10.1002/adma.202102703] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 07/09/2021] [Indexed: 06/13/2023]
Abstract
Biomaterials is an exciting and dynamic field, which uses a collection of diverse materials to achieve desired biological responses. While there is constant evolution and innovation in materials with time, biomaterials research has been hampered by the relatively long development period required. In recent years, driven by the need to accelerate materials development, the applications of machine learning in materials science has progressed in leaps and bounds. The combination of machine learning with high-throughput theoretical predictions and high-throughput experiments (HTE) has shifted the traditional Edisonian (trial and error) paradigm to a data-driven paradigm. In this review, each type of biomaterial and their key properties and use cases are systematically discussed, followed by how machine learning can be applied in the development and design process. The discussions are classified according to various types of materials used including polymers, metals, ceramics, and nanomaterials, and implants using additive manufacturing. Last, the current gaps and potential of machine learning to further aid biomaterials discovery and application are also discussed.
Collapse
Affiliation(s)
- Ady Suwardi
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - FuKe Wang
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Kun Xue
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Ming-Yong Han
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Peili Teo
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Pei Wang
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Shijie Wang
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Ye Liu
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Enyi Ye
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Zibiao Li
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Xian Jun Loh
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| |
Collapse
|
17
|
A Critical Review of the Design, Manufacture, and Evaluation of Bone Joint Replacements for Bone Repair. MATERIALS 2021; 15:ma15010153. [PMID: 35009299 PMCID: PMC8746215 DOI: 10.3390/ma15010153] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/04/2021] [Accepted: 12/22/2021] [Indexed: 11/17/2022]
Abstract
With the change of people’s living habits, bone trauma has become a common clinical disease. A large number of bone joint replacements is performed every year around the world. Bone joint replacement is a major approach for restoring the functionalities of human joints caused by bone traumas or some chronic bone diseases. However, the current bone joint replacement products still cannot meet the increasing demands and there is still room to increase the performance of the current products. The structural design of the implant is crucial because the performance of the implant relies heavily on its geometry and microarchitecture. Bionic design learning from the natural structure is widely used. With the progress of technology, machine learning can be used to optimize the structure of bone implants, which may become the focus of research in the future. In addition, the optimization of the microstructure of bone implants also has an important impact on its performance. The widely used design algorithm for the optimization of bone joint replacements is reviewed in the present study. Regarding the manufacturing of the implant, the emerging additive manufacturing technique provides more room for the design of complex microstructures. The additive manufacturing technique has enabled the production of bone joint replacements with more complex internal structures, which makes the design process more convenient. Numerical modeling plays an important role in the evaluation of the performance of an implant. For example, theoretical and numerical analysis can be carried out by establishing a musculoskeletal model to prepare for the practical use of bone implants. Besides, the in vitro and in vivo testing can provide mechanical properties of bone implants that are more in line with the implant recipient’s situation. In the present study, the progress of the design, manufacture, and evaluation of the orthopedic implant, especially the joint replacement, is critically reviewed.
Collapse
|
18
|
Ghosh R, Chanda S, Chakraborty D. Influence of sequential opening/closing of interface gaps and texture density on bone growth over macro-textured implant surfaces using FE based mechanoregulatory algorithm. Comput Methods Biomech Biomed Engin 2021; 25:985-999. [PMID: 34698599 DOI: 10.1080/10255842.2021.1994960] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Intramedullary implant fixation is achieved through a press-fit between the implant and the host bone. A stronger press-fit between the bone and the prosthesis often introduces damage to the bone canal creating micro-gaps. The aim of the present investigation is to study the influences of simultaneous opening/closing of gaps on bone growth over macro-textured implant surfaces. Models based on textures available on CORAIL and SP-CL hip stems have been considered and 3D finite element (FE) analysis has been carried out in conjunction with mechanoregulation based tissue differentiation algorithm. Additionally, using a full-factorial approach, different combinations (between 5 µm to 15 µm) of sliding and gap distances at the bone-implant interface were considered to understand their combined influences on bone growth. All designs show an elevated fibrous tissue formation (10.96% at 5 µm to 29.38% at 40 µm for CORAIL based textured model; 11.45% at 5 µm to 32.25% at 40 µm for SP-CL based textured model) and inhibition of soft cartilaginous tissue (75.64% at 5 µm to 53.94% at 40 µm for CORAIL based model; 76.02% at 5 µm to 53.60% at 40 µm SP-CL based model) at progressively higher levels of normal micromotion, leading to a fragile bone-implant interface. These results highlight the importance of minimizing both sliding and gap distances simultaneously to enhance bone growth and implant stability. Further, results from the studies with differential texture density over CORAIL based implant reveal a non-linear complex relationship between tissue growth and texture density which might be investigated in a machine learning framework.
