1
|
Surendran T, Park LK, Lauber MV, Cha B, Jhun RS, Capellini TD, Kumar D, Felson DT, Kolachalama VB. Survival analysis on subchondral bone length for total knee replacement. Skeletal Radiol 2024; 53:1541-1552. [PMID: 38388702 PMCID: PMC11194148 DOI: 10.1007/s00256-024-04627-1] [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: 10/26/2023] [Revised: 02/01/2024] [Accepted: 02/15/2024] [Indexed: 02/24/2024]
Abstract
OBJECTIVE Use subchondral bone length (SBL), a new MRI-derived measure that reflects the extent of cartilage loss and bone flattening, to predict the risk of progression to total knee replacement (TKR). METHODS We employed baseline MRI data from the Osteoarthritis Initiative (OAI), focusing on 760 men and 1214 women with bone marrow lesions (BMLs) and joint space narrowing (JSN) scores, to predict the progression to TKR. To minimize bias from analyzing both knees of a participant, only the knee with a higher Kellgren-Lawrence (KL) grade was considered, given its greater potential need for TKR. We utilized the Kaplan-Meier survival curves and Cox proportional hazards models, incorporating raw and normalized values of SBL, JSN, and BML as predictors. The study included subgroup analyses for different demographics and clinical characteristics, using models for raw and normalized SBL (merged, femoral, tibial), BML (merged, femoral, tibial), and JSN (medial and lateral compartments). Model performance was evaluated using the time-dependent area under the curve (AUC), Brier score, and Concordance index to gauge accuracy, calibration, and discriminatory power. Knee joint and region-level analyses were conducted to determine the effectiveness of SBL, JSN, and BML in predicting TKR risk. RESULTS The SBL model, incorporating data from both the femur and tibia, demonstrated a predictive capacity for TKR that closely matched the performance of the BML score and the JSN grade. The Concordance index of the SBL model was 0.764, closely mirroring the BML's 0.759 and slightly below JSN's 0.788. The Brier score for the SBL model stood at 0.069, showing comparability with BML's 0.073 and a minor difference from JSN's 0.067. Regarding the AUC, the SBL model achieved 0.803, nearly identical to BML's 0.802 and slightly lower than JSN's 0.827. CONCLUSION SBL's capacity to predict the risk of progression to TKR highlights its potential as an effective imaging biomarker for knee osteoarthritis.
Collapse
Affiliation(s)
- Tejus Surendran
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA
| | - Lisa K Park
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA
| | - Meagan V Lauber
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA
| | - Baekdong Cha
- Sargent College, Boston University, Boston, MA, USA
| | - Ray S Jhun
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA
| | - Terence D Capellini
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Deepak Kumar
- Sargent College, Boston University, Boston, MA, USA
| | - David T Felson
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA
| | - Vijaya B Kolachalama
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA.
- Department of Computer Science and Faculty of Computing & Data Sciences, Boston University, Boston, MA, 02215, USA.
| |
Collapse
|
2
|
Longo UG, De Salvatore S, Valente F, Villa Corta M, Violante B, Samuelsson K. Artificial intelligence in total and unicompartmental knee arthroplasty. BMC Musculoskelet Disord 2024; 25:571. [PMID: 39034416 PMCID: PMC11265144 DOI: 10.1186/s12891-024-07516-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 05/13/2024] [Indexed: 07/23/2024] Open
Abstract
The application of Artificial intelligence (AI) and machine learning (ML) tools in total (TKA) and unicompartmental knee arthroplasty (UKA) emerges with the potential to improve patient-centered decision-making and outcome prediction in orthopedics, as ML algorithms can generate patient-specific risk models. This review aims to evaluate the potential of the application of AI/ML models in the prediction of TKA outcomes and the identification of populations at risk.An extensive search in the following databases: MEDLINE, Scopus, Cinahl, Google Scholar, and EMBASE was conducted using the PIOS approach to formulate the research question. The PRISMA guideline was used for reporting the evidence of the data extracted. A modified eight-item MINORS checklist was employed for the quality assessment. The databases were screened from the inception to June 2022.Forty-four out of the 542 initially selected articles were eligible for the data analysis; 5 further articles were identified and added to the review from the PUBMED database, for a total of 49 articles included. A total of 2,595,780 patients were identified, with an overall average age of the patients of 70.2 years ± 7.9 years old. The five most common AI/ML models identified in the selected articles were: RF, in 38.77% of studies; GBM, in 36.73% of studies; ANN in 34.7% of articles; LR, in 32.65%; SVM in 26.53% of articles.This systematic review evaluated the possible uses of AI/ML models in TKA, highlighting their potential to lead to more accurate predictions, less time-consuming data processing, and improved decision-making, all while minimizing user input bias to provide risk-based patient-specific care.
Collapse
Affiliation(s)
- Umile Giuseppe Longo
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, Rome, 200 - 00128, Italy.
