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Shi B, Barzan M, Nasseri A, Maharaj JN, Diamond LE, Saxby DJ. Automatic generation of knee kinematic models from medical imaging. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 256:108370. [PMID: 39180912 DOI: 10.1016/j.cmpb.2024.108370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 08/07/2024] [Accepted: 08/08/2024] [Indexed: 08/27/2024]
Abstract
BACKGROUND AND OBJECTIVE Three-dimensional spatial mechanisms have been used to accurately predict passive knee kinematics, and have shown potential to be used in optimized multibody kinematic models. Such multi-body models are anatomically consistent and can accurately predict passive knee kinematics, but require extensive medical image processing and thus are not widely adopted. This study aimed to automate the generation of kinematic models of tibiofemoral (TFJ) and patellofemoral (PFJ) joints from segmented magnetic resonance imaging (MRI) and compare them against a corresponding manual pipeline. METHODS From segmented MRI of eight healthy participants (four females; aged 14.0 ± 2.6 years), geometric parameters (i.e., articular surfaces, ligament attachments) were determined both automatically and manually, and then assembled into TFJ and PFJ kinematic models to predict passive kinematics. The TFJ model was a six-link mechanism with deformable ligamentous constraints, whereas PFJ was a modified hinge. The ligament length changes through TFJ flexion were prescribed to literature strain profile. The geometric parameters were optimized to ensure physiological kinematic predictions through a Multiple Objective Particle Swarm Optimization. RESULTS Geometric parameters showed strong agreement between automatic and manual pipelines (median error of 2.8 mm for anatomical landmarks and 1.5 mm for ligament lengths). Predicted TFJ and PFJ kinematics from the two pipelines were not statistically different, except for tibial superior/inferior translation near terminal TFJ extension. The TFJ kinematics predicted from the automatic pipeline had mean errors of 3.6° and 12.4° for adduction/abduction and internal/external rotation, respectively, and <7 mm mean translational error compared to the manual pipeline. Predicted PFJ had <9° mean rotational errors and <6 mm mean translational errors. CONCLUSIONS The automatic pipeline developed and presented here can predict passive knee kinematics comparable to a manual pipeline, but removes laborious manual processing and provides a systematic approach to model creation.
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Affiliation(s)
- Beichen Shi
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Gold Coast campus Griffith University QLD 4222, Australia; School of Health Sciences and Social Work, Gold Coast campus Griffith University, Parklands Dr Southport QLD 4222, Australia.
| | - Martina Barzan
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Gold Coast campus Griffith University QLD 4222, Australia; School of Health Sciences and Social Work, Gold Coast campus Griffith University, Parklands Dr Southport QLD 4222, Australia
| | - Azadeh Nasseri
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Gold Coast campus Griffith University QLD 4222, Australia; School of Health Sciences and Social Work, Gold Coast campus Griffith University, Parklands Dr Southport QLD 4222, Australia
| | - Jayishni N Maharaj
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Gold Coast campus Griffith University QLD 4222, Australia; School of Health Sciences and Social Work, Gold Coast campus Griffith University, Parklands Dr Southport QLD 4222, Australia
| | - Laura E Diamond
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Gold Coast campus Griffith University QLD 4222, Australia; School of Health Sciences and Social Work, Gold Coast campus Griffith University, Parklands Dr Southport QLD 4222, Australia
| | - David J Saxby
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Gold Coast campus Griffith University QLD 4222, Australia; School of Health Sciences and Social Work, Gold Coast campus Griffith University, Parklands Dr Southport QLD 4222, Australia
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Rasheed B, Bjelland Ø, Dalen AF, Schaarschmidt U, Schaathun HG, Pedersen MD, Steinert M, Bye RT. Intraoperative identification of patient-specific elastic modulus of the meniscus during arthroscopy. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108269. [PMID: 38861877 DOI: 10.1016/j.cmpb.2024.108269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 04/30/2024] [Accepted: 05/31/2024] [Indexed: 06/13/2024]
Abstract
BACKGROUND AND OBJECTIVE Degenerative meniscus tissue has been associated with a lower elastic modulus and can lead to the development of arthrosis. Safe intraoperative measurement of in vivo elastic modulus of the human meniscus could contribute to a better understanding of meniscus health, and for developing surgical simulators where novice surgeons can learn to distinguish healthy from degenerative meniscus tissue. Such measurement can also support intraoperative decision-making by providing a quantitative measure of the meniscus health condition. The objective of this study is to demonstrate a method for intraoperative identification of meniscus elastic modulus during arthroscopic probing using an adaptive observer method. METHODS Ex vivo arthroscopic examinations were performed on five cadaveric knees to estimate the elastic modulus of the anterior, mid-body, and posterior regions of lateral and medial menisci. Real-time intraoperative force-displacement data was obtained and utilized for modulus estimation through an adaptive observer method. For the validation of arthroscopic elastic moduli, an inverse parameter identification approach using optimization, based on biomechanical indentation tests