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Nair A, Alagha MA, Cobb J, Jones G. Assessing the Value of Imaging Data in Machine Learning Models to Predict Patient-Reported Outcome Measures in Knee Osteoarthritis Patients. Bioengineering (Basel) 2024; 11:824. [PMID: 39199782 PMCID: PMC11351307 DOI: 10.3390/bioengineering11080824] [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: 07/04/2024] [Revised: 07/28/2024] [Accepted: 07/30/2024] [Indexed: 09/01/2024] Open
Abstract
Knee osteoarthritis (OA) affects over 650 million patients worldwide. Total knee replacement is aimed at end-stage OA to relieve symptoms of pain, stiffness and reduced mobility. However, the role of imaging modalities in monitoring symptomatic disease progression remains unclear. This study aimed to compare machine learning (ML) models, with and without imaging features, in predicting the two-year Western Ontario and McMaster Universities Arthritis Index (WOMAC) score for knee OA patients. We included 2408 patients from the Osteoarthritis Initiative (OAI) database, with 629 patients from the Multicenter Osteoarthritis Study (MOST) database. The clinical dataset included 18 clinical features, while the imaging dataset contained an additional 10 imaging features. Minimal Clinically Important Difference (MCID) was set to 24, reflecting meaningful physical impairment. Clinical and imaging dataset models produced similar area under curve (AUC) scores, highlighting low differences in performance AUC < 0.025). For both clinical and imaging datasets, Gradient Boosting Machine (GBM) models performed the best in the external validation, with a clinically acceptable AUC of 0.734 (95% CI 0.687-0.781) and 0.747 (95% CI 0.701-0.792), respectively. The five features identified included educational background, family history of osteoarthritis, co-morbidities, use of osteoporosis medications and previous knee procedures. This is the first study to demonstrate that ML models achieve comparable performance with and without imaging features.
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Affiliation(s)
- Abhinav Nair
- 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, London School of Economics and Political Science, London, UK
| | - Justin Cobb
- 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
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Nurmirinta TAT, Turunen MJ, Korhonen RK, Tohka J, Liukkonen MK, Mononen ME. Two-Stage Classification of Future Knee Osteoarthritis Severity After 8 Years Using MRI: Data from the Osteoarthritis Initiative. Ann Biomed Eng 2024:10.1007/s10439-024-03578-x. [PMID: 38980544 DOI: 10.1007/s10439-024-03578-x] [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: 12/04/2023] [Accepted: 06/28/2024] [Indexed: 07/10/2024]
Abstract
Currently, there are no methods or tools available in clinical practice for classifying future knee osteoarthritis (KOA). In this study, we aimed to fill this gap by classifying future KOA into three severity grades: KL01 (healthy), KL2 (moderate), and KL34 (severe) based on the Kellgren-Lawrance scale. Due to the complex nature of multiclass classification, we used a two-stage method, which separates the classification task into two binary classifications (KL01 vs. KL234 in the first stage and KL2 vs. KL34 in the second stage). Our machine learning (ML) model used two Balanced Random Forest algorithms and was trained with gender, age, height, weight, and quantitative knee morphology obtained from magnetic resonance imaging. Our training dataset comprised longitudinal 8-year follow-up data of 1213 knees from the Osteoarthritis Initiative. Through extensive experimentation with various feature combinations, we identified KL baseline and weight as the most essential features, while gender surprisingly proved to be one of the least influential feature. Our best classification model generated a weighted F1 score of 79.0% and a balanced accuracy of 65.9%. The area under the receiver operating characteristic curve was 83.0% for healthy (KL01) versus moderate (KL2) or severe (KL34) KOA patients and 86.6% for moderate (KL2) versus severe (KL34) KOA patients. We found a statistically significant difference in performance between our two-stage classification model and the traditional single-stage classification model. These findings demonstrate the encouraging results of our two-stage classification model for multiclass KOA severity classification, suggesting its potential application in clinical settings in future.
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Affiliation(s)
- Teemu A T Nurmirinta
- Department of Technical Physics, University of Eastern Finland, POB 1627, FI-70211, Kuopio, Finland.
- Diagnostic Imaging Centre, Kuopio University Hospital, The Wellbeing Services County of North Savo, Kuopio, Finland.
| | - Mikael J Turunen
- Department of Technical Physics, University of Eastern Finland, POB 1627, FI-70211, Kuopio, Finland
- Science Service Center, Kuopio University Hospital, The Wellbeing Services County of North Savo, Kuopio, Finland
| | - Rami K Korhonen
- Department of Technical Physics, University of Eastern Finland, POB 1627, FI-70211, Kuopio, Finland
| | - Jussi Tohka
- AI Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Mimmi K Liukkonen
- Diagnostic Imaging Centre, Kuopio University Hospital, The Wellbeing Services County of North Savo, Kuopio, Finland
| | - Mika E Mononen
- Department of Technical Physics, University of Eastern Finland, POB 1627, FI-70211, Kuopio, Finland
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Raza A, Sekiguchi Y, Yaguchi H, Honda K, Fukushi K, Huang C, Ihara K, Nozaki Y, Nakahara K, Izumi SI, Ebihara S. Gait classification of knee osteoarthritis patients using shoe-embedded internal measurement units sensor. Clin Biomech (Bristol, Avon) 2024; 117:106285. [PMID: 38901396 DOI: 10.1016/j.clinbiomech.2024.106285] [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: 01/09/2024] [Revised: 05/14/2024] [Accepted: 06/03/2024] [Indexed: 06/22/2024]
Abstract
BACKGROUND Knee osteoarthritis negatively affects the gait of patients, especially that of elderly people. However, the assessment of wearable sensors in knee osteoarthritis patients has been under-researched. During clinical assessments, patients may change their gait patterns under the placebo effect, whereas wearable sensors can be used in any environment. METHODS Sixty patients with knee osteoarthritis and 20 control subjects were included in the study. Wearing shoes with an IMU sensor embedded in the insoles, the participants were required to walk along a walkway. The sensor data were collected during the gait. To discriminate between healthy and knee osteoarthritis patients and to classify different subgroups of knee osteoarthritis patients (patients scheduled for surgery vs. patients not scheduled for surgery; bilateral knee osteoarthritis diagnosis vs. unilateral knee osteoarthritis diagnosis), we used a machine learning approach called the support vector machine. A total of 88 features were extracted and used for classification. FINDINGS The patients vs. healthy participants were classified with 71% accuracy, 85% sensitivity, and 56% specificity. The "patients scheduled for surgery" vs. "patients not scheduled for surgery" were classified with 83% accuracy, 83% sensitivity, and 81% specificity. The bilateral knee osteoarthritis diagnosis vs. unilateral knee osteoarthritis diagnosis was classified with 81% accuracy, 75% sensitivity, and 79% specificity. INTERPRETATION Gait analysis using wearable sensors and machine learning can discriminate between healthy and knee osteoarthritis patients and classify different subgroups with reasonable accuracy, sensitivity, and specificity. The proposed approach requires no complex gait factors and is not limited to controlled laboratory settings.
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Affiliation(s)
- Ahmed Raza
- Department of Physical Medicine and Rehabilitation, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan.
| | - Yusuke Sekiguchi
- Department of Physical Medicine and Rehabilitation, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan.
| | - Haruki Yaguchi
- Department of Physical Medicine and Rehabilitation, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan
| | - Keita Honda
- Department of Physical Medicine and Rehabilitation, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan
| | - Kenichiro Fukushi
- Biometrics Research Labs, NEC Corporation, Hinode 1131, Abiko, Chiba 270-1174, Japan
| | - Chenhui Huang
- Biometrics Research Labs, NEC Corporation, Hinode 1131, Abiko, Chiba 270-1174, Japan
| | - Kazuki Ihara
- Biometrics Research Labs, NEC Corporation, Hinode 1131, Abiko, Chiba 270-1174, Japan
| | - Yoshitaka Nozaki
- Biometrics Research Labs, NEC Corporation, Hinode 1131, Abiko, Chiba 270-1174, Japan
| | - Kentaro Nakahara
- Biometrics Research Labs, NEC Corporation, Hinode 1131, Abiko, Chiba 270-1174, Japan
| | - Shin-Ichi Izumi
- Department of Physical Medicine and Rehabilitation, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan; Graduate School of Biomedical Engineering, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan
| | - Satoru Ebihara
- Department of Internal Medicine & Rehabilitation Science, Disability Sciences, Tohoku University Graduate School of Medicine,1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan
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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 PMCID: PMC11208546 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.
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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.
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5
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Tong B, Chen H, Wang C, Zeng W, Li D, Liu P, Liu M, Jin X, Shang S. Clinical prediction models for knee pain in patients with knee osteoarthritis: a systematic review. Skeletal Radiol 2024; 53:1045-1059. [PMID: 38265451 DOI: 10.1007/s00256-024-04590-x] [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: 10/14/2023] [Revised: 12/15/2023] [Accepted: 12/15/2023] [Indexed: 01/25/2024]
Abstract
OBJECTIVE To identify and describe existing models for predicting knee pain in patients with knee osteoarthritis. METHODS The electronic databases PubMed, EMBASE, CINAHL, Web of Science, and Cochrane Library were searched from their inception to May 2023 for any studies to develop and validate a prediction model for predicting knee pain in patients with knee osteoarthritis. Two reviewers independently screened titles, abstracts, and full-text qualifications, and extracted data. Risk of bias was assessed using the PROBAST. Data extraction of eligible articles was extracted by a data extraction form based on CHARMS. The quality of evidence was graded according to GRADE. The results were summarized with descriptive statistics. RESULTS The search identified 2693 records. Sixteen articles reporting on 26 prediction models were included targeting occurrence (n = 9), others (n = 7), progression (n = 5), persistent (n = 2), incident (n = 1), frequent (n = 1), and flares (n = 1) of knee pain. Most of the studies (94%) were at high risk of bias. Model discrimination was assessed by the AUROC ranging from 0.62 to 0.81. The most common predictors were age, BMI, gender, baseline pain, and joint space width. Only frequent knee pain had a moderate quality of evidence; all other types of knee pain had a low quality of evidence. CONCLUSION There are many prediction models for knee pain in patients with knee osteoarthritis that do show promise. However, the clinical extensibility, applicability, and interpretability of predictive tools should be considered during model development.
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Affiliation(s)
- Beibei Tong
- School of Nursing, Peking University, Beijing, China
| | - Hongbo Chen
- Nursing Department of Peking University Third Hospital, Beijing, China
| | - Cui Wang
- School of Nursing, Peking University, Beijing, China
| | - Wen Zeng
- School of Nursing, Peking University, Beijing, China
| | - Dan Li
- School of Nursing, Peking University, Beijing, China
| | - Peiyuan Liu
- School of Nursing, Peking University, Beijing, China
| | - Ming Liu
- Macao Polytechnic University, Macao, China
| | | | - Shaomei Shang
- School of Nursing, Peking University, Beijing, China.