- Department of Medicine and Surgery, Research Unit of Orthopaedic and Trauma Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, Rome, 21 - 00128, Italy.
| | - Sergio De Salvatore
- IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy
- Orthopedic Unit, Department of Surgery, Bambino Gesù Children's Hospital, Rome, Italy
| | - Federica Valente
- Department of Medicine and Surgery, Research Unit of Orthopaedic and Trauma Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, Rome, 21 - 00128, Italy
| | - Mariajose Villa Corta
- Department of Medicine and Surgery, Research Unit of Orthopaedic and Trauma Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, Rome, 21 - 00128, Italy
| | - Bruno Violante
- Orthopaedic Department, Clinical Institute Sant'Ambrogio, IRCCS - Galeazzi, Milan, Italy
| | - Kristian Samuelsson
- Department of Medicine and Surgery, Research Unit of Orthopaedic and Trauma Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, Rome, 21 - 00128, Italy
| |
Collapse
|
3
|
Salis Z, Driban JB, McAlindon TE. Predicting the onset of end-stage knee osteoarthritis over two- and five-years using machine learning. Semin Arthritis Rheum 2024; 66:152433. [PMID: 38513411 DOI: 10.1016/j.semarthrit.2024.152433] [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: 10/24/2023] [Revised: 03/01/2024] [Accepted: 03/11/2024] [Indexed: 03/23/2024]
Abstract
OBJECTIVE Identifying participants who will progress to advanced stage in knee osteoarthritis (KOA) trials remains a significant challenge. Current tools, relying on total knee replacements (TKR), fall short in reliability due to the extraneous factors influencing TKR decisions. Acknowledging these limitations, our study identifies a critical need for a more robust metric to assess severe KOA. The end-stage KOA (esKOA) measure, which combines symptomatic and radiographic criteria, serves as a solid indicator. To enhance future trials that use esKOA as an endpoint, our study focuses on developing and validating a machine-learning tool to identify individuals likely to develop esKOA within 2 to 5 years. DESIGN Utilizing the Osteoarthritis Initiative (OAI) data, we trained models on 3,114 participants and validated them with 606 participants for the right knee, and similarly for the left knee, with external validation from the Multicentre Osteoarthritis Study (MOST) involving 1,602 participants. We aimed to predict esKOA onset at 2-to-2.5 years and 4-to-5 years, defining esKOA by severe radiographic KOA with moderate/severe symptoms or mild/moderate radiographic KOA with persistent/intense symptoms. Our analysis considered 51 candidate predictors, including demographics, clinical history, physical examination, and X-ray evaluations. An online tool predicting esKOA progression, based on models with ten and nine predictors for the right and left knees, respectively, was developed. RESULTS External validation (MOST) for the right knee at 2.5 years yielded an Area Under Curve (AUC) of 0.847 (95 % CI 0.811 to 0.882), and at 5 years, 0.853 (95 % CI 0.823 to 0.881); for the left knee at 2.5 years, AUC was 0.824 (95 % CI 0.782 to 0.857), and at 5 years, 0.807 (95 % CI 0.768 to 0.843). Models with fewer predictors demonstrated comparable performance. The online tool is available at: https://eskoa.shinyapps.io/webapp/. CONCLUSION Our study unveils a robust, externally validated machine learning tool proficient in predicting the onset of esKOA over the next 2 to 5 years. Our tool can lead to more efficient KOA trials.
Collapse
Affiliation(s)
- Zubeyir Salis
- Division of Rheumatology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland; School of Human Sciences, the University of Western Australia, Perth, WA, Australia; Centre for Big Data Research in Health, the University of New South Wales, Kensington, NSW, Australia.
| | - Jeffrey B Driban
- UMass Chan Medical School, Department of Population and Quantitative Health Sciences, Worcester, MA, USA
| | - Timothy E McAlindon
- Division of Rheumatology, Allergy, and Immunology; Tufts Medical Center, Boston, MA, USA
| |
Collapse
|
4
|
Li H, Chan L, Chan P, Wen C. An interpretable knee replacement risk assessment system for osteoarthritis patients. OSTEOARTHRITIS AND CARTILAGE OPEN 2024; 6:100440. [PMID: 38385105 PMCID: PMC10878788 DOI: 10.1016/j.ocarto.2024.100440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/07/2024] [Accepted: 02/12/2024] [Indexed: 02/23/2024] Open
Abstract
Objective Knee osteoarthritis (OA) is a complex disease with heterogeneous representations. Although it is modifiable to prevention and early treatment, there still lacks a reliable and accurate prognostic tool. Hence, we aim to develop a quantitative and self-administrable knee replacement (KR) risk stratification system for knee osteoarthritis (KOA) patients with clinical features. Method A total of 14 baseline features were extracted from 9592 cases in the Osteoarthritis Initiative (OAI) cohort. A survival model was constructed using the Random Survival Forests algorithm. The prediction performance was evaluated with the concordance index (C-index) and average receiver operating characteristic curve (AUC). A three-class KR risk stratification system was built to differentiate three distinct KR-free survival groups. Thereafter, Shapley Additive Explanations (SHAP) was introduced for model explanation. Results KR incidence was accurately predicted by the model with a C-index of 0.770 (±0.0215) and an average AUC of 0.807 (±0.0181) with 14 clinical features. Three distinct survival groups were observed from the ten-point KR risk stratification system with a four-year KR rate of 0.79%, 5.78%, and 16.2% from the low, medium, and high-risk groups respectively. KR is mainly caused by pain medication use, age, surgery history, diabetes, and a high body mass index, as revealed by SHAP. Conclusion A self-administrable and interpretable KR survival model was developed, underscoring a KR risk scoring system to stratify KOA patients. It will encourage regular self-assessments within the community and facilitate personalised healthcare for both primary and secondary prevention of KOA.
