1
|
Zhao Z, Zhao M, Yang T, Li J, Qin C, Wang B, Wang L, Li B, Liu J. Identifying significant structural factors associated with knee pain severity in patients with osteoarthritis using machine learning. Sci Rep 2024; 14:14705. [PMID: 38926487 DOI: 10.1038/s41598-024-65613-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 06/21/2024] [Indexed: 06/28/2024] Open
Abstract
Our main objective was to use machine learning methods to identify significant structural factors associated with pain severity in knee osteoarthritis patients. Additionally, we assessed the potential of various classes of imaging data using machine learning techniques to gauge knee pain severity. The data of semi-quantitative assessments of knee radiographs, semi-quantitative assessments of knee magnetic resonance imaging (MRI), and MRI images from 567 individuals in the Osteoarthritis Initiative (OAI) were utilized to train a series of machine learning models. Models were constructed using five machine learning methods: random forests (RF), support vector machines (SVM), logistic regression (LR), decision tree (DT), and Bayesian (Bayes). Employing tenfold cross-validation, we selected the best-performing models based on the area under the curve (AUC). The study results indicate no significant difference in performance among models using different imaging data. Subsequently, we employed a convolutional neural network (CNN) to extract features from magnetic resonance imaging (MRI), and class activation mapping (CAM) was utilized to generate saliency maps, highlighting regions associated with knee pain severity. A radiologist reviewed the images, identifying specific lesions colocalized with the CAM. The review of 421 knees revealed that effusion/synovitis (30.9%) and cartilage loss (30.6%) were the most frequent abnormalities associated with pain severity. Our study suggests cartilage loss and synovitis/effusion lesions as significant structural factors affecting pain severity in patients with knee osteoarthritis. Furthermore, our study highlights the potential of machine learning for assessing knee pain severity using radiographs.
Collapse
Affiliation(s)
- Zhengkuan Zhao
- Department of Joint, Tianjin Hospital, Tianjin, China
- Tianjin Medical University, Tianjin, China
| | - Mingkuan Zhao
- National Elite Institute of Engineering, Chongqing University, Chongqing, China
- School of Computer Science, Xi'an Jiaotong University, Xi'an, China
| | - Tao Yang
- Orthopedics Department, Tianjin Hospital, Tianjin, China
| | - Jie Li
- Tianjin Medical University, Tianjin, China
| | - Chao Qin
- Department of Joint, Tianjin Hospital, Tianjin, China
- Tianjin Medical University, Tianjin, China
| | - Ben Wang
- Tianjin Medical University, Tianjin, China
| | - Li Wang
- Tianjin Medical University, Tianjin, China
| | - Bing Li
- Department of Joint, Tianjin Hospital, Tianjin, China.
| | - Jun Liu
- Department of Joint, Tianjin Hospital, Tianjin, China.
| |
Collapse
|
2
|
Caratsch L, Lechtenboehmer C, Caorsi M, Oung K, Zanchi F, Aleman Y, Walker UA, Omoumi P, Hügle T. Detection and Grading of Radiographic Hand Osteoarthritis Using an Automated Machine Learning Platform. ACR Open Rheumatol 2024; 6:388-395. [PMID: 38576187 PMCID: PMC11168904 DOI: 10.1002/acr2.11665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 01/31/2024] [Accepted: 02/09/2024] [Indexed: 04/06/2024] Open
Abstract
OBJECTIVE Automated machine learning (autoML) platforms allow health care professionals to play an active role in the development of machine learning (ML) algorithms according to scientific or clinical needs. The aim of this study was to develop and evaluate such a model for automated detection and grading of distal hand osteoarthritis (OA). METHODS A total of 13,690 hand radiographs from 2,863 patients within the Swiss Cohort of Quality Management (SCQM) and an external control data set of 346 non-SCQM patients were collected and scored for distal interphalangeal OA (DIP-OA) using the modified Kellgren/Lawrence (K/L) score. Giotto (Learn to Forecast [L2F]) was used as an autoML platform for training two convolutional neural networks for DIP joint extraction and subsequent classification according to the K/L scores. A total of 48,892 DIP joints were extracted and then used to train the classification model. Heatmaps were generated independently of the platform. User experience of a web application as a provisional user interface was investigated by rheumatologists and radiologists. RESULTS The sensitivity and specificity of this model for detecting DIP-OA were 79% and 86%, respectively. The accuracy for grading the correct K/L score was 75%, with a κ score of 0.76. The accuracy per DIP-OA class differed, with 86% for no OA (defined as K/L scores 0 and 1), 71% for a K/L score of 2, 46% for a K/L score of 3, and 67% for a K/L score of 4. Similar values were obtained in an independent external test set. Qualitative and quantitative user experience testing of the web application revealed a moderate to high demand for automated DIP-OA scoring among rheumatologists. Conversely, radiologists expressed a low demand, except for the use of heatmaps. CONCLUSION AutoML platforms are an opportunity to develop clinical end-to-end ML algorithms. Here, automated radiographic DIP-OA detection is both feasible and usable, whereas grading among individual K/L scores (eg, for clinical trials) remains challenging.