Collapse
Affiliation(s)
- Rajdeep Ghosh
- Composite Structures and Fracture Mechanics Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India
| | - Souptick Chanda
- Biomechanics and Simulations Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India.,Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Guwahati, Guwahati, Assam, India
| | - Debabrata Chakraborty
- Composite Structures and Fracture Mechanics Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India
| |
Collapse
|
19
|
Lafuente-Gracia L, Borgiani E, Nasello G, Geris L. Towards in silico Models of the Inflammatory Response in Bone Fracture Healing. Front Bioeng Biotechnol 2021; 9:703725. [PMID: 34660547 PMCID: PMC8514728 DOI: 10.3389/fbioe.2021.703725] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 09/07/2021] [Indexed: 12/21/2022] Open
Abstract
In silico modeling is a powerful strategy to investigate the biological events occurring at tissue, cellular and subcellular level during bone fracture healing. However, most current models do not consider the impact of the inflammatory response on the later stages of bone repair. Indeed, as initiator of the healing process, this early phase can alter the regenerative outcome: if the inflammatory response is too strongly down- or upregulated, the fracture can result in a non-union. This review covers the fundamental information on fracture healing, in silico modeling and experimental validation. It starts with a description of the biology of fracture healing, paying particular attention to the inflammatory phase and its cellular and subcellular components. We then discuss the current state-of-the-art regarding in silico models of the immune response in different tissues as well as the bone regeneration process at the later stages of fracture healing. Combining the aforementioned biological and computational state-of-the-art, continuous, discrete and hybrid modeling technologies are discussed in light of their suitability to capture adequately the multiscale course of the inflammatory phase and its overall role in the healing outcome. Both in the establishment of models as in their validation step, experimental data is required. Hence, this review provides an overview of the different in vitro and in vivo set-ups that can be used to quantify cell- and tissue-scale properties and provide necessary input for model credibility assessment. In conclusion, this review aims to provide hands-on guidance for scientists interested in building in silico models as an additional tool to investigate the critical role of the inflammatory phase in bone regeneration.
Collapse
Affiliation(s)
- Laura Lafuente-Gracia
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium.,Prometheus: Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium
| | - Edoardo Borgiani
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium.,Prometheus: Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium.,Biomechanics Research Unit, GIGA in silico Medicine, University of Liège, Liège, Belgium
| | - Gabriele Nasello
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium.,Prometheus: Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium.,Skeletal Biology and Engineering Research Center, KU Leuven, Leuven, Belgium
| | - Liesbet Geris
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium.,Prometheus: Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium.,Biomechanics Research Unit, GIGA in silico Medicine, University of Liège, Liège, Belgium.,Skeletal Biology and Engineering Research Center, KU Leuven, Leuven, Belgium
| |
Collapse
|
20
|
Lalehzarian SP, Gowd AK, Liu JN. Machine learning in orthopaedic surgery. World J Orthop 2021; 12:685-699. [PMID: 34631452 PMCID: PMC8472446 DOI: 10.5312/wjo.v12.i9.685] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/12/2021] [Accepted: 08/05/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence and machine learning in orthopaedic surgery has gained mass interest over the last decade or so. In prior studies, researchers have demonstrated that machine learning in orthopaedics can be used for different applications such as fracture detection, bone tumor diagnosis, detecting hip implant mechanical loosening, and grading osteoarthritis. As time goes on, the utility of artificial intelligence and machine learning algorithms, such as deep learning, continues to grow and expand in orthopaedic surgery. The purpose of this review is to provide an understanding of the concepts of machine learning and a background of current and future orthopaedic applications of machine learning in risk assessment, outcomes assessment, imaging, and basic science fields. In most cases, machine learning has proven to be just as effective, if not more effective, than prior methods such as logistic regression in assessment and prediction. With the help of deep learning algorithms, such as artificial neural networks and convolutional neural networks, artificial intelligence in orthopaedics has been able to improve diagnostic accuracy and speed, flag the most critical and urgent patients for immediate attention, reduce the amount of human error, reduce the strain on medical professionals, and improve care. Because machine learning has shown diagnostic and prognostic uses in orthopaedic surgery, physicians should continue to research these techniques and be trained to use these methods effectively in order to improve orthopaedic treatment.