Collapse
Affiliation(s)
- H.H.T. Li
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong
- Department of Prosthetics and Orthotics, Tuen Mun Hospital, Hong Kong
| | - L.C. Chan
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong
| | - P.K. Chan
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong
| | - C. Wen
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong
- Research Institute of Smart Ageing, The Hong Kong Polytechnic University, Hong Kong
| |
Collapse
|
5
|
Roemer FW, Jarraya M, Hayashi D, Crema MD, Haugen IK, Hunter DJ, Guermazi A. A perspective on the evolution of semi-quantitative MRI assessment of osteoarthritis: Past, present and future. Osteoarthritis Cartilage 2024; 32:460-472. [PMID: 38211810 DOI: 10.1016/j.joca.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 12/15/2023] [Accepted: 01/04/2024] [Indexed: 01/13/2024]
Abstract
OBJECTIVE This perspective describes the evolution of semi-quantitative (SQ) magnetic resonance imaging (MRI) in characterizing structural tissue pathologies in osteoarthritis (OA) imaging research over the last 30 years. METHODS Authors selected representative articles from a PubMed search to illustrate key steps in SQ MRI development, validation, and application. Topics include main scoring systems, reading techniques, responsiveness, reliability, technical considerations, and potential impact of artificial intelligence (AI). RESULTS Based on original research published between 1993 and 2023, this article introduces available scoring systems, including but not limited to Whole-Organ Magnetic Resonance Imaging Score (WORMS) as the first system for whole-organ assessment of the knee and the now commonly used MRI Osteoarthritis Knee Score (MOAKS) instrument. Specific systems for distinct OA subtypes or applications have been developed as well as MRI scoring instruments for other joints such as the hip, the fingers or thumb base. SQ assessment has proven to be valid, reliable, and responsive, aiding OA investigators in understanding the natural history of the disease and helping to detect response to treatment. AI may aid phenotypic characterization in the future. SQ MRI assessment's role is increasing in eligibility and safety evaluation in knee OA clinical trials. CONCLUSIONS Evidence supports the validity, reliability, and responsiveness of SQ MRI assessment in understanding structural aspects of disease onset and progression. SQ scoring has helped explain associations between structural tissue damage and clinical manifestations, as well as disease progression. While AI may support human readers to more efficiently perform SQ assessment in the future, its current application in clinical trials still requires validation and regulatory approval.
Collapse
Affiliation(s)
- Frank W Roemer
- Universitätsklinikum Erlangen & Friedrich Alexander Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA.
| | - Mohamed Jarraya
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Daichi Hayashi
- Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA; Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Michel D Crema
- Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA; Institute of Sports Imaging, French National Institute of Sports (INSEP), Paris, France
| | - Ida K Haugen
- Center for Treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, Norway
| | - David J Hunter
- Department of Rheumatology, Royal North Shore Hospital and Sydney Musculoskeletal Health, Kolling Institute, University of Sydney, St. Leonards, NSW, Australia
| | - Ali Guermazi
- Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA; Boston VA Healthcare System, West Roxbury, MA, USA
| |
Collapse
|
6
|
Subha B, Jeyakumar V, Deepa SN. Gaussian Aquila optimizer based dual convolutional neural networks for identification and grading of osteoarthritis using knee joint images. Sci Rep 2024; 14:7225. [PMID: 38538646 PMCID: PMC11349978 DOI: 10.1038/s41598-024-57002-4] [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: 11/09/2023] [Accepted: 03/13/2024] [Indexed: 07/03/2024] Open
Abstract
Degenerative musculoskeletal disease known as Osteoarthritis (OA) causes serious pain and abnormalities for humans and on detecting at an early stage, timely treatment shall be initiated to the patients at the earliest to overcome this pain. In this research study, X-ray images are captured from the humans and the proposed Gaussian Aquila Optimizer based Dual Convolutional Neural Networks is employed for detecting and classifying the osteoarthritis patients. The new Gaussian Aquila Optimizer (GAO) is devised to include Gaussian mutation at the exploitation stage of Aquila optimizer, which results in attaining the best global optimal value. Novel Dual Convolutional Neural Network (DCNN) is devised to balance the convolutional layers in each convolutional model and the weight and bias parameters of the new DCNN model are optimized using the developed GAO. The novelty of the proposed work lies in evolving a new optimizer, Gaussian Aquila Optimizer for parameter optimization of the devised DCNN model and the new DCNN model is structured to minimize the computational burden incurred in spite of it possessing dual layers but with minimal number of layers. The knee dataset comprises of total 2283 knee images, out of which 1267 are normal knee images and 1016 are the osteoarthritis images with an image of 512 × 512-pixel width and height respectively. The proposed novel GAO-DCNN system attains the classification results of 98.25% of sensitivity, 98.93% of specificity and 98.77% of classification accuracy for abnormal knee case-knee joint images. Experimental simulation results carried out confirms the superiority of the developed hybrid GAO-DCNN over the existing deep learning neural models form previous literature studies.
Collapse
Affiliation(s)
- B Subha
- Department of Biomedical Engineering, PSNA College of Engineering and Technology, Dindigul, India.
| | - Vijay Jeyakumar
- Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India
| | - S N Deepa
- National Institute of Technology Calicut, NITC Campus Post, Kozhikode, Kerala, India
| |
Collapse
|
7
|
Arbeeva L, Minnig MC, Yates KA, Nelson AE. Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes. Curr Rheumatol Rep 2023; 25:213-225. [PMID: 37561315 PMCID: PMC10592147 DOI: 10.1007/s11926-023-01114-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/27/2023] [Indexed: 08/11/2023]
Abstract
PURPOSE OF REVIEW Osteoarthritis (OA) is a complex heterogeneous disease with no effective treatments. Artificial intelligence (AI) and its subfield machine learning (ML) can be applied to data from different sources to (1) assist clinicians and patients in decision making, based on machine-learned evidence, and (2) improve our understanding of pathophysiology and mechanisms underlying OA, providing new insights into disease management and prevention. The purpose of this review is to improve the ability of clinicians and OA researchers to understand the strengths and limitations of AI/ML methods in applications to OA research. RECENT FINDINGS AI/ML can assist clinicians by prediction of OA incidence and progression and by providing tailored personalized treatment. These methods allow using multidimensional multi-source data to understand the nature of OA, to identify different OA phenotypes, and for biomarker discovery. We described the recent implementations of AI/ML in OA research and highlighted potential future directions and associated challenges.