Collapse
Affiliation(s)
- Leo Caratsch
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
- City Hospital WaidZurichSwitzerland
- L2F (Learn to Forecast)LausanneSwitzerland
| | - Christian Lechtenboehmer
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
- City Hospital WaidZurichSwitzerland
- L2F (Learn to Forecast)LausanneSwitzerland
- University Hospital of BaselBaselSwitzerland
| | | | - Karine Oung
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Fabio Zanchi
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Yasser Aleman
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
| | | | - Patrick Omoumi
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Thomas Hügle
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
| |
Collapse
|
3
|
Naguib SM, Kassem MA, Hamza HM, Fouda MM, Saleh MK, Hosny KM. Automated system for classifying uni-bicompartmental knee osteoarthritis by using redefined residual learning with convolutional neural network. Heliyon 2024; 10:e31017. [PMID: 38803931 PMCID: PMC11128872 DOI: 10.1016/j.heliyon.2024.e31017] [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: 01/11/2024] [Revised: 05/08/2024] [Accepted: 05/09/2024] [Indexed: 05/29/2024] Open
Abstract
Knee Osteoarthritis (OA) is one of the most common joint diseases that may cause physical disability associated with a significant personal and socioeconomic burden. X-ray imaging is the cheapest and most common method to detect Knee (OA). Accurate classification of knee OA can help physicians manage treatment efficiently and slow knee OA progression. This study aims to classify knee OA X-ray images according to anatomical types, such as uni or bicompartmental. The study proposes a deep learning model for classifying uni or bicompartmental knee OA based on redefined residual learning with CNN. The proposed model was trained, validated, and tested on a dataset containing 733 knee X-ray images (331 normal Knee images, 205 unicompartmental, and 197 bicompartmental knee images). The results show 61.81 % and 68.33 % for accuracy and specificity, respectively. Then, the performance of the proposed model was compared with different pre-trained CNNs. The proposed model achieved better results than all pre-trained CNNs.
Collapse
Affiliation(s)
- Soaad M. Naguib
- Department of Information Systems, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519, Egypt
| | - Mohamed A. Kassem
- Department of Robotics and Intelligent Machines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr el-Sheikh, Egypt
| | - Hanaa M. Hamza
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519, Egypt
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA
| | - Mohammed K. Saleh
- Department of Orthopedic Surgery, Faculty of Medicine, Zagazig University, Zagazig, 44519, Egypt
| | - Khalid M. Hosny
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519, Egypt
| |
Collapse
|
4
|
Nielsen RL, Monfeuga T, Kitchen RR, Egerod L, Leal LG, Schreyer ATH, Gade FS, Sun C, Helenius M, Simonsen L, Willert M, Tahrani AA, McVey Z, Gupta R. Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning. Nat Commun 2024; 15:2817. [PMID: 38561399 PMCID: PMC10985086 DOI: 10.1038/s41467-024-46663-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Osteoarthritis (OA) is increasing in prevalence and has a severe impact on patients' lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA diagnosis, integrating retrospective clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC: 0.72, 95%CI (0.71-0.73)). Higher age, BMI and prescription of non-steroidal anti-inflammatory drugs contributed most to increased OA risk prediction ahead of diagnosis. We identified 14 subgroups of OA risk profiles. These subgroups were validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients being uniquely assigned to one of the 14 subgroups. Individual OA risk profiles were characterised by personalised biomarkers. Omics integration demonstrated the predictive importance of key OA genes and pathways (e.g., GDF5 and TGF-β signalling) and OA-specific biomarkers (e.g., CRTAC1 and COL9A1). In summary, this work identifies opportunities for personalised OA prevention and insights into its underlying pathogenesis.
Collapse
Affiliation(s)
| | | | | | - Line Egerod
- Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Luis G Leal
- Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | - Carol Sun
- Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | | | | | - Zahra McVey
- Novo Nordisk Research Centre Oxford, Oxford, UK
| | | |
Collapse
|
5
|
Hügle T. Advancing Rheumatology Care Through Machine Learning. Pharmaceut Med 2024; 38:87-96. [PMID: 38421585 PMCID: PMC10948517 DOI: 10.1007/s40290-024-00515-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/31/2024] [Indexed: 03/02/2024]
Abstract
Rheumatologic diseases are marked by their complexity, involving immune-, metabolic- and mechanically mediated processes which can affect different organ systems. Despite a growing arsenal of targeted medications, many rheumatology patients fail to achieve full remission. Assessing disease activity remains challenging, as patients prioritize different symptoms and disease phenotypes vary. This is also reflected in clinical trials where the efficacy of drugs is not necessarily measured in an optimal way with the traditional outcome assessment. The recent COVID-19 pandemic has catalyzed a digital transformation in healthcare, embracing telemonitoring and patient-reported data via apps and wearables. As a further driver of digital medicine, electronic medical record (EMR) providers are actively engaged in developing algorithms for clinical decision support, heralding a shift towards patient-centered, decentralized care. Machine learning algorithms have emerged as valuable tools for handling the increasing volume of patient data, promising to enhance treatment quality and patient well-being. Convolutional neural networks (CNN) are particularly promising for radiological image analysis, aiding in the detection of specific lesions such as erosions, sacroiliitis, or osteoarthritis, with several FDA-approved applications. Clinical predictions, including numerical disease activity forecasts and medication choices, offer the potential to optimize treatment strategies. Numeric predictions can be integrated into clinical workflows, allowing for shared decision making with patients. Clustering patients based on disease characteristics provides a personalized care approach. Digital biomarkers, such as patient-reported outcomes and wearables data, offer insights into disease progression and therapy response more flexibly and outside patient consultations. In association with patient-reported outcomes, disease-specific digital biomarkers via image recognition or single-camera motion capture enables more efficient remote patient monitoring. Digital biomarkers may also play a major role in clinical trials in the future as continuous, disease-specific outcome measurement facilitating decentralized studies. Prediction models can help with patient selection in clinical trials, such as by predicting high disease activity. Efforts are underway to integrate these advancements into clinical workflows using digital pathways and remote patient monitoring platforms. In summary, machine learning, digital biomarkers, and advanced imaging technologies hold immense promise for enhancing clinical decision support and clinical trials in rheumatology. Effective integration will require a multidisciplinary approach and continued validation through prospective studies.