and finite element analyses, was employed. Experimental force-displacement data in various anatomical locations were measured through indentation. An iterative optimization algorithm was employed to optimize elastic moduli and Poisson's ratios by comparing experimental force values at maximum displacement with the corresponding force values from linear elastic region-specific finite element models. Finally, the estimated elastic modulus values obtained from ex vivo arthroscopy were compared against optimized values using a paired t-test. RESULTS The elastic moduli obtained from ex vivo arthroscopy and optimization showcased subject specificity in material properties. Additionally, the results emphasized anatomical and regional specificity within the menisci. The anterior region of the medial menisci exhibited the highest elastic modulus among the anatomical locations studied (9.97±3.20MPa from arthroscopy and 5.05±1.97MPa from finite element-based inverse parameter identification). The paired t-test results indicated no statistically significant difference between the elastic moduli obtained from arthroscopy and inverse parameter identification, suggesting the feasibility of stiffness estimation using arthroscopic examination. CONCLUSIONS This study has demonstrated the feasibility of intraoperative identification of patient-specific elastic modulus for meniscus tissue during arthroscopy.
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Affiliation(s)
- Bismi Rasheed
- Cyber-Physical Systems Laboratory, Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Å lesund, 6025, Norway; Å lesund Biomechanics Lab, Department of Research and Innovation, Møre and Romsdal Hospital Trust, Å lesund, 6017, Norway.
| | - Øystein Bjelland
- Cyber-Physical Systems Laboratory, Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Å lesund, 6025, Norway; Å lesund Biomechanics Lab, Department of Research and Innovation, Møre and Romsdal Hospital Trust, Å lesund, 6017, Norway
| | - Andreas F Dalen
- Å lesund Biomechanics Lab, Department of Research and Innovation, Møre and Romsdal Hospital Trust, Å lesund, 6017, Norway; Department of Orthopaedic Surgery, Møre and Romsdal Hospital Trust, Å lesund, 6017, Norway
| | - Ute Schaarschmidt
- Cyber-Physical Systems Laboratory, Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Å lesund, 6025, Norway
| | - Hans Georg Schaathun
- Cyber-Physical Systems Laboratory, Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Å lesund, 6025, Norway
| | - Morten D Pedersen
- Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, 7491, Norway
| | - Martin Steinert
- Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, 7491, Norway
| | - Robin T Bye
- Cyber-Physical Systems Laboratory, Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Å lesund, 6025, Norway
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Sun C, Gao H, Wu S, Lu Q, Wang Y, Cai X. Evaluation of the consistency of the MRI- based AI segmentation cartilage model using the natural tibial plateau cartilage. J Orthop Surg Res 2024; 19:247. [PMID: 38632625 PMCID: PMC11025227 DOI: 10.1186/s13018-024-04680-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 03/15/2024] [Indexed: 04/19/2024] Open
Abstract
OBJECTIVE The study aims to evaluate the accuracy of an MRI-based artificial intelligence (AI) segmentation cartilage model by comparing it to the natural tibial plateau cartilage. METHODS This study included 33 patients (41 knees) with severe knee osteoarthritis scheduled to undergo total knee arthroplasty (TKA). All patients had a thin-section MRI before TKA. Our study is mainly divided into two parts: (i) In order to evaluate the MRI-based AI segmentation cartilage model's 2D accuracy, the natural tibial plateau was used as gold standard. The MRI-based AI segmentation cartilage model and the natural tibial plateau were represented in binary visualization (black and white) simulated photographed images by the application of Simulation Photography Technology. Both simulated photographed images were compared to evaluate the 2D Dice similarity coefficients (DSC). (ii) In order to evaluate the MRI-based AI segmentation cartilage model's 3D accuracy. Hand-crafted cartilage model based on knee CT was established. We used these hand-crafted CT-based knee cartilage model as gold standard to evaluate 2D and 3D consistency of between the MRI-based AI segmentation cartilage model and hand-crafted CT-based cartilage model. 3D registration technology was used for both models. Correlations between the MRI-based AI knee cartilage model and CT-based knee cartilage model were also assessed with the Pearson correlation coefficient. RESULTS The AI segmentation cartilage model produced reasonably high two-dimensional DSC. The average 2D DSC between MRI-based AI cartilage model and the tibial plateau cartilage is 0.83. The average 2D DSC between the AI segmentation cartilage model and the CT-based cartilage model is 0.82. As for 3D consistency, the average 3D DSC between MRI-based AI cartilage model and CT-based cartilage model is 0.52. However, the quantification of cartilage segmentation with the AI and CT-based models showed excellent correlation (r = 0.725; P values < 0.05). CONCLUSION Our study demonstrated that our MRI-based AI cartilage model can reliably extract morphologic features such as cartilage shape and defect location of the tibial plateau cartilage. This approach could potentially benefit clinical practices such as diagnosing osteoarthritis. However, in terms of cartilage thickness and three-dimensional accuracy, MRI-based AI cartilage model underestimate the actual cartilage volume. The previous AI verification methods may not be completely accurate and should be verified with natural cartilage images. Combining multiple verification methods will improve the accuracy of the AI model.