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Gatti AA, Blankemeier L, Van Veen D, Hargreaves B, Delp SL, Gold GE, Kogan F, Chaudhari AS. ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.06.24306965. [PMID: 38766040 PMCID: PMC11100941 DOI: 10.1101/2024.05.06.24306965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Analyzing anatomic shapes of tissues and organs is pivotal for accurate disease diagnostics and clinical decision-making. One prominent disease that depends on anatomic shape analysis is osteoarthritis, which affects 30 million Americans. To advance osteoarthritis diagnostics and prognostics, we introduce ShapeMed-Knee, a 3D shape dataset with 9,376 high-resolution, medical-imaging-based 3D shapes of both femur bone and cartilage. Besides data, ShapeMed-Knee includes two benchmarks for assessing reconstruction accuracy and five clinical prediction tasks that assess the utility of learned shape representations. Leveraging ShapeMed-Knee, we develop and evaluate a novel hybrid explicit-implicit neural shape model which achieves up to 40% better reconstruction accuracy than a statistical shape model and implicit neural shape model. Our hybrid models achieve state-of-the-art performance for preserving cartilage biomarkers; they're also the first models to successfully predict localized structural features of osteoarthritis, outperforming shape models and convolutional neural networks applied to raw magnetic resonance images and segmentations. The ShapeMed-Knee dataset provides medical evaluations to reconstruct multiple anatomic surfaces and embed meaningful disease-specific information. ShapeMed-Knee reduces barriers to applying 3D modeling in medicine, and our benchmarks highlight that advancements in 3D modeling can enhance the diagnosis and risk stratification for complex diseases. The dataset, code, and benchmarks will be made freely accessible.
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Affiliation(s)
- Anthony A Gatti
- Department of Radiology at Stanford University, Stanford, CA, 94305, USA
| | - Louis Blankemeier
- Department of Electrical Engineering at Stanford University, Stanford, CA, 94305, USA
| | - Dave Van Veen
- Department of Electrical Engineering at Stanford University, Stanford, CA, 94305, USA
| | - Brian Hargreaves
- Department of Radiology at Stanford University, Stanford, CA, 94305, USA
| | - Scott L Delp
- Department of Bioengineering at Stanford University, Stanford, CA, 94305, USA
| | - Garry E Gold
- Department of Radiology at Stanford University, Stanford, CA, 94305, USA
| | - Feliks Kogan
- Department of Radiology at Stanford University, Stanford, CA, 94305, USA
| | - Akshay S Chaudhari
- Department of Radiology at Stanford University, Stanford, CA, 94305, USA
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Mohammadi S, Salehi MA, Jahanshahi A, Shahrabi Farahani M, Zakavi SS, Behrouzieh S, Gouravani M, Guermazi A. Artificial intelligence in osteoarthritis detection: A systematic review and meta-analysis. Osteoarthritis Cartilage 2024; 32:241-253. [PMID: 37863421 DOI: 10.1016/j.joca.2023.09.011] [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: 04/12/2023] [Revised: 08/11/2023] [Accepted: 09/27/2023] [Indexed: 10/22/2023]
Abstract
OBJECTIVES As an increasing number of studies apply artificial intelligence (AI) algorithms in osteoarthritis (OA) detection, we performed a systematic review and meta-analysis to pool the data on diagnostic performance metrics of AI, and to compare them with clinicians' performance. MATERIALS AND METHODS A search in PubMed and Scopus was performed to find studies published up to April 2022 that evaluated and/or validated an AI algorithm for the detection or classification of OA. We performed a meta-analysis to pool the data on the metrics of diagnostic performance. Subgroup analysis based on the involved joint and meta-regression based on multiple parameters were performed to find potential sources of heterogeneity. The risk of bias was assessed using Prediction Model Study Risk of Bias Assessment Tool reporting guidelines. RESULTS Of the 61 studies included, 27 studies with 91 contingency tables provided sufficient data to enter the meta-analysis. The pooled sensitivities for AI algorithms and clinicians on internal validation test sets were 88% (95% confidence interval [CI]: 86,91) and 80% (95% CI: 68,88) and pooled specificities were 81% (95% CI: 75,85) and 79% (95% CI: 80,85), respectively. At external validation, the pooled sensitivity and specificity for AI algorithms were 94% (95% CI: 90,97) and 91% (95% CI: 77,97), respectively. CONCLUSION Although the results of this meta-analysis should be interpreted with caution due to the potential pitfalls in the included studies, the promising role of AI as a diagnostic adjunct to radiologists is indisputable.
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Affiliation(s)
- Soheil Mohammadi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
| | | | - Ali Jahanshahi
- Faculty of Medicine, Guilan University of Medical Sciences, Rasht, Iran.
| | | | - Seyed Sina Zakavi
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
| | - Sadra Behrouzieh
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
| | - Mahdi Gouravani
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
| | - Ali Guermazi
- Department of Radiology, VA Boston Healthcare System, Boston University School of Medicine, Boston, MA, USA.
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8
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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.
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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
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Gkantzios A, Kokkotis C, Tsiptsios D, Moustakidis S, Gkartzonika E, Avramidis T, Tripsianis G, Iliopoulos I, Aggelousis N, Vadikolias K. From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning. J Pers Med 2023; 13:1375. [PMID: 37763143 PMCID: PMC10532952 DOI: 10.3390/jpm13091375] [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: 08/10/2023] [Revised: 09/03/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
As a result of social progress and improved living conditions, which have contributed to a prolonged life expectancy, the prevalence of strokes has increased and has become a significant phenomenon. Despite the available stroke treatment options, patients frequently suffer from significant disability after a stroke. Initial stroke severity is a significant predictor of functional dependence and mortality following an acute stroke. The current study aims to collect and analyze data from the hyperacute and acute phases of stroke, as well as from the medical history of the patients, in order to develop an explainable machine learning model for predicting stroke-related neurological deficits at discharge, as measured by the National Institutes of Health Stroke Scale (NIHSS). More specifically, we approached the data as a binary task problem: improvement of NIHSS progression vs. worsening of NIHSS progression at discharge, using baseline data within the first 72 h. For feature selection, a genetic algorithm was applied. Using various classifiers, we found that the best scores were achieved from the Random Forest (RF) classifier at the 15 most informative biomarkers and parameters for the binary task of the prediction of NIHSS score progression. RF achieved 91.13% accuracy, 91.13% recall, 90.89% precision, 91.00% f1-score, 8.87% FNrate and 4.59% FPrate. Those biomarkers are: age, gender, NIHSS upon admission, intubation, history of hypertension and smoking, the initial diagnosis of hypertension, diabetes, dyslipidemia and atrial fibrillation, high-density lipoprotein (HDL) levels, stroke localization, systolic blood pressure levels, as well as erythrocyte sedimentation rate (ESR) levels upon admission and the onset of respiratory infection. The SHapley Additive exPlanations (SHAP) model interpreted the impact of the selected features on the model output. Our findings suggest that the aforementioned variables may play a significant role in determining stroke patients' NIHSS progression from the time of admission until their discharge.
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Affiliation(s)
- Aimilios Gkantzios
- Department of Neurology, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (D.T.); (I.I.); (K.V.)
- Department of Neurology, Korgialeneio—Benakeio “Hellenic Red Cross” General Hospital of Athens, 11526 Athens, Greece;
| | - Christos Kokkotis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece; (C.K.); (S.M.); (N.A.)
| | - Dimitrios Tsiptsios
- Department of Neurology, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (D.T.); (I.I.); (K.V.)
| | - Serafeim Moustakidis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece; (C.K.); (S.M.); (N.A.)
| | - Elena Gkartzonika
- School of Philosophy, University of Ioannina, 45110 Ioannina, Greece;
| | - Theodoros Avramidis
- Department of Neurology, Korgialeneio—Benakeio “Hellenic Red Cross” General Hospital of Athens, 11526 Athens, Greece;
| | - Gregory Tripsianis
- Laboratory of Medical Statistics, Democritus University of Thrace, 68100 Alexandroupolis, Greece;
| | - Ioannis Iliopoulos
- Department of Neurology, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (D.T.); (I.I.); (K.V.)
| | - Nikolaos Aggelousis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece; (C.K.); (S.M.); (N.A.)
| | - Konstantinos Vadikolias
- Department of Neurology, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (D.T.); (I.I.); (K.V.)
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10
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Howard T, Iyengar KP, Vaishya R, Ahluwalia R. High-fidelity virtual reality simulation training in enhancing competency assessment in orthopaedic training. Br J Hosp Med (Lond) 2023; 84:1-8. [PMID: 37769263 DOI: 10.12968/hmed.2022.0360] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
Surgical competence is the ability to successfully apply academic knowledge, clinical skills and professional behaviour to inpatient care. Along with ensuring patient safety, the ability to communicate effectively, collaborative teamwork and probity, and achieving satisfactory competencies form the fundamental principles of good medical practice. Current strategies to develop surgical competencies include a range of formative and summative assessments. The cancellation of traditional face-to-face meetings and training opportunities during the COVID-19 pandemic had a profound impact on the delivery of medical education and opportunities to achieve surgical competencies. Simulation learning has been used since before the pandemic to deliver surgical training across all grades and specialities, including orthopaedic surgery. Simulation-based training provides a safe, controlled environment to develop skill acquisition. Simulated surgery using virtual reality has evolved following developments in software and hardware. This article explores the role of high-fidelity virtual reality simulation to assess competencies in orthopaedic training in the post-COVID-19 era, and examines whether simulation could be used within the curriculum to augment and improve training.