Collapse
Affiliation(s)
- Thomas Hügle
- Department of Rheumatology, University Hospital Lausanne (CHUV) and University of Lausanne, Avenue Pierre-Decker 4, 1001, Lausanne, Switzerland.
| |
Collapse
|
6
|
Guermazi A, Omoumi P, Tordjman M, Fritz J, Kijowski R, Regnard NE, Carrino J, Kahn CE, Knoll F, Rueckert D, Roemer FW, Hayashi D. How AI May Transform Musculoskeletal Imaging. Radiology 2024; 310:e230764. [PMID: 38165245 PMCID: PMC10831478 DOI: 10.1148/radiol.230764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 06/18/2023] [Accepted: 07/11/2023] [Indexed: 01/03/2024]
Abstract
While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists. Upon successful clinical implementation, a wide variety of AI-based tools can improve the musculoskeletal radiologist's workflow by triaging imaging examinations, helping with image interpretation, and decreasing the reporting time. Additional AI applications may also be helpful for business, education, and research purposes if successfully integrated into the daily practice of musculoskeletal radiology. The question is not whether AI will replace radiologists, but rather how musculoskeletal radiologists can take advantage of AI to enhance their expert capabilities.
Collapse
Affiliation(s)
- Ali Guermazi
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Patrick Omoumi
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Mickael Tordjman
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Jan Fritz
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Richard Kijowski
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Nor-Eddine Regnard
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - John Carrino
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Charles E. Kahn
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Florian Knoll
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Daniel Rueckert
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Frank W. Roemer
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Daichi Hayashi
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| |
Collapse
|
7
|
Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [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: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
Collapse
Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | | | | | | | | |
Collapse
|
8
|
Ratna HVK, Jeyaraman M, Jeyaraman N, Nallakumarasamy A, Sharma S, Khanna M, Gupta A. Machine learning and deep neural network-based learning in osteoarthritis knee. World J Methodol 2023; 13:419-425. [PMID: 38229942 PMCID: PMC10789099 DOI: 10.5662/wjm.v13.i5.419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 09/14/2023] [Accepted: 09/28/2023] [Indexed: 12/20/2023] Open
Abstract
Osteoarthritis (OA) of the knee joint is considered the commonest musculoskeletal condition leading to marked disability for patients residing in various regions around the globe. Application of machine learning (ML) in doing research regarding OA has brought about various clinical advances viz, OA being diagnosed at preliminary stages, prediction of chances of development of OA among the population, discovering various phenotypes of OA, calculating the severity in OA structure and also discovering people with slow and fast progression of disease pathology, etc. Various publications are available regarding machine learning methods for the early detection of osteoarthritis. The key features are detected by morphology, molecular architecture, and electrical and mechanical functions. In addition, this particular technique was utilized to assess non-interfering, non-ionizing, and in-vivo techniques using magnetic resonance imaging. ML is being utilized in OA, chiefly with the formulation of large cohorts viz, the OA Initiative, a cohort observational study, the Multi-centre Osteoarthritis Study, an observational, prospective longitudinal study and the Cohort Hip & Cohort Knee, an observational cohort prospective study of both hip and knee OA. Though ML has various contributions and enhancing applications, it remains an imminent field with high potential, also with its limitations. Many more studies are to be carried out to find more about the link between machine learning and knee osteoarthritis, which would help in the improvement of making decisions clinically, and expedite the necessary interventions.
Collapse
Affiliation(s)
- Harish V K Ratna
- Department of Orthopaedics, Rathimed Speciality Hospital, Chennai 600040, Tamil Nadu, India
| | - Madhan Jeyaraman
- Department of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai 600077, Tamil Nadu, India
- Department of Orthopaedics, South Texas Orthopaedic Research Institute, Laredo, TX 78045, United States
| | - Naveen Jeyaraman
- Department of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai 600077, Tamil Nadu, India
| | - Arulkumar Nallakumarasamy
- Department of Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai 600077, Tamil Nadu, India
| | - Shilpa Sharma
- Department of Paediatric Surgery, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Manish Khanna
- Department of Orthopaedics, Autonomous State Medical College, Ayodhya 224133, Uttar Pradesh, India
| | - Ashim Gupta
- Department of Orthopaedics, South Texas Orthopaedic Research Institute, Laredo, TX 78045, United States
- Department of Regenerative Medicine, Regenerative Orthopaedics, Noida 201301, Uttar Pradesh, India
- Department of Regenerative Medicine, Future Biologics, Lawrenceville, GA 30043, United States
- Department of Regenerative Medicine, BioIntegarte, Lawrenceville, GA 30043, United States
| |
Collapse
|
9
|
Kijowski R, Fritz J, Deniz CM. Deep learning applications in osteoarthritis imaging. Skeletal Radiol 2023; 52:2225-2238. [PMID: 36759367 PMCID: PMC10409879 DOI: 10.1007/s00256-023-04296-6] [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: 10/12/2022] [Revised: 12/22/2022] [Accepted: 01/31/2023] [Indexed: 02/11/2023]
Abstract
Deep learning (DL) is one of the most exciting new areas in medical imaging. This article will provide a review of current applications of DL in osteoarthritis (OA) imaging, including methods used for cartilage lesion detection, OA diagnosis, cartilage segmentation, and OA risk assessment. DL techniques have been shown to have similar diagnostic performance as human readers for detecting and grading cartilage lesions within the knee on MRI. A variety of DL methods have been developed for detecting and grading the severity of knee OA and various features of knee OA on X-rays using standardized classification systems with diagnostic performance similar to human readers. Multiple DL approaches have been described for fully automated segmentation of cartilage and other knee tissues and have achieved higher segmentation accuracy than currently used methods with substantial reductions in segmentation times. Various DL models analyzing baseline X-rays and MRI have been developed for OA risk assessment. These models have shown high diagnostic performance for predicting a wide variety of OA outcomes, including the incidence and progression of radiographic knee OA, the presence and progression of knee pain, and future total knee replacement. The preliminary results of DL applications in OA imaging have been encouraging. However, many DL techniques require further technical refinement to maximize diagnostic performance. Furthermore, the generalizability of DL approaches needs to be further investigated in prospective studies using large image datasets acquired at different institutions with different imaging hardware before they can be implemented in clinical practice and research studies.