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Affiliation(s)
- Changjiao Sun
- Joint Diseases Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, No. 168 Litang Road, Dongxiaokou Town, Changping District, Beijing, 102218, China
| | - Hong Gao
- Beijing MEDERA Medical Group, Beijing, 102200, China
| | - Sha Wu
- Joint Diseases Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, No. 168 Litang Road, Dongxiaokou Town, Changping District, Beijing, 102218, China
- Beijing MEDERA Medical Group, Beijing, 102200, China
| | - Qian Lu
- Nuctech Company Limited, Beijing, 100083, China
| | - Yakui Wang
- Radiology Department, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Xu Cai
- Joint Diseases Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, No. 168 Litang Road, Dongxiaokou Town, Changping District, Beijing, 102218, China.
- Beijing MEDERA Medical Group, Beijing, 102200, China.
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Andriollo L, Picchi A, Sangaletti R, Perticarini L, Rossi SMP, Logroscino G, Benazzo F. The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives. Healthcare (Basel) 2024; 12:300. [PMID: 38338185 PMCID: PMC10855330 DOI: 10.3390/healthcare12030300] [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: 12/31/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
The remarkable progress in data aggregation and deep learning algorithms has positioned artificial intelligence (AI) and machine learning (ML) to revolutionize the field of medicine. AI is becoming more and more prevalent in the healthcare sector, and its impact on orthopedic surgery is already evident in several fields. This review aims to examine the literature that explores the comprehensive clinical relevance of AI-based tools utilized before, during, and after anterior cruciate ligament (ACL) reconstruction. The review focuses on current clinical applications and future prospects in preoperative management, encompassing risk prediction and diagnostics; intraoperative tools, specifically navigation, identifying complex anatomic landmarks during surgery; and postoperative applications in terms of postoperative care and rehabilitation. Additionally, AI tools in educational and training settings are presented. Orthopedic surgeons are showing a growing interest in AI, as evidenced by the applications discussed in this review, particularly those related to ACL injury. The exponential increase in studies on AI tools applicable to the management of ACL tears promises a significant future impact in its clinical application, with growing attention from orthopedic surgeons.
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Affiliation(s)
- Luca Andriollo
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
- Department of Orthopedics, Catholic University of the Sacred Heart, 00168 Rome, Italy
| | - Aurelio Picchi
- Unit of Orthopedics, Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy; (A.P.); (G.L.)
| | - Rudy Sangaletti
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
| | - Loris Perticarini
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
| | - Stefano Marco Paolo Rossi
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
| | - Giandomenico Logroscino
- Unit of Orthopedics, Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy; (A.P.); (G.L.)
| | - Francesco Benazzo
- Robotic Prosthetic Surgery Unit—Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (R.S.); (L.P.); (S.M.P.R.); (F.B.)
- Biomedical Sciences Area, IUSS University School for Advanced Studies, 27100 Pavia, Italy
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Ehmig J, Engel G, Lotz J, Lehmann W, Taheri S, Schilling AF, Seif Amir Hosseini A, Panahi B. MR-Imaging in Osteoarthritis: Current Standard of Practice and Future Outlook. Diagnostics (Basel) 2023; 13:2586. [PMID: 37568949 PMCID: PMC10417111 DOI: 10.3390/diagnostics13152586] [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: 06/28/2023] [Revised: 07/30/2023] [Accepted: 08/01/2023] [Indexed: 08/13/2023] Open
Abstract
Osteoarthritis (OA) is a common degenerative joint disease that affects millions of people worldwide. Magnetic resonance imaging (MRI) has emerged as a powerful tool for the evaluation and monitoring of OA due to its ability to visualize soft tissues and bone with high resolution. This review aims to provide an overview of the current state of MRI in OA, with a special focus on the knee, including protocol recommendations for clinical and research settings. Furthermore, new developments in the field of musculoskeletal MRI are highlighted in this review. These include compositional MRI techniques, such as T2 mapping and T1rho imaging, which can provide additional important information about the biochemical composition of cartilage and other joint tissues. In addition, this review discusses semiquantitative joint assessment based on MRI findings, which is a widely used method for evaluating OA severity and progression in the knee. We analyze the most common scoring methods and discuss potential benefits. Techniques to reduce acquisition times and the potential impact of deep learning in MR imaging for OA are also discussed, as these technological advances may impact clinical routine in the future.