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Affiliation(s)
- Theodore Howard
- Department of Trauma and Orthopaedics, King's College Hospital, London, UK
- Department of Trauma and Orthopaedics, Imperial College London, London, UK
| | - Karthikeyan P Iyengar
- Department of Trauma and Orthopaedics, Southport and Ormskirk Hospital NHS Trust, Southport, UK
| | - Raju Vaishya
- Department of Orthopaedics, Indraprastha Apollo Hospital, New Delhi, India
| | - Raju Ahluwalia
- Department of Trauma and Orthopaedics, King's College Hospital, London, UK
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11
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Cha Y, Seo SH, Kim JT, Kim JW, Lee SY, Yoo JI. Osteoporosis Feature Selection and Risk Prediction Model by Machine Learning Using a Cross-Sectional Database. J Bone Metab 2023; 30:263-273. [PMID: 37718904 PMCID: PMC10509024 DOI: 10.11005/jbm.2023.30.3.263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/03/2023] [Accepted: 07/19/2023] [Indexed: 09/19/2023] Open
Abstract
BACKGROUND The purpose of this study was to verify the accuracy and validity of using machine learning (ML) to select risk factors, to discriminate differences in feature selection by ML between men and women, and to develop predictive models for patients with osteoporosis in a big database. METHODS The data on 968 observed features from a total of 3,484 the Korea National Health and Nutrition Examination Survey participants were collected. To find preliminary features that were well-related to osteoporosis, logistic regression, random forest, gradient boosting, adaptive boosting, and support vector machine were used. RESULTS In osteoporosis feature selection by 5 ML models in this study, the most selected variables as risk factors in men and women were body mass index, monthly alcohol consumption, and dietary surveys. However, differences between men and women in osteoporosis feature selection by ML models were age, smoking, and blood glucose level. The receiver operating characteristic (ROC) analysis revealed that the area under the ROC curve for each ML model was not significantly different for either gender. CONCLUSIONS ML performed a feature selection of osteoporosis, considering hidden differences between men and women. The present study considers the preprocessing of input data and the feature selection process as well as the ML technique to be important factors for the accuracy of the osteoporosis prediction model.
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Affiliation(s)
- Yonghan Cha
- Department of Orthopaedic Surgery, Daejeon Eulji Medical Center, Eulji University School of Medicine, Daejeon,
Korea
| | - Sung Hyo Seo
- Department of Biomedical Research Institute, Gyeongsang National University Hospital, Jinju,
Korea
| | - Jung-Taek Kim
- Department of Orthopedic Surgery, Ajou Medical Center, Ajou University School of Medicine, Suwon,
Korea
| | - Jin-Woo Kim
- Department of Orthopaedic Surgery, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul,
Korea
| | - Sang-Yeob Lee
- Department of Biomedical Research Institute, Gyeongsang National University Hospital, Jinju,
Korea
| | - Jun-Il Yoo
- Department of Orthopaedic Surgery, Inha University Hospital, Inha University School of Medicine, Incheon,
Korea
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12
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Plakias S, Moustakidis S, Kokkotis C, Papalexi M, Tsatalas T, Giakas G, Tsaopoulos D. Identifying Soccer Players' Playing Styles: A Systematic Review. J Funct Morphol Kinesiol 2023; 8:104. [PMID: 37606399 PMCID: PMC10443261 DOI: 10.3390/jfmk8030104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/20/2023] [Accepted: 07/25/2023] [Indexed: 08/23/2023] Open
Abstract
Identifying playing styles in football is highly valuable for achieving effective performance analysis. While there is extensive research on team styles, studies on individual player styles are still in their early stages. Thus, the aim of this systematic review was to provide a comprehensive overview of the existing literature on player styles and identify research areas required for further development, offering new directions for future research. Following the PRISMA guidelines for systematic reviews, we conducted a search using a specific strategy across four databases (PubMed, Scopus, Web of Science, and SPORTDiscus). Inclusion and exclusion criteria were applied to the initial search results, ultimately identifying twelve studies suitable for inclusion in this review. Through thematic analysis and qualitative evaluation of these studies, several key findings emerged: (a) a lack of a structured theoretical framework for player styles based on their positions within the team formation, (b) absence of studies investigating the influence of contextual variables on player styles, (c) methodological deficiencies observed in the reviewed studies, and (d) disparity in the objectives of sports science and data science studies. By identifying these gaps in the literature and presenting a structured framework for player styles (based on the compilation of all reported styles from the reviewed studies), this review aims to assist team stakeholders and provide guidance for future research endeavors.
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Affiliation(s)
- Spyridon Plakias
- Department of Physical Education and Sport Science, University of Thessaly, 38221 Trikala, Greece; (S.P.); (T.T.); (G.G.)
| | | | - Christos Kokkotis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece;
| | - Marina Papalexi
- Department of Operations, Technology, Events and Hospitality Management, Manchester Metropolitan University, Oxford Road, Manchester M15 6BH, UK;
| | - Themistoklis Tsatalas
- Department of Physical Education and Sport Science, University of Thessaly, 38221 Trikala, Greece; (S.P.); (T.T.); (G.G.)
| | - Giannis Giakas
- Department of Physical Education and Sport Science, University of Thessaly, 38221 Trikala, Greece; (S.P.); (T.T.); (G.G.)
| | - Dimitrios Tsaopoulos
- Center for Research and Technology Hellas, Institute for Bio-Economy & Agri-Technology, 60361 Volos, Greece;
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13
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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: 2] [Impact Index Per Article: 2.0] [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.
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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
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14
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Kwolek K, Grzelecki D, Kwolek K, Marczak D, Kowalczewski J, Tyrakowski M. Automated patellar height assessment on high-resolution radiographs with a novel deep learning-based approach. World J Orthop 2023; 14:387-398. [PMID: 37377994 PMCID: PMC10292056 DOI: 10.5312/wjo.v14.i6.387] [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: 02/15/2023] [Revised: 04/06/2023] [Accepted: 05/06/2023] [Indexed: 06/19/2023] Open
Abstract
BACKGROUND Artificial intelligence and deep learning have shown promising results in medical imaging and interpreting radiographs. Moreover, medical community shows a gaining interest in automating routine diagnostics issues and orthopedic measurements.
AIM To verify the accuracy of automated patellar height assessment using deep learning-based bone segmentation and detection approach on high resolution radiographs.
METHODS 218 Lateral knee radiographs were included in the analysis. 82 radiographs were utilized for training and 10 other radiographs for validation of a U-Net neural network to achieve required Dice score. 92 other radiographs were used for automatic (U-Net) and manual measurements of the patellar height, quantified by Caton-Deschamps (CD) and Blackburne-Peel (BP) indexes. The detection of required bones regions on high-resolution images was done using a You Only Look Once (YOLO) neural network. The agreement between manual and automatic measurements was calculated using the interclass correlation coefficient (ICC) and the standard error for single measurement (SEM). To check U-Net's generalization the segmentation accuracy on the test set was also calculated.
RESULTS Proximal tibia and patella was segmented with accuracy 95.9% (Dice score) by U-Net neural network on lateral knee subimages automatically detected by the YOLO network (mean Average Precision mAP greater than 0.96). The mean values of CD and BP indexes calculated by orthopedic surgeons (R#1 and R#2) was 0.93 (± 0.19) and 0.89 (± 0.19) for CD and 0.80 (± 0.17) and 0.78 (± 0.17) for BP. Automatic measurements performed by our algorithm for CD and BP indexes were 0.92 (± 0.21) and 0.75 (± 0.19), respectively. Excellent agreement between the orthopedic surgeons’ measurements and results of the algorithm has been achieved (ICC > 0.75, SEM < 0.014).
CONCLUSION Automatic patellar height assessment can be achieved on high-resolution radiographs with the required accuracy. Determining patellar end-points and the joint line-fitting to the proximal tibia joint surface allows for accurate CD and BP index calculations. The obtained results indicate that this approach can be valuable tool in a medical practice.
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Affiliation(s)
- Kamil Kwolek
- Department of Spine Disorders and Orthopaedics, Centre of Postgraduate Medical Education, Gruca Orthopaedic and Trauma Teaching Hospital, Otwock 05-400, Poland
| | - Dariusz Grzelecki
- Department of Orthopaedics and Rheumoorthopedics, Centre of Postgraduate Medical Education, Gruca Orthopaedic and Trauma Teaching Hospital, Otwock 05-400, Poland
| | - Konrad Kwolek
- Department of Orthopaedics and Traumatology, University Hospital, Krakow 30-663, Poland
| | - Dariusz Marczak
- Department of Orthopaedics and Rheumoorthopedics, Centre of Postgraduate Medical Education, Gruca Orthopaedic and Trauma Teaching Hospital, Otwock 05-400, Poland
| | - Jacek Kowalczewski
- Department of Orthopaedics and Rheumoorthopedics, Centre of Postgraduate Medical Education, Gruca Orthopaedic and Trauma Teaching Hospital, Otwock 05-400, Poland
| | - Marcin Tyrakowski
- Department of Spine Disorders and Orthopaedics, Centre of Postgraduate Medical Education, Gruca Orthopaedic and Trauma Teaching Hospital, Otwock 05-400, Poland
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15
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Pacheco VL, Bragagnolo L, Dalla Rosa F, Thomé A. Optimization of biocementation responses by artificial neural network and random forest in comparison to response surface methodology. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:61863-61887. [PMID: 36934187 DOI: 10.1007/s11356-023-26362-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 03/05/2023] [Indexed: 05/10/2023]
Abstract
In this article, the optimization of the specific urease activity (SUA) and the calcium carbonate (CaCO3) using microbially induced calcite precipitation (MICP) was compared to optimization using three algorithms based on machine learning: random forest regressor, artificial neural networks (ANNs), and multivariate linear regression. This study applied the techniques in two existing response surface method (RSM) experiments involving MICP technique. Random forest-based models and artificial neural network-based models were submitted through the optimization of hyperparameters via cross-validation technique and grid search, to select the best-optimized model. For this study, the random forest-based algorithm is aimed at having the best performance of 0.9381 and 0.9463 in comparison to the original r2 of 0.9021 and 0.8530, respectively. This study is aimed at exploring the capability of using machine learning-based models in small datasets for the purpose of optimization of experimental variables in MICP technique and the meaningfulness of the models by their specificities in the small experimental datasets applied to experimental designs. This study is aimed at exploring the capability of using machine learning-based models in small datasets for experimental variable optimization in MICP technique. The use of these techniques can create prerogatives to scale and mitigate costs in future experiments associated to the field.
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Affiliation(s)
- Vinicius Luiz Pacheco
- Graduate Program in Civil and Environmental Engineering, University of Passo Fundo (UPF), Campus I, Km 171, BR 285, Passo Fundo, Rio Grande Do Sul, CEP: 99001-970, Brazil.
| | - Lucimara Bragagnolo
- Graduate Program in Civil and Environmental Engineering, University of Passo Fundo (UPF), Campus I, Km 171, BR 285, Passo Fundo, Rio Grande Do Sul, CEP: 99001-970, Brazil
| | - Francisco Dalla Rosa
- Graduate Program in Civil and Environmental Engineering, University of Passo Fundo (UPF), Campus I, Km 171, BR 285, Passo Fundo, Rio Grande Do Sul, CEP: 99001-970, Brazil
| | - Antonio Thomé
- Graduate Program in Civil and Environmental Engineering, University of Passo Fundo (UPF), Campus I, Km 171, BR 285, Passo Fundo, Rio Grande Do Sul, CEP: 99001-970, Brazil
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16
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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%.