Collapse
Affiliation(s)
- Simon P Lalehzarian
- The Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL 60064, United States
| | - Anirudh K Gowd
- Department of Orthopaedic Surgery, Wake Forest Baptist Medical Center, Winston-Salem, NC 27157, United States
| | - Joseph N Liu
- USC Epstein Family Center for Sports Medicine, Keck Medicine of USC, Los Angeles, CA 90033, United States
| |
Collapse
|
21
|
Ghosh R, Chanda S, Chakraborty D. Qualitative predictions of bone growth over optimally designed macro-textured implant surfaces obtained using NN-GA based machine learning framework. Med Eng Phys 2021; 95:64-75. [PMID: 34479694 DOI: 10.1016/j.medengphy.2021.08.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 08/06/2021] [Accepted: 08/09/2021] [Indexed: 01/23/2023]
Abstract
The surface features on implant surface can improve biologic fixation of the implant with the host bone leading to improved secondary (biological) implant stability. Application of finite element (FE) based mechanoregulatory schemes to estimate the amount of bone growth for a wide range of implant surface features is either manually intensive or computationally expensive. This study adopts an integrated approach combining FE, back-propagation neural network (BPNN) and genetic algorithm (GA) based search to evaluate optimum surface macro-textures from three representative implant models so as to enhance bone growth. Initial surface textures chosen for the implant models were based on an earlier investigation. Based on FE predicted dataset, a BPNN was formulated for faster prediction of bone growth. Using the BPNN predicted output, a GA-based search was carried out to maximize bone growth subject to clinically admissible micromotion at the bone-implant interface. The results from FE analysis and bone growth predictions from the BPNN were found to have strong correlation. The optimal osseointegration-maximized-textures (OMTs) obtained were found to offer enhanced biological fixation, as compared to that offered by the textures in the initial models. Results from the present study reveal that certain reduction in the dimension of ribs/grooves promotes bone growth. However, periodic patterns of ribs with higher and lower rib dimensions provide uniform stress environment at the interface thus promoting osseointegration.
Collapse
Affiliation(s)
- Rajdeep Ghosh
- Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam 781 039, India
| | - Souptick Chanda
- Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, Assam 781 039, India.
| | - Debabrata Chakraborty
- Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam 781 039, India
| |
Collapse
|
22
|
Galbusera F, Cina A, Panico M, Albano D, Messina C. Image-based biomechanical models of the musculoskeletal system. Eur Radiol Exp 2020; 4:49. [PMID: 32789547 PMCID: PMC7423821 DOI: 10.1186/s41747-020-00172-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 06/30/2020] [Indexed: 12/31/2022] Open
Abstract
Finite element modeling is a precious tool for the investigation of the biomechanics of the musculoskeletal system. A key element for the development of anatomically accurate, state-of-the art finite element models is medical imaging. Indeed, the workflow for the generation of a finite element model includes steps which require the availability of medical images of the subject of interest: segmentation, which is the assignment of each voxel of the images to a specific material such as bone and cartilage, allowing for a three-dimensional reconstruction of the anatomy; meshing, which is the creation of the computational mesh necessary for the approximation of the equations describing the physics of the problem; assignment of the material properties to the various parts of the model, which can be estimated for example from quantitative computed tomography for the bone tissue and with other techniques (elastography, T1rho, and T2 mapping from magnetic resonance imaging) for soft tissues. This paper presents a brief overview of the techniques used for image segmentation, meshing, and assessing the mechanical properties of biological tissues, with focus on finite element models of the musculoskeletal system. Both consolidated methods and recent advances such as those based on artificial intelligence are described.