Collapse
Affiliation(s)
- Richard Kijowski
- Department of Radiology, New York University Grossman School of Medicine, 660 First Avenue, 3Rd Floor, New York, NY, 10016, USA.
| | - Jan Fritz
- Department of Radiology, New York University Grossman School of Medicine, 660 First Avenue, 3Rd Floor, New York, NY, 10016, USA
| | - Cem M Deniz
- Department of Radiology, New York University Grossman School of Medicine, 660 First Avenue, 3Rd Floor, New York, NY, 10016, USA
| |
Collapse
|
10
|
Wirth W, Ladel C, Maschek S, Wisser A, Eckstein F, Roemer F. Quantitative measurement of cartilage morphology in osteoarthritis: current knowledge and future directions. Skeletal Radiol 2023; 52:2107-2122. [PMID: 36380243 PMCID: PMC10509082 DOI: 10.1007/s00256-022-04228-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/29/2022] [Accepted: 10/31/2022] [Indexed: 11/16/2022]
Abstract
Quantitative measures of cartilage morphology ("cartilage morphometry") extracted from high resolution 3D magnetic resonance imaging (MRI) sequences have been shown to be sensitive to osteoarthritis (OA)-related change and also to treatment interventions. Cartilage morphometry is therefore nowadays widely used as outcome measure for observational studies and randomized interventional clinical trials. The objective of this narrative review is to summarize the current status of cartilage morphometry in OA research, to provide insights into aspects relevant for the design of future studies and clinical trials, and to give an outlook on future developments. It covers the aspects related to the acquisition of MRIs suitable for cartilage morphometry, the analysis techniques needed for deriving quantitative measures from the MRIs, the quality assurance required for providing reliable cartilage measures, and the appropriate participant recruitment criteria for the enrichment of study cohorts with knees likely to show structural progression. Finally, it provides an overview over recent clinical trials that relied on cartilage morphometry as a structural outcome measure for evaluating the efficacy of disease-modifying OA drugs (DMOAD).
Collapse
Affiliation(s)
- Wolfgang Wirth
- Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy & Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Strubergasse 21, 5020 Salzburg, Austria
- Ludwig Boltzmann Inst. for Arthritis and Rehabilitation, Paracelsus Medical University Salzburg & Nuremberg, Salzburg, Austria
- Chondrometrics GmbH, Freilassing, Germany
| | | | - Susanne Maschek
- Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy & Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Strubergasse 21, 5020 Salzburg, Austria
- Chondrometrics GmbH, Freilassing, Germany
| | - Anna Wisser
- Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy & Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Strubergasse 21, 5020 Salzburg, Austria
- Ludwig Boltzmann Inst. for Arthritis and Rehabilitation, Paracelsus Medical University Salzburg & Nuremberg, Salzburg, Austria
- Chondrometrics GmbH, Freilassing, Germany
| | - Felix Eckstein
- Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy & Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Strubergasse 21, 5020 Salzburg, Austria
- Ludwig Boltzmann Inst. for Arthritis and Rehabilitation, Paracelsus Medical University Salzburg & Nuremberg, Salzburg, Austria
- Chondrometrics GmbH, Freilassing, Germany
| | - Frank Roemer
- Quantitative Imaging Center, Department of Radiology, Boston University School of Medicine, Boston, MA USA
- Department of Radiology, Universitätsklinikum Erlangen and Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| |
Collapse
|
11
|
Kurz B, Lange T, Voelker M, Hart ML, Rolauffs B. Articular Cartilage-From Basic Science Structural Imaging to Non-Invasive Clinical Quantitative Molecular Functional Information for AI Classification and Prediction. Int J Mol Sci 2023; 24:14974. [PMID: 37834422 PMCID: PMC10573252 DOI: 10.3390/ijms241914974] [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: 09/08/2023] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023] Open
Abstract
This review presents the changes that the imaging of articular cartilage has undergone throughout the last decades. It highlights that the expectation is no longer to image the structure and associated functions of articular cartilage but, instead, to devise methods for generating non-invasive, function-depicting images with quantitative information that is useful for detecting the early, pre-clinical stage of diseases such as primary or post-traumatic osteoarthritis (OA/PTOA). In this context, this review summarizes (a) the structure and function of articular cartilage as a molecular imaging target, (b) quantitative MRI for non-invasive assessment of articular cartilage composition, microstructure, and function with the current state of medical diagnostic imaging, (c), non-destructive imaging methods, (c) non-destructive quantitative articular cartilage live-imaging methods, (d) artificial intelligence (AI) classification of degeneration and prediction of OA progression, and (e) our contribution to this field, which is an AI-supported, non-destructive quantitative optical biopsy for early disease detection that operates on a digital tissue architectural fingerprint. Collectively, this review shows that articular cartilage imaging has undergone profound changes in the purpose and expectations for which cartilage imaging is used; the image is becoming an AI-usable biomarker with non-invasive quantitative functional information. This may aid in the development of translational diagnostic applications and preventive or early therapeutic interventions that are yet beyond our reach.