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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
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17
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Mohammed AS, Hasanaath AA, Latif G, Bashar A. Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images. Diagnostics (Basel) 2023; 13:diagnostics13081380. [PMID: 37189481 DOI: 10.3390/diagnostics13081380] [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: 02/16/2023] [Revised: 03/19/2023] [Accepted: 04/06/2023] [Indexed: 05/17/2023] Open
Abstract
One of the most common and challenging medical conditions to deal with in old-aged people is the occurrence of knee osteoarthritis (KOA). Manual diagnosis of this disease involves observing X-ray images of the knee area and classifying it under five grades using the Kellgren-Lawrence (KL) system. This requires the physician's expertise, suitable experience, and a lot of time, and even after that the diagnosis can be prone to errors. Therefore, researchers in the ML/DL domain have employed the capabilities of deep neural network (DNN) models to identify and classify KOA images in an automated, faster, and accurate manner. To this end, we propose the application of six pretrained DNN models, namely, VGG16, VGG19, ResNet101, MobileNetV2, InceptionResNetV2, and DenseNet121 for KOA diagnosis using images obtained from the Osteoarthritis Initiative (OAI) dataset. More specifically, we perform two types of classification, namely, a binary classification, which detects the presence or absence of KOA and secondly, classifying the severity of KOA in a three-class classification. For a comparative analysis, we experiment on three datasets (Dataset I, Dataset II, and Dataset III) with five, two, and three classes of KOA images, respectively. We achieved maximum classification accuracies of 69%, 83%, and 89%, respectively, with the ResNet101 DNN model. Our results show an improved performance from the existing work in the literature.
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Affiliation(s)
- Abdul Sami Mohammed
- Computer Engineering Department, Prince Mohammad Bin Fahd University, Al-Khobar 31952, Saudi Arabia
| | - Ahmed Abul Hasanaath
- Computer Science Department, Prince Mohammad Bin Fahd University, Al-Khobar 31952, Saudi Arabia
| | - Ghazanfar Latif
- Computer Science Department, Prince Mohammad Bin Fahd University, Al-Khobar 31952, Saudi Arabia
| | - Abul Bashar
- Computer Engineering Department, Prince Mohammad Bin Fahd University, Al-Khobar 31952, Saudi Arabia
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18
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Wang CT, Huang B, Thogiti N, Zhu WX, Chang CH, Pao JL, Lai F. Successful real-world application of an osteoarthritis classification deep-learning model using 9210 knees-An orthopedic surgeon's view. J Orthop Res 2023; 41:737-746. [PMID: 35822355 DOI: 10.1002/jor.25415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 05/23/2022] [Accepted: 07/07/2022] [Indexed: 02/04/2023]
Abstract
This study aimed to evaluate the performance of a deep-learning model to evaluate knee osteoarthritis using Kellgren-Lawrence grading in real-life knee radiographs. A deep convolutional neural network model was trained using 8964 knee radiographs from the osteoarthritis initiative (OAI), including 962 testing set images. Another 246 knee radiographs from the Far Eastern Memorial Hospital were used for external validation. The OAI testing set and external validation images were evaluated by experienced specialists, two orthopedic surgeons, and a musculoskeletal radiologist. The accuracy, interobserver agreement, F1 score, precision, recall, specificity, and ability to identify surgical candidates were used to compare the performances of the model and specialists. Attention maps illustrated the interpretability of the model classification. The model had a 78% accuracy and consistent interobserver agreement for the OAI (model-surgeon 1 К = 0.80, model-surgeon 2 К = 0.84, model-radiologist К = 0.86) and external validation (model-surgeon 1 К = 0.81, model-surgeon 2 К = 0.82, model-radiologist К = 0.83) images. A lower interobserver agreement was found in the images misclassified by the model (model-surgeon 1 К = 0.57, model-surgeon 2 К = 0.47, model-radiologist К = 0.65). The model performed better than specialists in identifying surgical candidates (Kellgren-Lawrence Stages 3 and 4) with an F1 score of 0.923. Our model not only had comparable results with specialists with respect to the ability to identify surgical candidates but also performed consistently with open database and real-life radiographs. We believe the controversy of the misclassified knee osteoarthritis images was based on a significantly lower interobserver agreement.
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Affiliation(s)
- Cheng-Tzu Wang
- Department of Orthopaedic Surgery, Far Eastern Memorial Hospital, New Taipei City, Taiwan.,Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan
| | - Brady Huang
- Department of Computer Science, New York University, New York, New York, USA
| | - Nagaraju Thogiti
- Department of Mathematics, University of Southern California, Los Angeles, California, USA
| | - Wan-Xuan Zhu
- Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan
| | - Chih-Hung Chang
- Department of Orthopaedic Surgery, Far Eastern Memorial Hospital, New Taipei City, Taiwan.,Graduate School of Biotechnology and Bioengineering, Yuan Ze University, Taoyuan, Taiwan
| | - Jwo-Luen Pao
- Department of Orthopaedic Surgery, Far Eastern Memorial Hospital, New Taipei City, Taiwan.,General Education Center, Lunghwa University of Technology, Taoyuan, Taiwan
| | - Feipei Lai
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
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19
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Plakias S, Moustakidis S, Kokkotis C, Tsatalas T, Papalexi M, Plakias D, Giakas G, Tsaopoulos D. Identifying Soccer Teams' Styles of Play: A Scoping and Critical Review. J Funct Morphol Kinesiol 2023; 8:jfmk8020039. [PMID: 37092371 PMCID: PMC10123610 DOI: 10.3390/jfmk8020039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 03/27/2023] [Accepted: 03/28/2023] [Indexed: 04/25/2023] Open
Abstract
Identifying and measuring soccer playing styles is a very important step toward a more effective performance analysis. Exploring the different game styles that a team can adopt to enable a great performance remains under-researched. To address this challenge and identify new directions in future research in the area, this paper conducted a critical review of 40 research articles that met specific criteria. Following the 22-item Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, this scoping review searched for literature on Google Scholar and Pub Med database. The descriptive and thematic analysis found that the objectives of the identified papers can be classified into three main categories (recognition and effectiveness of playing styles and contextual variables that affect them). Critically reviewing the studies, the paper concluded that: (i) factor analysis seems to be the best technique among inductive statistics; (ii) artificial intelligence (AI) opens new horizons in performance analysis, and (iii) there is a need for further research on the effectiveness of different playing styles, as well as on the impact of contextual variables on them.
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Affiliation(s)
- Spyridon Plakias
- Department of Physical Education and Sport Science, University of Thessaly, Karyes, 42100 Trikala, Greece
| | | | - Christos Kokkotis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Themistoklis Tsatalas
- Department of Physical Education and Sport Science, University of Thessaly, Karyes, 42100 Trikala, Greece
| | - Marina Papalexi
- Department of Operations, Technology, Events and Hospitality Management, Manchester Metropolitan University, Oxford Road, Manchester M15 6BH, UK
| | | | - Giannis Giakas
- Department of Physical Education and Sport Science, University of Thessaly, Karyes, 42100 Trikala, Greece
| | - Dimitrios Tsaopoulos
- Institute for Bio-Economy & Agri-Technology, Center for Research and Technology Hellas, 60361 Volos, Greece
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20
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Deng C, Sun Y, Zhang Z, Ma X, Liu X, Zhou F. Development and evaluation of nomograms for predicting osteoarthritis progression based on MRI cartilage parameters: data from the FNIH OA biomarkers Consortium. BMC Med Imaging 2023; 23:43. [PMID: 36973670 PMCID: PMC10045658 DOI: 10.1186/s12880-023-01001-w] [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: 10/17/2022] [Accepted: 03/15/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Osteoarthritis (OA) is a leading cause of disability worldwide. However, the existing methods for evaluating OA patients do not provide enough comprehensive information to make reliable predictions of OA progression. This retrospective study aimed to develop prediction nomograms based on MRI cartilage that can predict disease progression of OA. METHODS A total of 600 subjects with mild-to-moderate osteoarthritis from the Foundation for National Institute of Health (FNIH) project of osteoarthritis initiative (OAI). The MRI cartilage parameters of the knee at baseline were measured, and the changes in cartilage parameters at 12- and 24-month follow-up were calculated. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to extract the valuable characteristic parameters at different time points including cartilage thickness, cartilage volume, subchondral bone exposure area and uniform cartilage thickness in different sub regions of the knee, and the MRI cartilage parameters score0, scoreΔ12, and scoreΔ24 at baseline, 12 months, and 24 months were constructed. ScoreΔ12, and scoreΔ24 represent changes between 12 M vs. baseline, and 24 M vs. baseline, respectively. Logistic regression analysis was used to construct the nomogram0, nomogramΔ12, and nomogramΔ24, including MRI-based score and risk factors. The area under curve (AUC) was used to evaluate the differentiation of nomograms in disease progression and subgroup analysis. The calibration curve and Hosmer-Lemeshow (H-L) test were used to verify the calibration of the nomograms. Clinical usefulness of each prediction nomogram was verified by decision curve analysis (DCA). The nomograms with predictive efficacy were analyzed by secondary analysis. Internal verification was assessed using bootstrapping validation. RESULTS Each nomogram included cartilage score, KL grade, WOMAC pain score, WOMAC disability score, and minimum joint space width. The AUC of nomogram0, nomogramΔ12, and nomogramΔ24 in predicing the progression of radiology and pain were 0.69, 0.64, and 0.71, respectively. All three nomograms had good calibration. Analysis by DCA showed that the clinical effectiveness of nomogramΔ24 was higher than others. Secondary analysis showed that nomogram0 and nomogramΔ24 were more capable of predicting OA radiologic progression than pain progression. CONCLUSION Nomograms based on MRI cartilage change were useful for predicting the progression of mild to moderate OA.
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Affiliation(s)
- Chunbo Deng
- Department of Orthopedics, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
- Department of Orthopedics, Central Hospital of Shenyang Medical College, Shenyang, Liaoning Province, China
| | - Yingwei Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
- Department of Radiology, Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, Liaoning Province, China
| | - Zhan Zhang
- Department of Orthopedics, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Xun Ma
- Department of Rehabilitation, Shengjing Hospital of China Medical University, No.16, Puhe Street, Shenyang North New Area, Shenyang, Liaoning Province, 110134, China
| | - Xueyong Liu
- Department of Rehabilitation, Shengjing Hospital of China Medical University, No.16, Puhe Street, Shenyang North New Area, Shenyang, Liaoning Province, 110134, China.
| | - Fenghua Zhou
- Department of Rehabilitation, Shengjing Hospital of China Medical University, No.16, Puhe Street, Shenyang North New Area, Shenyang, Liaoning Province, 110134, China.