Collapse
Affiliation(s)
| | - Andrea Cina
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Matteo Panico
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.,Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.,Department of Biomedicine, Neuroscience and Advanced Diagnostics, Università degli Studi di Palermo, Palermo, Italy
| | - Carmelo Messina
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.,Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
| |
Collapse
|
23
|
Klosterhoff BS, Kaiser J, Nelson BD, Karipott SS, Ruehle MA, Hollister SJ, Weiss JA, Ong KG, Willett NJ, Guldberg RE. Wireless sensor enables longitudinal monitoring of regenerative niche mechanics during rehabilitation that enhance bone repair. Bone 2020; 135:115311. [PMID: 32156664 PMCID: PMC7585453 DOI: 10.1016/j.bone.2020.115311] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 02/20/2020] [Accepted: 03/03/2020] [Indexed: 10/24/2022]
Abstract
Mechanical loads exerted on the skeleton during activities such as walking are important regulators of bone repair, but dynamic biomechanical signals are difficult to measure inside the body. The inability to measure the mechanical environment in injured tissues is a significant barrier to developing integrative regenerative and rehabilitative strategies that can accelerate recovery from fracture, segmental bone loss, and spinal fusion. Here we engineered an implantable strain sensor platform and longitudinally measured strain across a bone defect in real-time throughout rehabilitation. The results showed that load-sharing permitted by a load-sharing fixator initially delivered a two-fold increase in deformation magnitude, subsequently increased mineralized bridging by nearly three-fold, and increased bone formation by over 60%. These data implicate a critical role for early mechanical cues on the long term healing response as strain cycle magnitude at 1 week (before appreciable healing occurred) had a significant positive correlation with the long-term bone regeneration outcomes. Furthermore, we found that sensor readings correlated with the status of healing, suggesting a role for strain sensing as an X-ray-free healing assessment platform. Therefore, non-invasive strain measurements may possess diagnostic potential to evaluate bone repair and reduce clinical reliance on current radiation-emitting imaging methods. Together, this study demonstrates a promising framework to quantitatively develop and exploit mechanical rehabilitation strategies that enhance bone repair.
Collapse
Affiliation(s)
- Brett S Klosterhoff
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America; Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Jarred Kaiser
- Research Service, Atlanta VA Medical Center, Decatur, GA, United States of America; Department of Orthopaedics, Emory University, Atlanta, GA, United States of America
| | - Bradley D Nelson
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, United States of America
| | - Salil S Karipott
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, United States of America
| | - Marissa A Ruehle
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA
| | - Scott J Hollister
- Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, United States of America; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA
| | - Jeffrey A Weiss
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States of America; Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States of America; Department of Orthopedics, University of Utah, Salt Lake City, UT, United States of America
| | - Keat Ghee Ong
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, United States of America
| | - Nick J Willett
- Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, United States of America; Research Service, Atlanta VA Medical Center, Decatur, GA, United States of America; Department of Orthopaedics, Emory University, Atlanta, GA, United States of America; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA
| | - Robert E Guldberg
- Knight Campus, University of Oregon, Eugene, OR, United States of America.
| |
Collapse
|
24
|
Myers TG, Ramkumar PN, Ricciardi BF, Urish KL, Kipper J, Ketonis C. Artificial Intelligence and Orthopaedics: An Introduction for Clinicians. J Bone Joint Surg Am 2020; 102:830-840. [PMID: 32379124 PMCID: PMC7508289 DOI: 10.2106/jbjs.19.01128] [Citation(s) in RCA: 103] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
➤Artificial intelligence (AI) provides machines with the ability to perform tasks using algorithms governed by pattern recognition and self-correction on large amounts of data to narrow options in order to avoid errors. ➤The 4 things necessary for AI in medicine include big data sets, powerful computers, cloud computing, and open source algorithmic development. ➤The use of AI in health care continues to expand, and its impact on orthopaedic surgery can already be found in diverse areas such as image recognition, risk prediction, patient-specific payment models, and clinical decision-making. ➤Just as the business of medicine was once considered outside the domain of the orthopaedic surgeon, emerging technologies such as AI warrant ownership, leverage, and application by the orthopaedic surgeon to improve the care that we provide to the patients we serve. ➤AI could provide solutions to factors contributing to physician burnout and medical mistakes. However, challenges regarding the ethical deployment, regulation, and the clinical superiority of AI over traditional statistics and decision-making remain to be resolved.