Collapse
Affiliation(s)
- Bodo Kurz
- Department of Anatomy, Christian-Albrechts-University, Otto-Hahn-Platz 8, 24118 Kiel, Germany
| | - Thomas Lange
- Medical Physics Department of Radiology, Faculty of Medicine, Medical Center—Albert-Ludwigs-University of Freiburg, 79085 Freiburg im Breisgau, Germany;
| | - Marita Voelker
- G.E.R.N. Research Center for Tissue Replacement, Regeneration & Neogenesis, Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center—Albert-Ludwigs-University of Freiburg, 79085 Freiburg im Breisgau, Germany; (M.V.); (M.L.H.)
| | - Melanie L. Hart
- G.E.R.N. Research Center for Tissue Replacement, Regeneration & Neogenesis, Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center—Albert-Ludwigs-University of Freiburg, 79085 Freiburg im Breisgau, Germany; (M.V.); (M.L.H.)
| | - Bernd Rolauffs
- G.E.R.N. Research Center for Tissue Replacement, Regeneration & Neogenesis, Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center—Albert-Ludwigs-University of Freiburg, 79085 Freiburg im Breisgau, Germany; (M.V.); (M.L.H.)
| |
Collapse
|
12
|
Kim S, Kosorok MR, Arbeeva L, Schwartz TA, Callahan LF, Golightly YM, Nelson AE, Allen KD. Precision Medicine-Based Machine Learning Analyses to Explore Optimal Exercise Therapies for Individuals With Knee Osteoarthritis: Random Forest-Informed Tree-Based Learning. J Rheumatol 2023; 50:1341-1345. [PMID: 37527856 PMCID: PMC10543458 DOI: 10.3899/jrheum.2022-1039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2023] [Indexed: 08/03/2023]
Abstract
OBJECTIVE We applied a precision medicine-based machine learning approach to discover underlying patient characteristics associated with differential improvement in knee osteoarthritis symptoms following standard physical therapy (PT), internet-based exercise training (IBET), and a usual care/wait list control condition. METHODS Participants (n = 303) were from the Physical Therapy vs Internet-Based Training for Patients with Knee Osteoarthritis trial. The primary outcome was the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total score at 12-month follow-up. Random forest-informed tree-based learning was applied to identify patient characteristics that were critical to improving outcomes, and patients with those features were grouped. RESULTS Age, BMI, and Brief Fear of Movement (BFOM) score, all at baseline, were identified as characteristics that effectively divided participants, creating 6 subgroups. Assigning treatments according to these models, compared to assigning a single best treatment to all patients, resulted in greater improvements of the average WOMAC at 12 months (P = 0.01). Key patterns were that IBET was the optimal treatment for patients of younger age and low BFOM, whereas PT was the optimal treatment for patients of older age, high BFOM, and BMI (kg/m2) between 26.3 and 37.2. CONCLUSION These results suggest that easily assessed patient characteristics including age, fear of movement, and BMI could be used to guide patients toward either home-based exercise or PT, though additional studies are needed to confirm these findings. (ClinicalTrials.gov: NCT02312713).
Collapse
Affiliation(s)
- Siyeon Kim
- S. Kim, MA, M.R. Kosorok, PhD, Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Michael R Kosorok
- S. Kim, MA, M.R. Kosorok, PhD, Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Liubov Arbeeva
- L. Arbeeva, MS, L.F. Callahan, PhD, Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Todd A Schwartz
- T.A. Schwartz, DrPH, Department of Biostatistics, Gillings School of Global Public Health, and Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Leigh F Callahan
- L. Arbeeva, MS, L.F. Callahan, PhD, Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Yvonne M Golightly
- Y.M. Golightly, PT, MS, PhD, Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, and College of Allied Health Professions, University of Nebraska Medical Center, Omaha, Nebraska, and Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Amanda E Nelson
- A.E. Nelson, MD, MSCR, Thurston Arthritis Research Center, and Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Kelli D Allen
- K.D. Allen, PhD, Thurston Arthritis Research Center, and Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, and Center of Innovation to Accelerate Discovery and Practice Transformation, Durham VA Medical Center, Durham, North Carolina, USA.