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21
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Bensalma F, Mezghani N, Cagnin A, Fuente A, Lenoir L, Hagemeister N. Multimodal data analysis of knee osteoarthritis assessment: factors selection for conservative care decision making. Comput Methods Biomech Biomed Engin 2023; 26:450-459. [PMID: 35472257 DOI: 10.1080/10255842.2022.2066973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
When assessing a patient with knee osteoarthritis (OA), a number of factors are considered to guide treatment plan, namely, demographic, radiographic, clinical, musculoskeletal, and biomechanical factors. The aim of this study is to identify which of these factors are the most related to each other to potentially better prioritize the modifiable factors to be addressed as they may influence treatment outcomes. We investigated a multimodal canonical correlation analysis to evaluate associations between these factors. The analysis was performed on 415 OA patients who were not candidates for knee arthroplasty, to identify factors that are associated to the patients' clinical conditions.
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Affiliation(s)
- F Bensalma
- Research Center LICEF institute, TELUQ, Montréal, Canada.,Laboratoire de recherche en imagerie et orthopédie (LIO), Research Centre of the centre hospitalier de l'université de Montréal (CRCHUM), Montréal, Canada
| | - N Mezghani
- Research Center LICEF institute, TELUQ, Montréal, Canada.,Laboratoire de recherche en imagerie et orthopédie (LIO), Research Centre of the centre hospitalier de l'université de Montréal (CRCHUM), Montréal, Canada
| | - A Cagnin
- Laboratoire de recherche en imagerie et orthopédie (LIO), Research Centre of the centre hospitalier de l'université de Montréal (CRCHUM), Montréal, Canada.,LIO, École de technologie supérieure, Montréal, Canada
| | | | | | - N Hagemeister
- Laboratoire de recherche en imagerie et orthopédie (LIO), Research Centre of the centre hospitalier de l'université de Montréal (CRCHUM), Montréal, Canada.,LIO, École de technologie supérieure, Montréal, Canada
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22
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Bonnin M, Müller-Fouarge F, Estienne T, Bekadar S, Pouchy C, Ait Si Selmi T. Artificial Intelligence Radiographic Analysis Tool for Total Knee Arthroplasty. J Arthroplasty 2023:S0883-5403(23)00184-5. [PMID: 36858127 DOI: 10.1016/j.arth.2023.02.053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/15/2023] [Accepted: 02/17/2023] [Indexed: 03/03/2023] Open
Abstract
BACKGROUND The postoperative follow-up of a patient after total knee arthroplasty (TKA) requires regular evaluation of the condition of the knee through interpretation of X-rays. This rigorous analysis requires expertize, time, and methodical standardization. Our work evaluated the use of an artificial intelligence tool, X-TKA, to assist surgeons in their interpretation. METHODS A series of 12 convolutional neural networks were trained on a large database containing 39,751 X-ray images. These algorithms are able to determine examination quality, identify image characteristics, assess prosthesis sizing and positioning, measure knee-prosthesis alignment angles, and detect anomalies in the bone-cement-implant complex. The individual interpretations of a pool of senior surgeons with and without the assistance of X-TKA were evaluated on a reference dataset built in consensus by senior surgeons. RESULTS The algorithms obtained a mean area under the curve value of 0.98 on the quality assurance and the image characteristics tasks. They reached a mean difference for the predicted angles of 1.71° (standard deviation, 1.53°), similar to the surgeon average difference of 1.69° (standard deviation, 1.52°). The comparative analysis showed that the assistance of X-TKA allowed surgeons to gain 5% in accuracy and 12% in sensitivity in the detection of interface anomalies. Moreover, this study demonstrated a gain in repeatability for each single surgeon (Light's kappa +0.17), as well as a gain in the reproducibility between surgeons (Light's kappa +0.1). CONCLUSION This study highlights the benefit of using an intelligent artificial tool for a standardized interpretation of postoperative knee X-rays and indicates the potential for its use in clinical practice.
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23
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Gkantzios A, Kokkotis C, Tsiptsios D, Moustakidis S, Gkartzonika E, Avramidis T, Aggelousis N, Vadikolias K. Evaluation of Blood Biomarkers and Parameters for the Prediction of Stroke Survivors' Functional Outcome upon Discharge Utilizing Explainable Machine Learning. Diagnostics (Basel) 2023; 13:diagnostics13030532. [PMID: 36766637 PMCID: PMC9914778 DOI: 10.3390/diagnostics13030532] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 01/25/2023] [Accepted: 01/29/2023] [Indexed: 02/04/2023] Open
Abstract
Despite therapeutic advancements, stroke remains a leading cause of death and long-term disability. The quality of current stroke prognostic models varies considerably, whereas prediction models of post-stroke disability and mortality are restricted by the sample size, the range of clinical and risk factors and the clinical applicability in general. Accurate prognostication can ease post-stroke discharge planning and help healthcare practitioners individualize aggressive treatment or palliative care, based on projected life expectancy and clinical course. In this study, we aimed to develop an explainable machine learning methodology to predict functional outcomes of stroke patients at discharge, using the Modified Rankin Scale (mRS) as a binary classification problem. We identified 35 parameters from the admission, the first 72 h, as well as the medical history of stroke patients, and used them to train the model. We divided the patients into two classes in two approaches: "Independent" vs. "Non-Independent" and "Non-Disability" vs. "Disability". Using various classifiers, we found that the best models in both approaches had an upward trend, with respect to the selected biomarkers, and achieved a maximum accuracy of 88.57% and 89.29%, respectively. The common features in both approaches included: age, hemispheric stroke localization, stroke localization based on blood supply, development of respiratory infection, National Institutes of Health Stroke Scale (NIHSS) upon admission and systolic blood pressure levels upon admission. Intubation and C-reactive protein (CRP) levels upon admission are additional features for the first approach and Erythrocyte Sedimentation Rate (ESR) levels upon admission for the second. Our results suggest that the said factors may be important predictors of functional outcomes in stroke patients.
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Affiliation(s)
- Aimilios Gkantzios
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
- Department of Neurology, Korgialeneio—Benakeio “Hellenic Red Cross” General Hospital of Athens, 11526 Athens, Greece
- Correspondence:
| | - Christos Kokkotis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Dimitrios Tsiptsios
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Serafeim Moustakidis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
- AIDEAS OÜ, Narva mnt 5, 10117 Tallinn, Estonia
| | - Elena Gkartzonika
- School of Philosophy, University of Ioannina, 45110 Ioannina, Greece
| | - Theodoros Avramidis
- Department of Neurology, Korgialeneio—Benakeio “Hellenic Red Cross” General Hospital of Athens, 11526 Athens, Greece
| | - Nikolaos Aggelousis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Konstantinos Vadikolias
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
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24
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Demanse D, Saxer F, Lustenberger P, Tankó LB, Nikolaus P, Rasin I, Brennan DF, Roubenoff R, Premji S, Conaghan PG, Schieker M. Unsupervised machine-learning algorithms for the identification of clinical phenotypes in the osteoarthritis initiative database. Semin Arthritis Rheum 2023; 58:152140. [PMID: 36446256 DOI: 10.1016/j.semarthrit.2022.152140] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 11/03/2022] [Accepted: 11/16/2022] [Indexed: 11/20/2022]
Abstract
OBJECTIVES Osteoarthritis (OA) is a complex disease comprising diverse underlying patho-mechanisms. To enable the development of effective therapies, segmentation of the heterogenous patient population is critical. This study aimed at identifying such patient clusters using two different machine learning algorithms. METHODS Using the progression and incident cohorts of the Osteoarthritis Initiative (OAI) dataset, deep embedded clustering (DEC) and multiple factor analysis with clustering (MFAC) approaches, including 157 input-variables at baseline, were employed to differentiate specific patient profiles. RESULTS DEC resulted in 5 and MFAC in 3 distinct patient phenotypes. Both identified a "comorbid" cluster with higher body mass index (BMI), relevant burden of comorbidity and low levels of physical activity. Both methods also identified a younger and physically more active cluster and an elderly cluster with functional limitations, but low disease impact. The additional two clusters identified with DEC were subgroups of the young/physically active and the elderly/physically inactive clusters. Overall pain trajectories over 9 years were stable, only the numeric rating scale (NRS) for pain showed distinct increase, while physical activity decreased in all clusters. Clusters showed different (though non-significant) trajectories of joint space changes over the follow-up period of 8 years. CONCLUSION Two different clustering approaches yielded similar patient allocations primarily separating complex "comorbid" patients from healthier subjects, the latter divided in young/physically active vs elderly/physically inactive subjects. The observed association to clinical (pain/physical activity) and structural progression could be helpful for early trial design as strategy to enrich for patients who may specifically benefit from disease-modifying treatments.
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Affiliation(s)
| | - Franziska Saxer
- Novartis Institutes for Biomedical Research, Novartis Campus, 4002, Basel, Switzerland; Medical Faculty, University of Basel, 4002, Basel, Switzerland.
| | | | | | - Philipp Nikolaus
- IBM Switzerland AG, Vulkanstrasse 106, 8048, Zürich, Switzerland
| | - Ilja Rasin
- IBM Switzerland AG, Vulkanstrasse 106, 8048, Zürich, Switzerland.
| | - Damian F Brennan
- IBM Switzerland AG, Vulkanstrasse 106, 8048, Zürich, Switzerland
| | - Ronenn Roubenoff
- Novartis Institutes for Biomedical Research, Novartis Campus, 4002, Basel, Switzerland.
| | - Sumehra Premji
- Novartis Pharma AG, 4002, Basel, Switzerland; IBM Switzerland AG, Vulkanstrasse 106, 8048, Zürich, Switzerland.
| | - Philip G Conaghan
- Leeds Institute of Rheumatic & Musculoskeletal Medicine, University of Leeds and NIHR Leeds Biomedical Research Centre, UK.
| | - Matthias Schieker
- Novartis Institutes for Biomedical Research, Novartis Campus, 4002, Basel, Switzerland.
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25
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Hirvasniemi J, Runhaar J, van der Heijden RA, Zokaeinikoo M, Yang M, Li X, Tan J, Rajamohan HR, Zhou Y, Deniz CM, Caliva F, Iriondo C, Lee JJ, Liu F, Martinez AM, Namiri N, Pedoia V, Panfilov E, Bayramoglu N, Nguyen HH, Nieminen MT, Saarakkala S, Tiulpin A, Lin E, Li A, Li V, Dam EB, Chaudhari AS, Kijowski R, Bierma-Zeinstra S, Oei EHG, Klein S. The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images. Osteoarthritis Cartilage 2023; 31:115-125. [PMID: 36243308 DOI: 10.1016/j.joca.2022.10.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 09/02/2022] [Accepted: 10/03/2022] [Indexed: 11/05/2022]
Abstract
OBJECTIVES The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. DESIGN The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). RESULTS Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57-0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52-0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. CONCLUSION The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.