Collapse
Affiliation(s)
- Liubov Arbeeva
- Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, 3300 Doc J. Thurston Bldg, Campus Box #7280, Chapel Hill, NC, 27599-7280, USA
| | - Mary C Minnig
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Katherine A Yates
- Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, 3300 Doc J. Thurston Bldg, Campus Box #7280, Chapel Hill, NC, 27599-7280, USA
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Amanda E Nelson
- Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, 3300 Doc J. Thurston Bldg, Campus Box #7280, Chapel Hill, NC, 27599-7280, USA.
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| |
Collapse
|
8
|
Driban JB, Lu B, Flechsenhar K, Lo GH, McAlindon TE. The Prognostic Potential of End-Stage Knee Osteoarthritis and Its Components to Predict Knee Replacement: Data From the Osteoarthritis Initiative. J Rheumatol 2023; 50:1481-1487. [PMID: 37657799 PMCID: PMC10840653 DOI: 10.3899/jrheum.2023-0017] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/09/2023] [Indexed: 09/03/2023]
Abstract
OBJECTIVE We aimed to determine how 2 definitions of end-stage knee osteoarthritis (esKOA) and each component (knee symptoms, persistent knee pain, radiographic severity, and presence of limited mobility or instability) related to future knee replacement (KR). METHODS We performed knee-based analyses of Osteoarthritis Initiative data from baseline to the first 4 annual follow-up visits, and data on KR from baseline until the fifth yearly contact. We calculated a base model using common risk factors for KR in logistic regression models with generalized estimating equations. We assessed model performance with area under the receiver-operating characteristic curve (AUC) and Hosmer-Lemeshow test. We then added esKOA or each component from the visit (< 12 months) before a KR and change in the year before a KR. We calculated the net reclassification improvement (NRI) index and the integrated discrimination improvement (IDI) index. RESULTS Our sample was mostly female (58%), ≥ 65 years old, White (82%), and without radiographic knee osteoarthritis (50%). At the visit before a KR, Kellgren-Lawrence (KL) grades (ordinal scale; AUC 0.88, NRI 1.12, IDI 0.11), the alternate definition of esKOA (AUC 0.84, NRI 1.16, IDI 0.12), and a model with every component of esKOA (AUC 0.91, NRI 1.30, IDI 0.17) had the best performances. During the year before a KR, change in esKOA status (alternate definition) had the best performance (AUC 0.86, NRI 1.24, IDI 0.12). CONCLUSION Radiographic severity may be a screening tool to find a knee that will likely receive a KR. However, esKOA may be an ideal outcome in clinical trials because a change in esKOA state predicts future KR.
Collapse
Affiliation(s)
- Jeffrey B Driban
- J.B. Driban, PhD, T.E. McAlindon, MD, MPH, Division of Rheumatology, Allergy, and Immunology, Tufts Medical Center, Boston, Massachusetts, USA;
| | - Bing Lu
- B. Lu, MD, DrPH, Department of Public Health Sciences, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - Klaus Flechsenhar
- K. Flechsenhar, MD, Immunology and Inflammation Therapeutic Area, Type 1/17 Immunology Cluster, Sanofi, Frankfurt am Main, Germany
| | - Grace H Lo
- G.H. Lo, MD, MSc, Medical Care Line and Research Care Line, Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VAMC, and Section of Immunology, Allergy, and Rheumatology, Baylor College of Medicine, Houston, Texas, USA
| | - Timothy E McAlindon
- J.B. Driban, PhD, T.E. McAlindon, MD, MPH, Division of Rheumatology, Allergy, and Immunology, Tufts Medical Center, Boston, Massachusetts, USA
| |
Collapse
|
9
|
Li T, Luo T, Chen B, Huang C, Shen Z, Xu Z, Nissman D, Golightly YM, Nelson AE, Niethammer M, Zhu H. Charting Aging Trajectories of Knee Cartilage Thickness for Early Osteoarthritis Risk Prediction: An MRI Study from the Osteoarthritis Initiative Cohort. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.12.23295398. [PMID: 37745529 PMCID: PMC10516090 DOI: 10.1101/2023.09.12.23295398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Knee osteoarthritis (OA), a prevalent joint disease in the U.S., poses challenges in terms of predicting of its early progression. Although high-resolution knee magnetic resonance imaging (MRI) facilitates more precise OA diagnosis, the heterogeneous and multifactorial aspects of OA pathology remain significant obstacles for prognosis. MRI-based scoring systems, while standardizing OA assessment, are both time-consuming and labor-intensive. Current AI technologies facilitate knee OA risk scoring and progression prediction, but these often focus on the symptomatic phase of OA, bypassing initial-stage OA prediction. Moreover, their reliance on complex algorithms can hinder clinical interpretation. To this end, we make this effort to construct a computationally efficient, easily-interpretable, and state-of-the-art approach aiding in the radiographic OA (rOA) auto-classification and prediction of the incidence and progression, by contrasting an individual's cartilage thickness with a similar demographic in the rOA-free cohort. To better visualize, we have developed the toolset for both prediction and local visualization. A movie demonstrating different subtypes of dynamic changes in local centile scores during rOA progression is available at https://tli3.github.io/KneeOA/. Specifically, we constructed age-BMI-dependent reference charts for knee OA cartilage thickness, based on MRI scans from 957 radiographic OA (rOA)-free individuals from the Osteoarthritis Initiative cohort. Then we extracted local and global centiles by contrasting an individual's cartilage thickness to the rOA-free cohort with a similar age and BMI. Using traditional boosting approaches with our centile-based features, we obtain rOA classification of KLG ≤ 1 versus KLG = 2 (AUC = 0.95, F1 = 0.89), KLG ≤ 1 versus KLG ≥ 2 (AUC = 0.90, F1 = 0.82) and prediction of KLG2 progression (AUC = 0.98, F1 = 0.94), rOA incidence (KLG increasing from < 2 to ≥ 2; AUC = 0.81, F1 = 0.69) and rOA initial transition (KLG from 0 to 1; AUC = 0.64, F1 = 0.65) within a future 48-month period. Such performance in classifying KLG ≥ 2 matches that of deep learning methods in recent literature. Furthermore, its clinical interpretation suggests that cartilage changes, such as thickening in lateral femoral and anterior femoral regions and thinning in lateral tibial regions, may serve as indicators for prediction of rOA incidence and early progression. Meanwhile, cartilage thickening in the posterior medial and posterior lateral femoral regions, coupled with a reduction in the central medial femoral region, may signify initial phases of rOA transition.