Collapse
Affiliation(s)
- Thomas G. Myers
- Divisions of Adult Reconstruction (T.G.M. and B.F.R.) and Hand and Upper Extremity Surgery (C.K.), Department of Orthopaedics and Rehabilitation, University of Rochester Medical Center, Rochester, New York
| | - Prem N. Ramkumar
- Machine Learning Arthroplasty Laboratory, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Benjamin F. Ricciardi
- Divisions of Adult Reconstruction (T.G.M. and B.F.R.) and Hand and Upper Extremity Surgery (C.K.), Department of Orthopaedics and Rehabilitation, University of Rochester Medical Center, Rochester, New York
| | - Kenneth L. Urish
- Department of Orthopaedics and The Bone and Joint Center, Magee Women’s Hospital of the University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Jens Kipper
- Department of Philosophy, University of Rochester, Rochester, New York
| | - Constantinos Ketonis
- Divisions of Adult Reconstruction (T.G.M. and B.F.R.) and Hand and Upper Extremity Surgery (C.K.), Department of Orthopaedics and Rehabilitation, University of Rochester Medical Center, Rochester, New York
| |
Collapse
|
25
|
Poduval M, Ghose A, Manchanda S, Bagaria V, Sinha A. Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics. Indian J Orthop 2020; 54:109-122. [PMID: 32257027 PMCID: PMC7096590 DOI: 10.1007/s43465-019-00023-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Accepted: 09/18/2019] [Indexed: 02/04/2023]
Abstract
Orthopaedics as a surgical discipline requires a combination of good clinical acumen, good surgical skill, a reasonable physical strength and most of all, good understanding of technology. The last few decades have seen rapid adoption of new technologies into orthopaedic practice, power tools, new implants, CAD-CAM design, 3-D printing, additive manufacturing just to name a few. The new disruption in orthopaedics in the current time and era is undoubtedly the advent of artificial intelligence and robotics. As these technologies take root and innovative applications continue to be incorporated into the main-stream orthopedics, as we know it today, it is imperative to look at and understand the basics of artificial intelligence and what work is being done in the field today. This article takes the form of a loosely structured narrative review and will introduce the reader to key concepts in the field of artificial intelligence as well as some of the directions in application of the same in orthopaedics. Some of the recent work has been summarised and we present our viewpoint at the conclusion as to why we must consider artificial intelligence as a disrupting positive influence on orthopaedic surgery.
Collapse
Affiliation(s)
- Murali Poduval
- Tata Consultancy Services, Unit 129/130, SDF V, SEEPZ, Andheri East, Mumbai, 400093 India
| | - Avik Ghose
- TCS Research and Innovation, Tata Consultancy Services, Kolkata, 700160 India
| | - Sanjeev Manchanda
- TCS Research and Innovation, Tata Consultancy Services, Unit 129/130, SEEPZ, Andheri East, Mumbai, 400096 India
| | | | - Aniruddha Sinha
- TCS Research and Innovation, Tata Consultancy Services, Kolkata, 700160 India
| |
Collapse
|
26
|
Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV. MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2019. [DOI: 10.3390/make1020033] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This article presents an optimisation framework that uses stochastic multi-objective optimisation, combined with an Artificial Neural Network (ANN), and describes its application to the aerodynamic design of aircraft shapes. The framework uses the Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm and the obtained results confirm that the proposed technique provides highly optimal solutions in less computational time than other approaches to the same design problem. The main idea was to focus computational effort on worthwhile design solutions rather than exploring and evaluating all possible solutions in the design space. It is shown that the number of valid solutions obtained using ANN-MOPSO compared to MOPSO for 3000 evaluations grew from 529 to 1006 (90% improvement) with a penalty of only 8.3% (11 min) in computational time. It is demonstrated that including an ANN, the ANN-MOPSO with 3000 evaluations produced a larger number of valid solutions than the MOPSO with 5500 evaluations, and in 33% less computational time (64 min). This is taken as confirmation of the potential power of ANNs when applied to this type of design problem.