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Affiliation(s)
- J Hirvasniemi
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
| | - J Runhaar
- Department of General Practice, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - R A van der Heijden
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - M Zokaeinikoo
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, USA
| | - M Yang
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, USA
| | - X Li
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, USA
| | - J Tan
- Department of Radiology, New York University Langone Health, New York, USA
| | - H R Rajamohan
- Department of Radiology, New York University Langone Health, New York, USA
| | - Y Zhou
- Department of Radiology, New York University Langone Health, New York, USA
| | - C M Deniz
- Department of Radiology, New York University Langone Health, New York, USA
| | - F Caliva
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - C Iriondo
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - J J Lee
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - F Liu
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - A M Martinez
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - N Namiri
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - V Pedoia
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - E Panfilov
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - N Bayramoglu
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - H H Nguyen
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - M T Nieminen
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - S Saarakkala
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - A Tiulpin
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - E Lin
- Akousist Co., Ltd., Taoyuan City, Taiwan
| | - A Li
- Akousist Co., Ltd., Taoyuan City, Taiwan
| | - V Li
- Akousist Co., Ltd., Taoyuan City, Taiwan
| | - E B Dam
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - A S Chaudhari
- Department of Radiology, Stanford University, Stanford, USA
| | - R Kijowski
- Department of Radiology, New York University Langone Health, New York, USA
| | - S Bierma-Zeinstra
- Department of General Practice, Erasmus MC University Medical Center, Rotterdam, the Netherlands; Department of Orthopedics & Sport Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - E H G Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - S Klein
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
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26
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Hu K, Wu W, Li W, Simic M, Zomaya A, Wang Z. Adversarial Evolving Neural Network for Longitudinal Knee Osteoarthritis Prediction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:3207-3217. [PMID: 35675256 PMCID: PMC9750833 DOI: 10.1109/tmi.2022.3181060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Knee osteoarthritis (KOA) as a disabling joint disease has doubled in prevalence since the mid-20th century. Early diagnosis for the longitudinal KOA grades has been increasingly important for effective monitoring and intervention. Although recent studies have achieved promising performance for baseline KOA grading, longitudinal KOA grading has been seldom studied and the KOA domain knowledge has not been well explored yet. In this paper, a novel deep learning architecture, namely adversarial evolving neural network (A-ENN), is proposed for longitudinal grading of KOA severity. As the disease progresses from mild to severe level, ENN involves the progression patterns for accurately characterizing the disease by comparing an input image it to the template images of different KL grades using convolution and deconvolution computations. In addition, an adversarial training scheme with a discriminator is developed to obtain the evolution traces. Thus, the evolution traces as fine-grained domain knowledge are further fused with the general convolutional image representations for longitudinal grading. Note that ENN can be applied to other learning tasks together with existing deep architectures, in which the responses characterize progressive representations. Comprehensive experiments on the Osteoarthritis Initiative (OAI) dataset were conducted to evaluate the proposed method. An overall accuracy was achieved as 62.7%, with the baseline, 12-month, 24-month, 36-month, and 48-month accuracy as 64.6%, 63.9%, 63.2%, 61.8% and 60.2%, respectively.
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27
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Kokkotis C, Giarmatzis G, Giannakou E, Moustakidis S, Tsatalas T, Tsiptsios D, Vadikolias K, Aggelousis N. An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data. Diagnostics (Basel) 2022; 12:2392. [PMID: 36292081 PMCID: PMC9600473 DOI: 10.3390/diagnostics12102392] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/26/2022] [Accepted: 09/27/2022] [Indexed: 11/16/2022] Open
Abstract
Stroke is an acute neurological dysfunction attributed to a focal injury of the central nervous system due to reduced blood flow to the brain. Nowadays, stroke is a global threat associated with premature death and huge economic consequences. Hence, there is an urgency to model the effect of several risk factors on stroke occurrence, and artificial intelligence (AI) seems to be the appropriate tool. In the present study, we aimed to (i) develop reliable machine learning (ML) prediction models for stroke disease; (ii) cope with a typical severe class imbalance problem, which is posed due to the stroke patients' class being significantly smaller than the healthy class; and (iii) interpret the model output for understanding the decision-making mechanism. The effectiveness of the proposed ML approach was investigated in a comparative analysis with six well-known classifiers with respect to metrics that are related to both generalization capability and prediction accuracy. The best overall false-negative rate was achieved by the Multi-Layer Perceptron (MLP) classifier (18.60%). Shapley Additive Explanations (SHAP) were employed to investigate the impact of the risk factors on the prediction output. The proposed AI method could lead to the creation of advanced and effective risk stratification strategies for each stroke patient, which would allow for timely diagnosis and the right treatments.
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Affiliation(s)
- Christos Kokkotis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Georgios Giarmatzis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | - Erasmia Giannakou
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
| | | | - Themistoklis Tsatalas
- Department of Physical Education and Sport Science, University of Thessaly, 38221 Trikala, Greece
| | - Dimitrios Tsiptsios
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Konstantinos Vadikolias
- Department of Neurology, School of Medicine, University Hospital of Alexandroupolis, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Nikolaos Aggelousis
- Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
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28
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A Novel Focal Ordinal Loss for Assessment of Knee Osteoarthritis Severity. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10857-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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29
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Jones GMC, Pitsillides AA, Meeson RL. Moving Beyond the Limits of Detection: The Past, the Present, and the Future of Diagnostic Imaging in Canine Osteoarthritis. Front Vet Sci 2022; 9:789898. [PMID: 35372534 PMCID: PMC8964951 DOI: 10.3389/fvets.2022.789898] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 01/17/2022] [Indexed: 12/13/2022] Open
Abstract
Osteoarthritis (OA) is the most common orthopedic condition in dogs, characterized as the chronic, painful end-point of a synovial joint with limited therapeutic options other than palliative pain control or surgical salvage. Since the 1970s, radiography has been the standard-of-care for the imaging diagnosis of OA, despite its known limitations. As newer technologies have been developed, the limits of detection have lowered, allowing for the identification of earlier stages of OA. Identification of OA at a stage where it is potentially reversible still remains elusive, however, yet there is hope that newer technologies may be able to close this gap. In this article, we review the changes in the imaging of canine OA over the past 50 years and give a speculative view on future innovations which may provide for earlier identification, with the ultimate goal of repositioning the limit of detection to cross the threshold of this potentially reversible disease.
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Affiliation(s)
- Gareth M. C. Jones
- Department of Clinical Science and Services, Royal Veterinary College, Hatfield, United Kingdom
| | - Andrew A. Pitsillides
- Department of Comparative Biological Science, Royal Veterinary College, London, United Kingdom
| | - Richard L. Meeson
- Department of Clinical Science and Services, Royal Veterinary College, Hatfield, United Kingdom
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30
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Almhdie-Imjabbar A, Nguyen KL, Toumi H, Jennane R, Lespessailles E. Prediction of knee osteoarthritis progression using radiological descriptors obtained from bone texture analysis and Siamese neural networks: data from OAI and MOST cohorts. Arthritis Res Ther 2022; 24:66. [PMID: 35260192 PMCID: PMC8903620 DOI: 10.1186/s13075-022-02743-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 02/10/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Trabecular bone texture (TBT) analysis has been identified as an imaging biomarker that provides information on trabecular bone changes due to knee osteoarthritis (KOA). In parallel with the improvement in medical imaging technologies, machine learning methods have received growing interest in the scientific osteoarthritis community to potentially provide clinicians with prognostic data from conventional knee X-ray datasets, in particular from the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST) cohorts. PATIENTS AND METHODS This study included 1888 patients from OAI and 683 patients from MOST cohorts. Radiographs were automatically segmented to determine 16 regions of interest. Patients with an early stage of OA risk, with Kellgren and Lawrence (KL) grade of 1 < KL < 4, were selected. The definition of OA progression was an increase in the OARSI medial joint space narrowing (mJSN) grades over 48 months in OAI and 60 months in MOST. The performance of the TBT-CNN model was evaluated and compared to well-known prediction models using logistic regression. RESULTS The TBT-CNN model was predictive of the JSN progression with an area under the curve (AUC) up to 0.75 in OAI and 0.81 in MOST. The predictive ability of the TBT-CNN model was invariant with respect to the acquisition modality or image quality. The prediction models performed significantly better with estimated KL (KLprob) grades than those provided by radiologists. TBT-based models significantly outperformed KLprob-based models in MOST and provided similar performances in OAI. In addition, the combined model, when trained in one cohort, was able to predict OA progression in the other cohort. CONCLUSION The proposed combined model provides a good performance in the prediction of mJSN over 4 to 6 years in patients with relevant KOA. Furthermore, the current study presents an important contribution in showing that TBT-based OA prediction models can work with different databases.
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Affiliation(s)
- Ahmad Almhdie-Imjabbar
- EA 4708-I3MTO Laboratory, University of Orleans, Orléans, France
- Translational Medicine Research Platform, PRIMMO, Regional Hospital of Orleans, Orléans, France
| | - Khac-Lan Nguyen
- EA 4708-I3MTO Laboratory, University of Orleans, Orléans, France
- Translational Medicine Research Platform, PRIMMO, Regional Hospital of Orleans, Orléans, France
| | - Hechmi Toumi
- EA 4708-I3MTO Laboratory, University of Orleans, Orléans, France
- Translational Medicine Research Platform, PRIMMO, Regional Hospital of Orleans, Orléans, France
- Department of Rheumatology, Regional Hospital of Orleans, Orléans, France
| | - Rachid Jennane
- EA 4708-I3MTO Laboratory, University of Orleans, Orléans, France
- Translational Medicine Research Platform, PRIMMO, Regional Hospital of Orleans, Orléans, France
| | - Eric Lespessailles
- EA 4708-I3MTO Laboratory, University of Orleans, Orléans, France
- Translational Medicine Research Platform, PRIMMO, Regional Hospital of Orleans, Orléans, France
- Department of Rheumatology, Regional Hospital of Orleans, Orléans, France
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31
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Kokkotis C, Ntakolia C, Moustakidis S, Giakas G, Tsaopoulos D. Explainable machine learning for knee osteoarthritis diagnosis based on a novel fuzzy feature selection methodology. Phys Eng Sci Med 2022; 45:219-229. [PMID: 35099771 PMCID: PMC8802106 DOI: 10.1007/s13246-022-01106-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 01/19/2022] [Indexed: 01/30/2023]
Abstract
Knee Osteoarthritis (ΚΟΑ) is a degenerative joint disease of the knee that results from the progressive loss of cartilage. Due to KOA's multifactorial nature and the poor understanding of its pathophysiology, there is a need for reliable tools that will reduce diagnostic errors made by clinicians. The existence of public databases has facilitated the advent of advanced analytics in KOA research however the heterogeneity of the available data along with the observed high feature dimensionality make this diagnosis task difficult. The objective of the present study is to provide a robust Feature Selection (FS) methodology that could: (i) handle the multidimensional nature of the available datasets and (ii) alleviate the defectiveness of existing feature selection techniques towards the identification of important risk factors which contribute to KOA diagnosis. For this aim, we used multidimensional data obtained from the Osteoarthritis Initiative database for individuals without or with KOA. The proposed fuzzy ensemble feature selection methodology aggregates the results of several FS algorithms (filter, wrapper and embedded ones) based on fuzzy logic. The effectiveness of the proposed methodology was evaluated using an extensive experimental setup that involved multiple competing FS algorithms and several well-known ML models. A 73.55% classification accuracy was achieved by the best performing model (Random Forest classifier) on a group of twenty-one selected risk factors. Explainability analysis was finally performed to quantify the impact of the selected features on the model's output thus enhancing our understanding of the rationale behind the decision-making mechanism of the best model.