Collapse
Affiliation(s)
- Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tianyou Luo
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Boqi Chen
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Chao Huang
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Zhengyang Shen
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Zhenlin Xu
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daniel Nissman
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yvonne M. Golightly
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- College of Allied Health Professions, University of Nebraska Medical Center, Omaha, NE, USA
| | - Amanda E. Nelson
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Marc Niethammer
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hongtu Zhu
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| |
Collapse
|
10
|
Kurmis AP. A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty. ARTHROPLASTY 2023; 5:40. [PMID: 37400876 DOI: 10.1186/s42836-023-00189-0] [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: 02/03/2023] [Accepted: 04/19/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) has become involved in many aspects of everyday life, from voice-activated virtual assistants built into smartphones to global online search engines. Similarly, many areas of modern medicine have found ways to incorporate such technologies into mainstream practice. Despite the enthusiasm, robust evidence to support the utility of AI in contemporary total knee arthroplasty (TKA) remains limited. The purpose of this review was to provide an up-to-date summary of the use of AI in TKA and to explore its current and future value. METHODS Initially, a structured systematic review of the literature was carried out, following PRISMA search principles, with the aim of summarising the understanding of the field and identifying clinical and knowledge gaps. RESULTS A limited body of published work exists in this area. Much of the available literature is of poor methodological quality and many published studies could be best described as "demonstration of concepts" rather than "proof of concepts". There exists almost no independent validation of reported findings away from designer/host sites, and the extrapolation of key results to general orthopaedic sites is limited. CONCLUSION While AI has certainly shown value in a small number of specific TKA-associated applications, the majority to date have focused on risk, cost and outcome prediction, rather than surgical care, per se. Extensive future work is needed to demonstrate external validity and reliability in non-designer settings. Well-performed studies are warranted to ensure that the scientific evidence base supporting the use of AI in knee arthroplasty matches the global hype.
Collapse
Affiliation(s)
- Andrew P Kurmis
- Discipline of Medical Specialties, University of Adelaide, Adelaide, SA, 5005, Australia.
- Department of Orthopaedic Surgery, Lyell McEwin Hospital, Haydown Road, Elizabeth Vale, SA, 5112, Australia.
- College of Medicine & Public Health, Flinders University, Bedford Park, SA, 5042, Australia.
| |
Collapse
|
11
|
Martel-Pelletier J, Paiement P, Pelletier JP. Magnetic resonance imaging assessments for knee segmentation and their use in combination with machine/deep learning as predictors of early osteoarthritis diagnosis and prognosis. Ther Adv Musculoskelet Dis 2023; 15:1759720X231165560. [PMID: 37151912 PMCID: PMC10155034 DOI: 10.1177/1759720x231165560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 03/23/2023] [Indexed: 05/09/2023] Open
Abstract
Knee osteoarthritis (OA) is a prevalent and disabling disease that can develop over decades. This disease is heterogeneous and involves structural changes in the whole joint, encompassing multiple tissue types. Detecting OA before the onset of irreversible changes is crucial for early management, and this could be achieved by allowing knee tissue visualization and quantifying their changes over time. Although some imaging modalities are available for knee structure assessment, magnetic resonance imaging (MRI) is preferred. This narrative review looks at existing literature, first on MRI-developed approaches for evaluating knee articular tissues, and second on prediction using machine/deep-learning-based methodologies and MRI as input or outcome for early OA diagnosis and prognosis. A substantial number of MRI methodologies have been developed to assess several knee tissues in a semi-quantitative and quantitative fashion using manual, semi-automated and fully automated systems. This dynamic field has grown substantially since the advent of machine/deep learning. Another active area is predictive modelling using machine/deep-learning methodologies enabling robust early OA diagnosis/prognosis. Moreover, incorporating MRI markers as input/outcome in such predictive models is important for a more accurate OA structural diagnosis/prognosis. The main limitation of their usage is the ability to move them in rheumatology practice. In conclusion, MRI knee tissue determination and quantification provide early indicators for individuals at high risk of developing this disease or for patient prognosis. Such assessment of knee tissues, combined with the development of models/tools from machine/deep learning using, in addition to other parameters, MRI markers for early diagnosis/prognosis, will maximize opportunities for individualized risk assessment for use in clinical practice permitting precision medicine. Future efforts should be made to integrate such prediction models into open access, allowing early disease management to prevent or delay the OA outcome.