| |
Collapse
|
13
|
Domingues JG, Araujo DC, Costa-Silva L, Machado AMC, Machado LAC, Veloso AA, Barreto SM, Telles RW. Development of a convolutional neural network for diagnosing osteoarthritis, trained with knee radiographs from the ELSA-Brasil Musculoskeletal. Radiol Bras 2023; 56:248-254. [PMID: 38204901 PMCID: PMC10775807 DOI: 10.1590/0100-3984.2023.0020-en] [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: 03/01/2023] [Revised: 05/05/2023] [Accepted: 07/19/2023] [Indexed: 01/12/2024] Open
Abstract
Objective To develop a convolutional neural network (CNN) model, trained with the Brazilian "Estudo Longitudinal de Saúde do Adulto Musculoesquelético" (ELSA-Brasil MSK, Longitudinal Study of Adult Health, Musculoskeletal) baseline radiographic examinations, for the automated classification of knee osteoarthritis. Materials and Methods This was a cross-sectional study carried out with 5,660 baseline posteroanterior knee radiographs from the ELSA-Brasil MSK database (5,660 baseline posteroanterior knee radiographs). The examinations were interpreted by a radiologist with specific training, and the calibration was as established previously. Results The CNN presented an area under the receiver operating characteristic curve of 0.866 (95% CI: 0.842-0.882). The model can be optimized to achieve, not simultaneously, maximum values of 0.907 for accuracy, 0.938 for sensitivity, and 0.994 for specificity. Conclusion The proposed CNN can be used as a screening tool, reducing the total number of examinations evaluated by the radiologists of the study, and as a double-reading tool, contributing to the reduction of possible interpretation errors.
Collapse
Affiliation(s)
- Júlio Guerra Domingues
- Faculdade de Medicina da Universidade Federal de Minas Gerais
(UFMG), Belo Horizonte, MG, Brazil
| | - Daniella Castro Araujo
- Instituto de Ciências Exatas da Universidade Federal de
Minas Gerais (UFMG), Belo Horizonte, MG, Brazil
- Huna-AI, São Paulo, SP, Brazil
| | | | | | - Luciana Andrade Carneiro Machado
- Hospital das Clínicas da Universidade Federal de Minas
Gerais (UFMG)/Empresa Brasileira de Serviços Hospitalares (EBSERH), Belo
Horizonte, MG, Brazil
| | - Adriano Alonso Veloso
- Instituto de Ciências Exatas da Universidade Federal de
Minas Gerais (UFMG), Belo Horizonte, MG, Brazil
| | - Sandhi Maria Barreto
- Faculdade de Medicina da Universidade Federal de Minas Gerais
(UFMG), Belo Horizonte, MG, Brazil
- Hospital das Clínicas da Universidade Federal de Minas
Gerais (UFMG)/Empresa Brasileira de Serviços Hospitalares (EBSERH), Belo
Horizonte, MG, Brazil
| | - Rosa Weiss Telles
- Faculdade de Medicina da Universidade Federal de Minas Gerais
(UFMG), Belo Horizonte, MG, Brazil
- Hospital das Clínicas da Universidade Federal de Minas
Gerais (UFMG)/Empresa Brasileira de Serviços Hospitalares (EBSERH), Belo
Horizonte, MG, Brazil
| |
Collapse
|
14
|
Madrid-García A, Merino-Barbancho B, Rodríguez-González A, Fernández-Gutiérrez B, Rodríguez-Rodríguez L, Menasalvas-Ruiz E. Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature. Semin Arthritis Rheum 2023; 61:152213. [PMID: 37315379 DOI: 10.1016/j.semarthrit.2023.152213] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 06/16/2023]
Abstract
The major and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five- year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease identification and prediction, disease classification, patient stratification and disease subtype identification, disease progression and activity, treatment response, and predictors of outcomes. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed, are highlighted in a discussion. Therefore, the present review aims to characterise how researchers are applying data science techniques in the rheumatology medical field. The most immediate conclusions that can be drawn from this work are: multiple and novel data science techniques have been used in a wide range of rheumatic and musculoskeletal diseases including rare diseases; the sample size and the data type used are heterogeneous, and new technical approaches are expected to arrive in the short-middle term.
Collapse
Affiliation(s)
- Alfredo Madrid-García
- Grupo de Patología Musculoesquelética. Hospital Clínico San Carlos, Prof. Martin Lagos s/n, Madrid, 28040, Spain; Escuela Técnica Superior de Ingenieros de Telecomunicación. Universidad Politécnica de Madrid, Avenida Complutense, 30, Madrid, 28040, Spain.
| | - Beatriz Merino-Barbancho
- Escuela Técnica Superior de Ingenieros de Telecomunicación. Universidad Politécnica de Madrid, Avenida Complutense, 30, Madrid, 28040, Spain
| | | | - Benjamín Fernández-Gutiérrez
- Grupo de Patología Musculoesquelética. Hospital Clínico San Carlos, Prof. Martin Lagos s/n, Madrid, 28040, Spain
| | - Luis Rodríguez-Rodríguez
- Grupo de Patología Musculoesquelética. Hospital Clínico San Carlos, Prof. Martin Lagos s/n, Madrid, 28040, Spain
| | - Ernestina Menasalvas-Ruiz
- Centro de Tecnología Biomédica. Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, 28223, Spain
| |
Collapse
|
15
|
Westbury LD, Fuggle NR, Pereira D, Oka H, Yoshimura N, Oe N, Mahmoodi S, Niranjan M, Dennison EM, Cooper C. Machine learning as an adjunct to expert observation in classification of radiographic knee osteoarthritis: findings from the Hertfordshire Cohort Study. Aging Clin Exp Res 2023; 35:1449-1457. [PMID: 37202598 PMCID: PMC10284967 DOI: 10.1007/s40520-023-02428-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 04/26/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Osteoarthritis is the most prevalent type of arthritis. Many approaches exist for characterising radiographic knee OA, including machine learning (ML). AIMS To examine Kellgren and Lawrence (K&L) scores from ML and expert observation, minimum joint space and osteophyte in relation to pain and function. METHODS Participants from the Hertfordshire Cohort Study, comprising individuals born in Hertfordshire from 1931 to 1939, were analysed. Radiographs were assessed by clinicians and ML (convolutional neural networks) for K&L scoring. Medial minimum joint space and osteophyte area were ascertained using the knee OA computer-aided diagnosis (KOACAD) program. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was administered. Receiver operating characteristic analysis was implemented for minimum joint space, osteophyte, and observer- and ML-derived K&L scores in relation to pain (WOMAC pain score > 0) and impaired function (WOMAC function score > 0). RESULTS 359 participants (aged 71-80) were analysed. Among both sexes, discriminative capacity regarding pain and function was fairly high for observer-derived K&L scores [area under curve (AUC): 0.65 (95% CI 0.57, 0.72) to 0.70 (0.63, 0.77)]; results were similar among women for ML-derived K&L scores. Discriminative capacity was moderate among men for minimum joint space in relation to pain [0.60 (0.51, 0.67)] and function [0.62 (0.54, 0.69)]. AUC < 0.60 for other sex-specific associations. DISCUSSION Observer-derived K&L scores had higher discriminative capacity regarding pain and function compared to minimum joint space and osteophyte. Among women, discriminative capacity was similar for observer- and ML-derived K&L scores. CONCLUSION ML as an adjunct to expert observation for K&L scoring may be beneficial due to the efficiency and objectivity of ML.