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Affiliation(s)
- Christos Kokkotis
- Institute for Bio-Economy & Agri-Technology, Center for Research and Technology Hellas, 38333, Volos, Greece.
- TEFAA, Department of Physical Education & Sport Science, University of Thessaly, 42100, Trikala, Greece.
| | - Charis Ntakolia
- University Mental Health Research Institute, 11527, Athens, Greece
- School of Naval Architecture and Marine Engineering, National Technical University of Athens, 15772, Athens, Greece
| | | | - Giannis Giakas
- TEFAA, Department of Physical Education & Sport Science, University of Thessaly, 42100, Trikala, Greece
| | - Dimitrios Tsaopoulos
- Institute for Bio-Economy & Agri-Technology, Center for Research and Technology Hellas, 38333, Volos, Greece
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Brejneboel M, Hansen P, Nybing J, Bachmann R, Ratjen U, Hansen I, Lenskjold A, Axelsen M, Lundemann M, Boesen M. External Validation of an Artificial Intelligence Tool for Radiographic Knee Osteoarthritis Severity Classification. Eur J Radiol 2022; 150:110249. [DOI: 10.1016/j.ejrad.2022.110249] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 12/07/2021] [Accepted: 03/07/2022] [Indexed: 11/27/2022]
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Binvignat M, Pedoia V, Butte AJ, Louati K, Klatzmann D, Berenbaum F, Mariotti-Ferrandiz E, Sellam J. Use of machine learning in osteoarthritis research: a systematic literature review. RMD Open 2022; 8:e001998. [PMID: 35296530 PMCID: PMC8928401 DOI: 10.1136/rmdopen-2021-001998] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 02/16/2022] [Indexed: 11/21/2022] Open
Abstract
OBJECTIVE The aim of this systematic literature review was to provide a comprehensive and exhaustive overview of the use of machine learning (ML) in the clinical care of osteoarthritis (OA). METHODS A systematic literature review was performed in July 2021 using MEDLINE PubMed with key words and MeSH terms. For each selected article, the number of patients, ML algorithms used, type of data analysed, validation methods and data availability were collected. RESULTS From 1148 screened articles, 46 were selected and analysed; most were published after 2017. Twelve articles were related to diagnosis, 7 to prediction, 4 to phenotyping, 12 to severity and 11 to progression. The number of patients included ranged from 18 to 5749. Overall, 35% of the articles described the use of deep learning And 74% imaging analyses. A total of 85% of the articles involved knee OA and 15% hip OA. No study investigated hand OA. Most of the studies involved the same cohort, with data from the OA initiative described in 46% of the articles and the MOST and Cohort Hip and Cohort Knee cohorts in 11% and 7%. Data and source codes were described as publicly available respectively in 54% and 22% of the articles. External validation was provided in only 7% of the articles. CONCLUSION This review proposes an up-to-date overview of ML approaches used in clinical OA research and will help to enhance its application in this field.
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Affiliation(s)
- Marie Binvignat
- Department of Rheumatology, Hôpital Saint-Antoine, Assistance Publique - Hôpitaux de Paris (AP-HP), Centre de Recherche Saint-Antoine, Inserm UMRS_938, Assistance Publique - Hôpitaux de Paris (AP-HP), Sorbonne Universite, Paris, France
- Bakar Computational Health Science Institute, University of California, San Francisco, California, USA
- Immunology Immunopathology Immunotherapy UMRS_959, Sorbonne Universite, Paris, France
| | - Valentina Pedoia
- Center for Intelligent Imaging (CI2), Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Atul J Butte
- Bakar Computational Health Science Institute, University of California, San Francisco, California, USA
| | - Karine Louati
- Department of Rheumatology, Hôpital Saint-Antoine, Assistance Publique - Hôpitaux de Paris (AP-HP), Centre de Recherche Saint-Antoine, Inserm UMRS_938, Assistance Publique - Hôpitaux de Paris (AP-HP), Sorbonne Universite, Paris, France
| | - David Klatzmann
- Immunology Immunopathology Immunotherapy UMRS_959, Sorbonne Universite, Paris, France
- Biotherapy (CIC-BTi) and Inflammation Immunopathology-Biotherapy Department (i2B), Hôpital Pitié-Salpêtrière, AP-HP, Paris, France
| | - Francis Berenbaum
- Department of Rheumatology, Hôpital Saint-Antoine, Assistance Publique - Hôpitaux de Paris (AP-HP), Centre de Recherche Saint-Antoine, Inserm UMRS_938, Assistance Publique - Hôpitaux de Paris (AP-HP), Sorbonne Universite, Paris, France
| | | | - Jérémie Sellam
- Department of Rheumatology, Hôpital Saint-Antoine, Assistance Publique - Hôpitaux de Paris (AP-HP), Centre de Recherche Saint-Antoine, Inserm UMRS_938, Assistance Publique - Hôpitaux de Paris (AP-HP), Sorbonne Universite, Paris, France
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Evans J, Hamilton RI, Biggs P, Holt C, Elliott MT. Data sharing across osteoarthritis research groups and disciplines: Opportunities and challenges. OSTEOARTHRITIS AND CARTILAGE OPEN 2022; 4:100236. [PMID: 36474476 PMCID: PMC9718296 DOI: 10.1016/j.ocarto.2022.100236] [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: 05/27/2021] [Revised: 01/13/2022] [Accepted: 01/17/2022] [Indexed: 11/24/2022] Open
Abstract
Background Osteoarthritis is a heterogeneous condition characterised by a wide variety of factors and represents a worldwide healthcare challenge. There are multiple clinical and research specialisms involved in the diagnosis, prognosis and treatment of osteoarthritis, and there may be opportunities to share or pool data which are currently not being utilised. However, there are challenges to doing so which require carefully structured solutions and partnership working. Methods Interviews were conducted with nine experts from various fields within osteoarthritis research. A semi-structured approach was used, and thematic analysis applied to the results. Results Generally, osteoarthritis researchers were supportive of data sharing, provided it is done responsibly and without impacting data integrity. Benefits identified included increasing typically low-powered data, the potential for machine learning opportunities, and the potential for improved patient outcomes. However, a number of challenges were identified, relating to: data security, data harmonisation, storage costs, ethical considerations and governance. Conclusions There is clear support for increased data sharing and partnership working in osteoarthritis research. Further investigation will be required to navigate the complex issues identified; however, it is clear that collaborative opportunities should be better facilitated and there may be innovative ways to do this. It is also clear that nomenclature within different disciplines could be better streamlined, to improve existing opportunities to harmonise data.
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Affiliation(s)
- Jill Evans
- Institute of Digital Healthcare, WMG, University of Warwick, Coventry, CV4 7AL, UK
| | - Rebecca I. Hamilton
- Arthritis Research UK Biomechanics and Bioengineering Centre, Cardiff University, Cardiff, CF10 3AT, UK
| | - Paul Biggs
- Arthritis Research UK Biomechanics and Bioengineering Centre, Cardiff University, Cardiff, CF10 3AT, UK
| | - Cathy Holt
- Arthritis Research UK Biomechanics and Bioengineering Centre, Cardiff University, Cardiff, CF10 3AT, UK
| | - Mark T. Elliott
- Institute of Digital Healthcare, WMG, University of Warwick, Coventry, CV4 7AL, UK
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Joseph GB, McCulloch CE, Sohn JH, Pedoia V, Majumdar S, Link TM. AI MSK clinical applications: cartilage and osteoarthritis. Skeletal Radiol 2022; 51:331-343. [PMID: 34735607 DOI: 10.1007/s00256-021-03909-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 09/08/2021] [Accepted: 09/12/2021] [Indexed: 02/02/2023]
Abstract
The advancements of artificial intelligence (AI) for osteoarthritis (OA) applications have been rapid in recent years, particularly innovations of deep learning for image classification, lesion detection, cartilage segmentation, and prediction modeling of future knee OA development. This review article focuses on AI applications in OA research, first describing machine learning (ML) techniques and workflow, followed by how these algorithms are used for OA classification tasks through imaging and non-imaging-based ML models. Deep learning applications for OA research, including analysis of both radiographs for automatic detection of OA severity, and MR images for detection of cartilage/meniscus lesions and cartilage segmentation for automatic T2 quantification will be described. In addition, information on ML models that identify individuals at high risk of OA development will be provided. The future vision of machine learning applications in imaging of OA and cartilage hinges on implementation of AI for optimizing imaging protocols, quantitative assessment of cartilage, and automated analysis of disease burden yielding a faster and more efficient workflow for a radiologist with a higher level of reproducibility and precision. It may also provide risk assessment tools for individual patients, which is an integral part of precision medicine.