Collapse
Affiliation(s)
- Johanne Martel-Pelletier
- Osteoarthritis Research Unit, University of
Montreal Hospital Research Centre (CRCHUM), 900 Saint-Denis, R11.412B,
Montreal, QC H2X 0A9, Canada
| | - Patrice Paiement
- Osteoarthritis Research Unit, University of
Montreal Hospital Research Centre (CRCHUM), Montreal, QC, Canada
| | - Jean-Pierre Pelletier
- Osteoarthritis Research Unit, University of
Montreal Hospital Research Centre (CRCHUM), Montreal, QC, Canada
| |
Collapse
|
12
|
Salis Z, Gallego B, Sainsbury A. Researchers in rheumatology should avoid categorization of continuous predictor variables. BMC Med Res Methodol 2023; 23:104. [PMID: 37101144 PMCID: PMC10134601 DOI: 10.1186/s12874-023-01926-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 04/18/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND Rheumatology researchers often categorize continuous predictor variables. We aimed to show how this practice may alter results from observational studies in rheumatology. METHODS We conducted and compared the results of two analyses of the association between our predictor variable (percentage change in body mass index [BMI] from baseline to four years) and two outcome variable domains of structure and pain in knee and hip osteoarthritis. These two outcome variable domains covered 26 different outcomes for knee and hip combined. In the first analysis (categorical analysis), percentage change in BMI was categorized as ≥ 5% decrease in BMI, < 5% change in BMI, and ≥ 5% increase in BMI, while in the second analysis (continuous analysis), it was left as a continuous variable. In both analyses (categorical and continuous), we used generalized estimating equations with a logistic link function to investigate the association between the percentage change in BMI and the outcomes. RESULTS For eight of the 26 investigated outcomes (31%), the results from the categorical analyses were different from the results from the continuous analyses. These differences were of three types: 1) for six of these eight outcomes, while the continuous analyses revealed associations in both directions (i.e., a decrease in BMI had one effect, while an increase in BMI had the opposite effect), the categorical analyses showed associations only in one direction of BMI change, not both; 2) for another one of these eight outcomes, the categorical analyses suggested an association with change in BMI, while this association was not shown in the continuous analyses (this is potentially a false positive association); 3) for the last of the eight outcomes, the continuous analyses suggested an association of change in BMI, while this association was not shown in the categorical analyses (this is potentially a false negative association). CONCLUSIONS Categorization of continuous predictor variables alters the results of analyses and could lead to different conclusions; therefore, researchers in rheumatology should avoid it.
Collapse
Affiliation(s)
- Zubeyir Salis
- The University of New South Wales, Centre for Big Data Research in Health, Kensington, NSW, Australia
| | - Blanca Gallego
- The University of New South Wales, Centre for Big Data Research in Health, Kensington, NSW, Australia
| | - Amanda Sainsbury
- School of Human Sciences, The University of Western Australia, Crawley, Perth, WA, 6009, Australia.
| |
Collapse
|
13
|
Jang SJ, Fontana MA, Kunze KN, Anderson CG, Sculco TP, Mayman DJ, Jerabek SA, Vigdorchik JM, Sculco PK. An Interpretable Machine Learning Model for Predicting 10-Year Total Hip Arthroplasty Risk. J Arthroplasty 2023:S0883-5403(23)00336-4. [PMID: 37019312 DOI: 10.1016/j.arth.2023.03.087] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 03/20/2023] [Accepted: 03/25/2023] [Indexed: 04/07/2023] Open
Abstract
BACKGROUND As the demand for total hip arthroplasty (THA) rises, a predictive model for THA risk may aid patients and clinicians in augmenting shared decision-making. We aimed to develop and validate a model predicting THA within 10 years in patients using demographic, clinical, and deep learning (DL)-automated radiographic measurements. METHODS Patients enrolled in the Osteoarthritis Initiative were included. DL algorithms measuring osteoarthritis- and dysplasia-relevant parameters on baseline pelvis radiographs were developed. Demographic, clinical, and radiographic measurement variables were then used to train generalized additive models to predict THA within 10 years from baseline. A total of 4,796 patients were included (9,592 hips; 58% female; 230 THAs (2.4%)). Model performance using 1) baseline demographic and clinical variables 2) radiographic variables, and 3) all variables were compared. RESULTS Using 110 demographic and clinical variables, the model had a baseline area under the receiver operating curve (AUROC) of 0.68 and area under the precision recall curve (AUPRC) of 0.08. Using 26 DL-automated hip measurements, the AUROC was 0.77 and AUPRC was 0.22. Combining all variables, the model improved to an AUROC of 0.81 and AUPRC of 0.28. Three of the top five predictive features in the combined model were radiographic variables including minimum joint space along with hip pain and analgesic use. Partial dependency plots revealed predictive discontinuities for radiographic measurements consistent with literature thresholds of osteoarthritis progression and hip dysplasia. CONCLUSION A machine learning model predicting 10-year THA performed more accurately with DL radiographic measurements. The model weighted predictive variables in concordance with clinical THA-pathology assessments.