Collapse
Affiliation(s)
- Leo D Westbury
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK
| | - Nicholas R Fuggle
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK
- The Alan Turing Institute, London, UK
| | - Diogo Pereira
- Departamento de Engenharia Electrotécnica e de Computadores, Faculdade de Ciências e Tecnologia, FCT/UNL, Universidade Nova de Lisboa, 2829-516, Caparica, Portugal
- Instituto de Telecomunicacoes, 1049-001, Lisbon, Portugal
| | - Hiroyuki Oka
- Department of Medical Research and Management for Musculoskeletal Pain, 22nd Century Medical and Research Center, The University of Tokyo, Tokyo, 113-8655, Japan
| | - Noriko Yoshimura
- Department of Preventive Medicine for Locomotive Organ Disorders, 22nd Century Medical and Research Center, The University of Tokyo, Tokyo, Japan
| | - Noriyuki Oe
- Department of Preventive Medicine for Locomotive Organ Disorders, 22nd Century Medical and Research Center, The University of Tokyo, Tokyo, Japan
| | - Sasan Mahmoodi
- Faculty of Engineering and Physical Sciences, Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Mahesan Niranjan
- Faculty of Engineering and Physical Sciences, Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Elaine M Dennison
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK.
- Victoria University of Wellington, Wellington, New Zealand.
| | - Cyrus Cooper
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| |
Collapse
|
16
|
Lu Y, Ganz ML, Robinson RL, Zagar AJ, Okala S, Hartrick CT, Johnston B, Dorling P, Slim M, Thakkar S, Berger A. Use of electronic health data to identify patients with moderate-to-severe osteoarthritis of the hip and/or knee and inadequate response to pain medications. BMC Med Res Methodol 2023; 23:156. [PMID: 37391751 PMCID: PMC10311749 DOI: 10.1186/s12874-023-01964-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 06/06/2023] [Indexed: 07/02/2023] Open
Abstract
BACKGROUND No algorithms exist to identify important osteoarthritis (OA) patient subgroups (i.e., moderate-to-severe disease, inadequate response to pain treatments) in electronic healthcare data, possibly due to the complexity in defining these characteristics as well as the lack of relevant measures in these data sources. We developed and validated algorithms intended for use with claims and/or electronic medical records (EMR) to identify these patient subgroups. METHODS We obtained claims, EMR, and chart data from two integrated delivery networks. Chart data were used to identify the presence or absence of the three relevant OA-related characteristics (OA of the hip and/or knee, moderate-to-severe disease, inadequate/intolerable response to at least two pain-related medications); the resulting classification served as the benchmark for algorithm validation. We developed two sets of case-identification algorithms: one based on a literature review and clinical input (predefined algorithms), and another using machine learning (ML) methods (logistic regression, classification and regression tree, random forest). Patient classifications based on these algorithms were compared and validated against the chart data. RESULTS We sampled and analyzed 571 adult patients, of whom 519 had OA of hip and/or knee, 489 had moderate-to-severe OA, and 431 had inadequate response to at least two pain medications. Individual predefined algorithms had high positive predictive values (all PPVs ≥ 0.83) for identifying each of these OA characteristics, but low negative predictive values (all NPVs between 0.16-0.54) and sometimes low sensitivity; their sensitivity and specificity for identifying patients with all three characteristics was 0.95 and 0.26, respectively (NPV 0.65, PPV 0.78, accuracy 0.77). ML-derived algorithms performed better in identifying this patient subgroup (range: sensitivity 0.77-0.86, specificity 0.66-0.75, PPV 0.88-0.92, NPV 0.47-0.62, accuracy 0.75-0.83). CONCLUSIONS Predefined algorithms adequately identified OA characteristics of interest, but more sophisticated ML-based methods better differentiated between levels of disease severity and identified patients with inadequate response to analgesics. The ML methods performed well, yielding high PPV, NPV, sensitivity, specificity, and accuracy using either claims or EMR data. Use of these algorithms may expand the ability of real-world data to address questions of interest in this underserved patient population.