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Affiliation(s)
- Jonathan Ehmig
- Institute of Diagnostic and Interventional Radiology, University Medical Center Göttingen, 37075 Göttingen, Germany; (J.E.); (G.E.)
| | - Günther Engel
- Institute of Diagnostic and Interventional Radiology, University Medical Center Göttingen, 37075 Göttingen, Germany; (J.E.); (G.E.)
| | - Joachim Lotz
- Institute of Diagnostic and Interventional Radiology, University Medical Center Göttingen, 37075 Göttingen, Germany; (J.E.); (G.E.)
| | - Wolfgang Lehmann
- Clinic of Trauma, Orthopedics and Reconstructive Surgery, Georg-August-University of Göttingen, 37075 Göttingen, Germany
| | - Shahed Taheri
- Clinic of Trauma, Orthopedics and Reconstructive Surgery, Georg-August-University of Göttingen, 37075 Göttingen, Germany
| | - Arndt F. Schilling
- Clinic of Trauma, Orthopedics and Reconstructive Surgery, Georg-August-University of Göttingen, 37075 Göttingen, Germany
| | - Ali Seif Amir Hosseini
- Institute of Diagnostic and Interventional Radiology, University Medical Center Göttingen, 37075 Göttingen, Germany; (J.E.); (G.E.)
| | - Babak Panahi
- Institute of Diagnostic and Interventional Radiology, University Medical Center Göttingen, 37075 Göttingen, Germany; (J.E.); (G.E.)
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Matijaš T, Pinjuh A, Dolić K, Radović D, Galić T, Božić Štulić D, Mihanović F. Improving the Age Estimation Efficiency by Calculation of the Area Ratio Index Using Semi-Automatic Segmentation of Knee MRI Images. Biomedicines 2023; 11:2046. [PMID: 37509685 PMCID: PMC10377215 DOI: 10.3390/biomedicines11072046] [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: 06/20/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023] Open
Abstract
The knee is an anatomical structure that can provide a great deal of data for research on age estimation. The aim of this study was to evaluate and apply a method for semi-automatic measurements of the area under the growth plate closure of the femur distal epiphysis and the growth plate closure itself on the 2D coronary slices using T2 weighted images (T2WI) generated on magnetic resonance (MRI) devices of different technical and technological characteristics. After the semi-automatic segmentation of the femur distal epiphysis under the growth plate closure and the growth plate closure itself, the areas of the measured closures were calculated using MATLAB version: 9.12. (R2022a), MathWorks Inc., Natick, MA, USA, for each individual coronal slice. The area ratio index (ARI) was calculated as the ratio between the area under the growth plate closure of the femur distal epiphysis and the growth plate closure itself. The study sample consisted of 27 female and 23 male Caucasian participants aged 10 to 26 years. A total of 339 T2WI images were used for ARI calculations. There was a positive correlation between chronological age and the average ARI measured by three independent observers (r = 0.8280, p < 0.001). Multiple regression analysis did not show any significant impact of the technical and technological characteristics of the MRI devices on ARI. The results of this study showed that ARI could serve as a useful tool for age estimation using knee MRI as well as for the further development of artificial intelligence (AI) applications.
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Affiliation(s)
- Tatjana Matijaš
- University Department of Health Studies, University of Split, 21000 Split, Croatia
| | - Ana Pinjuh
- Faculty of Mechanical Engineering, Computing and Electrical Engineering, University of Mostar, 88000 Mostar, Bosnia and Herzegovina
| | - Krešimir Dolić
- University Department of Health Studies, University of Split, 21000 Split, Croatia
- Department of Diagnostic and Interventional Radiology, University Hospital of Split, 21000 Split, Croatia
- School of Medicine, University of Split, 21000 Split, Croatia
| | - Darijo Radović
- University Department of Health Studies, University of Split, 21000 Split, Croatia
- Polyclinic Medikol, 21000 Split, Croatia
| | - Tea Galić
- Department of Prosthodontics, Study of Dental Medicine, School of Medicine, University of Split, 21000 Split, Croatia
| | - Dunja Božić Štulić
- Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (FESB), University of Split, 21000 Split, Croatia
| | - Frane Mihanović
- University Department of Health Studies, University of Split, 21000 Split, Croatia
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