Collapse
Affiliation(s)
- Seong Jun Jang
- Weill Cornell College of Medicine, New York, NY, USA; Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA.
| | - Mark A Fontana
- Weill Cornell College of Medicine, New York, NY, USA; Center for Analytics, Modeling, and Performance, Hospital for Special Surgery, New York, NY, USA
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA
| | | | - Thomas P Sculco
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, NY, USA
| | - David J Mayman
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, NY, USA
| | - Seth A Jerabek
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, NY, USA
| | - Jonathan M Vigdorchik
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, NY, USA
| | - Peter K Sculco
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA; Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, NY, USA
| |
Collapse
|
14
|
Zaniletti I, Larson DR, Lewallen DG, Berry DJ, Maradit Kremers H. How to Develop and Validate Prediction Models for Orthopedic Outcomes. J Arthroplasty 2023; 38:627-633. [PMID: 36572235 PMCID: PMC10023373 DOI: 10.1016/j.arth.2022.12.032] [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: 07/12/2022] [Revised: 12/15/2022] [Accepted: 12/17/2022] [Indexed: 12/25/2022] Open
Abstract
Prediction models are common in medicine for predicting outcomes such as mortality, complications, or response to treatment. Despite the growing interest in these models in arthroplasty (and orthopaedics in general), few have been adopted in clinical practice. If robustly built and validated, prediction models can be excellent tools to support surgical decision making. In this paper, we provide an overview of the statistical concepts surrounding prediction models and outline practical steps for prediction model development and validation in arthroplasty research. Please visit the followinghttps://www.youtube.com/watch?v=9Yrit23Rkicfor a video that explains the highlights of the paper in practical terms.
Collapse
Affiliation(s)
| | - Dirk R. Larson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | | | - Daniel J. Berry
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN
| | - Hilal Maradit Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN
| |
Collapse
|
15
|
Farajzadeh N, Sadeghzadeh N, Hashemzadeh M. IJES-OA Net: A residual neural network to classify knee osteoarthritis from radiographic images based on the edges of the intra-joint spaces. Med Eng Phys 2023; 113:103957. [PMID: 36965998 DOI: 10.1016/j.medengphy.2023.103957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/30/2022] [Accepted: 02/09/2023] [Indexed: 02/12/2023]
Abstract
Among the musculoskeletal disorders in the world, osteoarthritis is the most common, affecting most of the body joints, especially the knee. Clinical radiographic imaging methods are commonly used to diagnose osteoarthritis thanks to their cheapness and availability. Due to the low quality and indiscernibility of these images, however, accurate osteoarthritis diagnosis has always faced inaccuracies, such as the wrong diagnosis. One of the osteoarthritis hallmarks is joint space narrowing. Thus, its degree and severity can be determined relatively by assessing the space between the bones in the joint. Therefore, in this research, a deep residual neural network, termed IJES-OA Net, is presented to automatically grade (classify) the severity of knee osteoarthritis via radiographs. This is achieved by tuning it in a way to have it focused on the distance of the edges of the bones inside the knee joint. Experimental results which are conducted on MOST (for training) and OAI (for validation and testing) datasets show that the IJES-OA Net achieves high average accuracy as well as average precision (80.23% and 0.802, respectively) while having less complexity compared to other methods. Additionally, the resulting attention maps from IJES-OA Net are accurate enough that increase experts' reliance on the provided results.
Collapse
Affiliation(s)
- Nacer Farajzadeh
- Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran; Artificial Intelligence and Machine Learning Research Laboratory, Azarbaijan Shahid Madani University, Tabriz, Iran.
| | - Nima Sadeghzadeh
- Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran; Artificial Intelligence and Machine Learning Research Laboratory, Azarbaijan Shahid Madani University, Tabriz, Iran
| | - Mahdi Hashemzadeh
- Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran; Artificial Intelligence and Machine Learning Research Laboratory, Azarbaijan Shahid Madani University, Tabriz, Iran
| |
Collapse
|
16
|
Mahmoud K, Alagha MA, Nowinka Z, Jones G. Predicting total knee replacement at 2 and 5 years in osteoarthritis patients using machine learning. BMJ SURGERY, INTERVENTIONS, & HEALTH TECHNOLOGIES 2023; 5:e000141. [PMID: 36817624 PMCID: PMC9933661 DOI: 10.1136/bmjsit-2022-000141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 12/09/2022] [Indexed: 02/17/2023] Open
Abstract
Objectives Knee osteoarthritis is a major cause of physical disability and reduced quality of life, with end-stage disease often treated by total knee replacement (TKR). We set out to develop and externally validate a machine learning model capable of predicting the need for a TKR in 2 and 5 years time using routinely collected health data. Design A prospective study using datasets Osteoarthritis Initiative (OAI) and the Multicentre Osteoarthritis Study (MOST). OAI data were used to train the models while MOST data formed the external test set. The data were preprocessed using feature selection to curate 45 candidate features including demographics, medical history, imaging assessments, history of intervention and outcome. Setting The study was conducted using two multicentre USA-based datasets of participants with or at high risk of knee OA. Participants The study excluded participants with at least one existing TKR. OAI dataset included participants aged 45-79 years of which 3234 were used for training and 809 for internal testing, while MOST involved participants aged 50-79 and 2248 were used for external testing. Main outcome measures The primary outcome of this study was prediction of TKR onset at 2 and 5 years. Performance was evaluated using area under the curve (AUC) and F1-score and key predictors identified. Results For the best performing model (gradient boosting machine), the AUC at 2 years was 0.913 (95% CI 0.876 to 0.951), and at 5 years 0.873 (95% CI 0.839 to 0.907). Radiographic-derived features, questionnaire-based assessments alongside the patient's educational attainment were key predictors for these models. Conclusions Our approach suggests that routinely collected patient data are sufficient to drive a predictive model with a clinically acceptable level of accuracy (AUC>0.7) and is the first such tool to be externally validated. This level of accuracy is higher than previously published models utilising MRI data, which is not routinely collected.