Collapse
|
17
|
Alshamrani HA, Rashid M, Alshamrani SS, Alshehri AHD. Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach. Healthcare (Basel) 2023; 11:healthcare11091206. [PMID: 37174748 PMCID: PMC10178688 DOI: 10.3390/healthcare11091206] [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] [Received: 03/02/2023] [Revised: 04/20/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
Knee osteoarthritis is a challenging problem affecting many adults around the world. There are currently no medications that cure knee osteoarthritis. The only way to control the progression of knee osteoarthritis is early detection. Currently, X-ray imaging is a central technique used for the prediction of osteoarthritis. However, the manual X-ray technique is prone to errors due to the lack of expertise of radiologists. Recent studies have described the use of automated systems based on machine learning for the effective prediction of osteoarthritis from X-ray images. However, most of these techniques still need to achieve higher predictive accuracy to detect osteoarthritis at an early stage. This paper suggests a method with higher predictive accuracy that can be employed in the real world for the early detection of knee osteoarthritis. In this paper, we suggest the use of transfer learning models based on sequential convolutional neural networks (CNNs), Visual Geometry Group 16 (VGG-16), and Residual Neural Network 50 (ResNet-50) for the early detection of osteoarthritis from knee X-ray images. In our analysis, we found that all the suggested models achieved a higher level of predictive accuracy, greater than 90%, in detecting osteoarthritis. However, the best-performing model was the pretrained VGG-16 model, which achieved a training accuracy of 99% and a testing accuracy of 92%.
Collapse
Affiliation(s)
- Hassan A Alshamrani
- Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran 11001, Saudi Arabia
| | - Mamoon Rashid
- Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune 411048, India
- Research Center of Excellence for Health Informatics, Vishwakarma University, Pune 411048, India
| | - Sultan S Alshamrani
- Department of Information Technology, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia
| | - Ali H D Alshehri
- Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran 11001, Saudi Arabia
| |
Collapse
|
18
|
Xuan A, Chen H, Chen T, Li J, Lu S, Fan T, Zeng D, Wen Z, Ma J, Hunter D, Ding C, Zhu Z. The application of machine learning in early diagnosis of osteoarthritis: a narrative review. Ther Adv Musculoskelet Dis 2023; 15:1759720X231158198. [PMID: 36937823 PMCID: PMC10017946 DOI: 10.1177/1759720x231158198] [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] [Received: 09/02/2022] [Accepted: 02/01/2023] [Indexed: 03/16/2023] Open
Abstract
Osteoarthritis (OA) is the commonest musculoskeletal disease worldwide, with an increasing prevalence due to aging. It causes joint pain and disability, decreased quality of life, and a huge burden on healthcare services for society. However, the current main diagnostic methods are not suitable for early diagnosing patients of OA. The use of machine learning (ML) in OA diagnosis has increased dramatically in the past few years. Hence, in this review article, we describe the research progress in the application of ML in the early diagnosis of OA, discuss the current trends and limitations of ML approaches, and propose future research priorities to apply the tools in the field of OA. Accurate ML-based predictive models with imaging techniques that are sensitive to early changes in OA ahead of the emergence of clinical features are expected to address the current dilemma. The diagnostic ability of the fusion model that combines multidimensional information makes patient-specific early diagnosis and prognosis estimation of OA possible in the future.
Collapse
Affiliation(s)
| | | | - Tianyu Chen
- Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jia Li
- Division of Orthopaedic Surgery, Department of Orthopaedics, Nafang Hospital, Southern Medical University, Guangzhou, China
| | - Shilong Lu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Tianxiang Fan
- Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Dong Zeng
- College of Automation Science and Engineering, South China University of Technology, Guangzhou, China
| | - Zhibo Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - David Hunter
- Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Department of Rheumatology, Royal North Shore Hospital and Institute of Bone and Joint Research, Kolling Institute, University of Sydney, Sydney, NSW, Australia
| | | | | |
Collapse
|
19
|
Hayashi D, Roemer FW, Link T, Li X, Kogan F, Segal NA, Omoumi P, Guermazi A. Latest advancements in imaging techniques in OA. Ther Adv Musculoskelet Dis 2022; 14:1759720X221146621. [PMID: 36601087 PMCID: PMC9806406 DOI: 10.1177/1759720x221146621] [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: 08/27/2022] [Accepted: 12/05/2022] [Indexed: 12/28/2022] Open
Abstract
The osteoarthritis (OA) research community has been advocating a shift from radiography-based screening criteria and outcome measures in OA clinical trials to a magnetic resonance imaging (MRI)-based definition of eligibility and endpoint. For conventional morphological MRI, various semiquantitative evaluation tools are available. We have lately witnessed a remarkable technological advance in MRI techniques, including compositional/physiologic imaging and automated quantitative analyses of articular and periarticular structures. More recently, additional technologies were introduced, including positron emission tomography (PET)-MRI, weight-bearing computed tomography (CT), photon-counting spectral CT, shear wave elastography, contrast-enhanced ultrasound, multiscale X-ray phase contrast imaging, and spectroscopic photoacoustic imaging of cartilage. On top of these, we now live in an era in which artificial intelligence is increasingly utilized in medicine. Osteoarthritis imaging is no exception. Successful implementation of artificial intelligence (AI) will hopefully improve the workflow of radiologists, as well as the level of precision and reproducibility in the interpretation of images.
Collapse
Affiliation(s)
- Daichi Hayashi
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA,Department of Radiology, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA
| | - Frank W. Roemer
- Department of Radiology, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA,Department of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Thomas Link
- Department of Radiology, University of California San Francisco, San Franciso, CA, USA
| | - Xiaojuan Li
- Department of Radiology, Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Neil A. Segal
- Department of Rehabilitation Medicine, The University of Kansas, Kansas City, KS, USA
| | - Patrick Omoumi
- Department of Radiology, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | | |
Collapse
|