Collapse
|
27
|
Ziaeipoor H, Taylor M, Pandy M, Martelli S. A novel training-free method for real-time prediction of femoral strain. J Biomech 2019; 86:110-116. [PMID: 30777342 DOI: 10.1016/j.jbiomech.2019.01.057] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 12/28/2018] [Accepted: 01/30/2019] [Indexed: 11/29/2022]
Abstract
Surrogate methods for rapid calculation of femoral strain are limited by the scope of the training data. We compared a newly developed training-free method based on the superposition principle (Superposition Principle Method, SPM) and popular surrogate methods for calculating femoral strain during activity. Finite-element calculations of femoral strain, muscle, and joint forces for five different activity types were obtained previously. Multi-linear regression, multivariate adaptive regression splines, and Gaussian process were trained for 50, 100, 200, and 300 random samples generated using Latin Hypercube (LH) and Design of Experiment (DOE) sampling. The SPM method used weighted linear combinations of 173 activity-independent finite-element analyses accounting for each muscle and hip contact force. Across the surrogate methods, we found that 200 DOE samples consistently provided low error (RMSE < 100 µε), with model construction time ranging from 3.8 to 63.3 h and prediction time ranging from 6 to 1236 s per activity. The SPM method provided the lowest error (RMSE = 40 µε), the fastest model construction time (3.2 h) and the second fastest prediction time per activity (36 s) after Multi-linear Regression (6 s). The SPM method will enable large numerical studies of femoral strain and will narrow the gap between bone strain prediction and real-time clinical applications.
Collapse
Affiliation(s)
- Hamed Ziaeipoor
- Medical Device Research Institute, College of Science and Engineering, Flinders University, Clovelly Park, SA, Australia.
| | - Mark Taylor
- Medical Device Research Institute, College of Science and Engineering, Flinders University, Clovelly Park, SA, Australia
| | - Marcus Pandy
- Department of Mechanical Engineering, University of Melbourne, Parkville, VIC, Australia
| | - Saulo Martelli
- Medical Device Research Institute, College of Science and Engineering, Flinders University, Clovelly Park, SA, Australia
| |
Collapse
|
28
|
Ziaeipoor H, Martelli S, Pandy M, Taylor M. Efficacy and efficiency of multivariate linear regression for rapid prediction of femoral strain fields during activity. Med Eng Phys 2018; 63:88-92. [PMID: 30551929 DOI: 10.1016/j.medengphy.2018.12.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Revised: 11/19/2018] [Accepted: 12/04/2018] [Indexed: 11/19/2022]
Abstract
Multivariate Linear Regression-based (MLR) surrogate models were explored to reduce the computational cost of predicting femoral strains during normal activity in comparison with finite element analysis. The musculoskeletal model of one individual, the finite-element model of the right femur, and experimental force and motion data for normal walking, fast walking, stair ascent, stair descent, and rising from a chair were obtained from a previous study. Equivalent Von Mises strain was calculated for 1000 frames uniformly distributed across activities. MLR surrogate models were generated using training sets of 50, 100, 200 and 300 samples. The finite-element and MLR analyses were compared using linear regression. The Root Mean Square Error (RMSE) and the 95th percentile of the strain error distribution were used as indicators of average and peak error. The MLR model trained using 200 samples (RMSE < 108 µε; peak error < 228 µε) was used as a reference. The finite-element method required 66 s per frame on a standard desktop computer. The MLR model required 0.1 s per frame plus 1848 s of training time. RMSE ranged from 1.2% to 1.3% while peak error ranged from 2.2% to 3.6% of the maximum micro-strain (5020 µε). Performance within an activity was lower during early and late stance, with RMSE of 4.1% and peak error of 8.6% of the maximum computed micro-strain. These results show that MLR surrogate models may be used to rapidly and accurately estimate strain fields in long bones during daily physical activity.
Collapse
Affiliation(s)
- Hamed Ziaeipoor
- Medical Device Research Institute, College of Science and Engineering, Flinders University, Clovelly Park, Tonsley, Adelaide, SA, Australia.
| | - Saulo Martelli
- Medical Device Research Institute, College of Science and Engineering, Flinders University, Clovelly Park, Tonsley, Adelaide, SA, Australia; NorthWest Academic Centre, The University of Melbourne, St Albans, VIC, Australia
| | - Marcus Pandy
- Department of Mechanical Engineering, University of Melbourne, Parkville, VIC, Australia
| | - Mark Taylor
- Medical Device Research Institute, College of Science and Engineering, Flinders University, Clovelly Park, Tonsley, Adelaide, SA, Australia
| |
Collapse
|