Collapse
Affiliation(s)
- Khadija Mahmoud
- MSk Lab, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - M Abdulhadi Alagha
- MSk Lab, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK,Data Science Institute, The London School of Economics and Political Science, London, UK
| | - Zuzanna Nowinka
- MSk Lab, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Gareth Jones
- MSk Lab, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| |
Collapse
|
17
|
Meena A. CORR Insights®: Machine-learning Models Predict 30-day Mortality, Cardiovascular Complications, and Respiratory Complications After Aseptic Revision Total Joint Arthroplasty. Clin Orthop Relat Res 2022; 480:2146-2147. [PMID: 35849057 PMCID: PMC9556019 DOI: 10.1097/corr.0000000000002325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 06/27/2022] [Indexed: 01/31/2023]
Affiliation(s)
- Amit Meena
- Clinical Fellow, Gelenkpunkt Sports and Joint Surgery, FIFA Medical Centre of Excellence, Innsbruck, Austria
| |
Collapse
|
18
|
Ciliberti FK, Cesarelli G, Guerrini L, Gunnarsson AE, Forni R, Aubonnet R, Recenti M, Jacob D, Jónsson H, Cangiano V, Islind AS, Gambacorta M, Gargiulo P. The role of bone mineral density and cartilage volume to predict knee cartilage degeneration. Eur J Transl Myol 2022; 32. [PMID: 35766481 PMCID: PMC9295173 DOI: 10.4081/ejtm.2022.10678] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 06/22/2022] [Indexed: 12/02/2022] Open
Abstract
Knee Osteoarthritis (OA) is a highly prevalent condition affecting knee joint that causes loss of physical function and pain. Clinical treatments are mainly focused on pain relief and limitation of disabilities; therefore, it is crucial to find new paradigms assessing cartilage conditions for detecting and monitoring the progression of OA. The goal of this paper is to highlight the predictive power of several features, such as cartilage density, volume and surface. These features were extracted from the 3D reconstruction of knee joint of forty-seven different patients, subdivided into two categories: degenerative and non-degenerative. The most influent parameters for the degeneration of the knee cartilage were determined using two machine learning classification algorithms (logistic regression and support vector machine); later, box plots, which depicted differences between the classes by gender, were presented to analyze several of the key features’ trend. This work is part of a strategy that aims to find a new solution to assess cartilage condition based on new-investigated features.
Collapse
Affiliation(s)
| | - Giuseppe Cesarelli
- Department of Chemical, Materials and Production Engineering (DICMaPI), University of Naples Federico II, Naples.
| | - Lorena Guerrini
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik.
| | | | - Riccardo Forni
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland; Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Cesena.
| | - Romain Aubonnet
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik.
| | - Marco Recenti
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik.
| | - Deborah Jacob
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik.
| | - Halldór Jónsson
- Department of Orthopaedics, Landspitali, University Hospital of Iceland, Reykjavik, Iceland; Medical Faculty, University of Iceland, Reykjavik.
| | - Vincenzo Cangiano
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik.
| | | | | | - Paolo Gargiulo
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland; Department of Science, Landspitali, University Hospital of Iceland, Reykjavik.
| |
Collapse
|
19
|
Teoh YX, Lai KW, Usman J, Goh SL, Mohafez H, Hasikin K, Qian P, Jiang Y, Zhang Y, Dhanalakshmi S. Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4138666. [PMID: 35222885 PMCID: PMC8881170 DOI: 10.1155/2022/4138666] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 01/24/2022] [Accepted: 01/26/2022] [Indexed: 12/30/2022]
Abstract
Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren-Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.
Collapse
Affiliation(s)
- Yun Xin Teoh
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Juliana Usman
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Siew Li Goh
- Faculty of Medicine, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Hamidreza Mohafez
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Pengjiang Qian
- School of Artificial Intelligence and Computer Sciences, Jiangnan University, Wuxi 214122, China
| | - Yizhang Jiang
- School of Artificial Intelligence and Computer Sciences, Jiangnan University, Wuxi 214122, China
| | - Yuanpeng Zhang
- Department of Medical Informatics of Medical (Nursing) School, Nantong University, Nantong 226001, China
| | - Samiappan Dhanalakshmi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
| |
Collapse
|
20
|
Mobasheri A, Trumble TN, Byron CR. Editorial: One Step at a Time: Advances in Osteoarthritis. Front Vet Sci 2021; 8:727477. [PMID: 34336985 PMCID: PMC8322576 DOI: 10.3389/fvets.2021.727477] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 06/21/2021] [Indexed: 12/14/2022] Open
Affiliation(s)
- Ali Mobasheri
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Regenerative Medicine, State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania
- Departments of Orthopedics, Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht, Netherlands
- Department of Joint Surgery, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- World Health Organization Collaborating Center for Public Health Aspects of Musculoskeletal Health and Aging, Université de Liège, Liège, Belgium
| | - Troy N. Trumble
- Veterinary Population Medicine, University of Minnesota Twin Cities, St. Paul, MN, United States
| | - Christopher R. Byron
- Department of Large Animal Clinical Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, United States
| |
Collapse
|