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Affiliation(s)
- Gabby B Joseph
- Department of Radiology and Biomedical Imaging, University of California, 185 Berry St, Suite 350, San Francisco, CA, 94158, USA.
| | - Charles E McCulloch
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Jae Ho Sohn
- Department of Radiology and Biomedical Imaging, University of California, 185 Berry St, Suite 350, San Francisco, CA, 94158, USA
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging, University of California, 185 Berry St, Suite 350, San Francisco, CA, 94158, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California, 185 Berry St, Suite 350, San Francisco, CA, 94158, USA
| | - Thomas M Link
- Department of Radiology and Biomedical Imaging, University of California, 185 Berry St, Suite 350, San Francisco, CA, 94158, USA
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Bayramoglu N, Nieminen MT, Saarakkala S. Machine learning based texture analysis of patella from X-rays for detecting patellofemoral osteoarthritis. Int J Med Inform 2021; 157:104627. [PMID: 34773800 DOI: 10.1016/j.ijmedinf.2021.104627] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 10/11/2021] [Accepted: 10/25/2021] [Indexed: 12/18/2022]
Abstract
OBJECTIVE To assess the ability of texture features for detecting radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. DESIGN We used lateral view knee radiographs from The Multicenter Osteoarthritis Study (MOST) public use datasets (n = 5507 knees). Patellar region-of-interest (ROI) was automatically detected using landmark detection tool (BoneFinder), and subsequently, these anatomical landmarks were used to extract three different texture ROIs. Hand-crafted features, based on Local Binary Patterns (LBP), were then extracted to describe the patellar texture. First, a machine learning model (Gradient Boosting Machine) was trained to detect radiographic PFOA from the LBP features. Furthermore, we used end-to-end trained deep convolutional neural networks (CNNs) directly on the texture patches for detecting the PFOA. The proposed classification models were eventually compared with more conventional reference models that use clinical assessments and participant characteristics such as age, sex, body mass index (BMI), the total Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, and tibiofemoral Kellgren-Lawrence (KL) grade. Atlas-guided visual assessment of PFOA status by expert readers provided in the MOST public use datasets was used as a classification outcome for the models. Performance of prediction models was assessed using the area under the receiver operating characteristic curve (ROC AUC), the area under the precision-recall (PR) curve -average precision (AP)-, and Brier score in the stratified 5-fold cross validation setting. RESULTS Of the 5507 knees, 953 (17.3%) had PFOA. AUC and AP for the strongest reference model including age, sex, BMI, WOMAC score, and tibiofemoral KL grade to predict PFOA were 0.817 and 0.487, respectively. Textural ROI classification using CNN significantly improved the prediction performance (ROC AUC = 0.889, AP = 0.714). CONCLUSION We present the first study that analyses patellar bone texture for diagnosing PFOA. Our results demonstrates the potential of using texture features of patella to predict PFOA.
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Affiliation(s)
- Neslihan Bayramoglu
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.
| | - Miika T Nieminen
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland; Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland.
| | - Simo Saarakkala
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland; Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland.
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Makrogiannis S. Editorial for "Cross-Cohort Automatic Knee MRI Segmentation with Multi-Planar U-Nets". J Magn Reson Imaging 2021; 55:1664-1665. [PMID: 34757652 PMCID: PMC10364466 DOI: 10.1002/jmri.27990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 11/01/2021] [Indexed: 11/10/2022] Open
Affiliation(s)
- Sokratis Makrogiannis
- Math Imaging and Visual Computing Lab, Division of Physics, Engineering, Mathematics, and Computer Science, Delaware State University, Dover, Delaware, USA
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Lin Z, He L. Intra-Articular Injection of PRP in the Treatment of Knee Osteoarthritis Using Big Data. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:4504155. [PMID: 34745498 PMCID: PMC8564187 DOI: 10.1155/2021/4504155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 09/13/2021] [Accepted: 10/13/2021] [Indexed: 11/23/2022]
Abstract
Observing data on the characteristics of intra-articular injection of sodium citrate for knee osteoarthritis is an important reference value for human safety and evacuation design. To address the problems of slow data collection and poor accuracy of results of knee osteoarthritis behavior, under intensive conditions of intra-articular injection for knee osteoarthritis, this paper designs a data mining-based feature extraction system for intra-articular injection of sodium citrate for knee osteoarthritis. Using the Hadoop architecture, we extract the basic data of human behavior in the two-dimensional plane by storing and stitching the collected continuous data and discriminate the behavioral categories of knee osteoarthritis. We collected a real dataset from 84 patients with knee osteoarthritis treated in our hospital from October 2019 to October 2020. The dataset was divided into 42 patients in the tretinoin group and 42 patients in the sodium glutamate group according to the randomized number table method. The trimethoprim group was treated with intra-articular injection of trimethoprim, and the sodium citrate group was treated with intra-articular injection of sodium citrate. The clinical efficacy, joint mobility, intra-articular fluid volume, Lysholm score of knee joint, numerical pain intensity scale (NRS) score, and adverse effects of the two groups were compared before and after treatment. In our experiments, we observed that, compared with triamcinolone acetonide intra-articular injection, sodium hyaluronate intra-articular injection is more effective in the treatment of knee osteoarthritis. It can effectively improve knee function and reduce pain and adverse reactions.
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Affiliation(s)
- Zhijin Lin
- Xingtai People's Hospital, Doctor–Patient Communication Office, Xingtai 054001, China
| | - Ling He
- Hebei Eye Hospital, Otolaryngology Head and Neck Surgery, Xingtai 054001, China
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Tack A, Ambellan F, Zachow S. Towards novel osteoarthritis biomarkers: Multi-criteria evaluation of 46,996 segmented knee MRI data from the Osteoarthritis Initiative. PLoS One 2021; 16:e0258855. [PMID: 34673842 PMCID: PMC8530341 DOI: 10.1371/journal.pone.0258855] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 10/06/2021] [Indexed: 01/16/2023] Open
Abstract
Convolutional neural networks (CNNs) are the state-of-the-art for automated assessment of knee osteoarthritis (KOA) from medical image data. However, these methods lack interpretability, mainly focus on image texture, and cannot completely grasp the analyzed anatomies' shapes. In this study we assess the informative value of quantitative features derived from segmentations in order to assess their potential as an alternative or extension to CNN-based approaches regarding multiple aspects of KOA. Six anatomical structures around the knee (femoral and tibial bones, femoral and tibial cartilages, and both menisci) are segmented in 46,996 MRI scans. Based on these segmentations, quantitative features are computed, i.e., measurements such as cartilage volume, meniscal extrusion and tibial coverage, as well as geometric features based on a statistical shape encoding of the anatomies. The feature quality is assessed by investigating their association to the Kellgren-Lawrence grade (KLG), joint space narrowing (JSN), incident KOA, and total knee replacement (TKR). Using gold standard labels from the Osteoarthritis Initiative database the balanced accuracy (BA), the area under the Receiver Operating Characteristic curve (AUC), and weighted kappa statistics are evaluated. Features based on shape encodings of femur, tibia, and menisci plus the performed measurements showed most potential as KOA biomarkers. Differentiation between non-arthritic and severely arthritic knees yielded BAs of up to 99%, 84% were achieved for diagnosis of early KOA. Weighted kappa values of 0.73, 0.72, and 0.78 were achieved for classification of the grade of medial JSN, lateral JSN, and KLG, respectively. The AUC was 0.61 and 0.76 for prediction of incident KOA and TKR within one year, respectively. Quantitative features from automated segmentations provide novel biomarkers for KLG and JSN classification and show potential for incident KOA and TKR prediction. The validity of these features should be further evaluated, especially as extensions of CNN-based approaches. To foster such developments we make all segmentations publicly available together with this publication.
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Affiliation(s)
| | | | - Stefan Zachow
- Zuse Institute Berlin, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Berlin, Germany
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Ntakolia C, Kokkotis C, Moustakidis S, Tsaopoulos D. Identification of most important features based on a fuzzy ensemble technique: Evaluation on joint space narrowing progression in knee osteoarthritis patients. Int J Med Inform 2021; 156:104614. [PMID: 34662820 DOI: 10.1016/j.ijmedinf.2021.104614] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 09/10/2021] [Accepted: 10/07/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Feature selection (FS) is a crucial and at the same time challenging processing step that aims to reduce the dimensionality of complex classification or regression problems. Various techniques have been proposed in the literature to address this challenge with emphasis to medical applications. However, each one of the existing FS algorithms come with its own advantages and disadvantages introducing a certain level of bias. MATERIALS AND METHODS To avoid bias and alleviate the defectiveness of single feature selection results, an ensemble FS methodology is proposed in this paper that aggregates the results of several FS algorithms (filter, wrapper and embedded ones). Fuzzy logic is employed to combine multiple feature importance scores thus leading to a more robust selection of informative features. The proposed fuzzy ensemble FS methodology was applied on the problem of knee osteoarthritis (KOA) prediction with special emphasis on the progression of joint space narrowing (JSN). The proposed FS methodology was integrated into an end-to-end machine learning pipeline and a thorough experimental evaluation was conducted using data from the Osteoarthritis Initiative (OAI) database. Several classifiers were investigated for their suitability in the task of JSN prediction and the best performing model was then post-hoc analyzed by using the SHAP method. RESULTS The results showed that the proposed method presented a better and more stable performance in contrast to other competitive feature selection methods, leading to an average accuracy of 78.14% using XG Boost at 31 selected features. The post-hoc explainability highlighted the important features that contribute to the classification of patients with JSN progression. CONCLUSIONS The proposed fuzzy feature selection approach improves the performance of the predictive models by selecting a small optimal subset of features compared to popular feature selection methods.
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Affiliation(s)
- Charis Ntakolia
- Hellenic National Center of COVID-19 Impact on Youth, University Mental Health Research Institute, Greece; School of Naval Architecture and Marine Engineering, National Technical University of Athens, 15772, Greece.
| | - Christos Kokkotis
- Institute for Bio-Economy and Agri-Technology, Center for Research and Technology Hellas, 38333, Greece; TEFAA, Department of Physical Education and Sport Science, University of Thessaly, 42100, Greece.
| | | | - Dimitrios Tsaopoulos
- Institute for Bio-Economy and Agri-Technology, Center for Research and Technology Hellas, 38333, Greece.
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Ningrum DNA, Kung WM, Tzeng IS, Yuan SP, Wu CC, Huang CY, Muhtar MS, Nguyen PA, Li JYC, Wang YC. A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record. J Multidiscip Healthc 2021; 14:2477-2485. [PMID: 34539180 PMCID: PMC8445097 DOI: 10.2147/jmdh.s325179] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 08/27/2021] [Indexed: 02/05/2023] Open
Abstract
PURPOSE To develop deep learning model (Deep-KOA) that can predict the risk of knee osteoarthritis (KOA) within the next year by using the previous three years nonimage-based electronic medical record (EMR) data. PATIENTS AND METHODS We randomly selected information of two million patients from the Taiwan National Health Insurance Research Database (NHIRD) from January 1, 1999 to December 31, 2013. During the study period, 132,594 patients were diagnosed with KOA, while 1,068,464 patients without KOA were chosen randomly as control. We constructed a feature matrix by using the three-year history of sequential diagnoses, drug prescriptions, age, and sex. Deep learning methods of convolutional neural network (CNN) and artificial neural network (ANN) were used together to develop a risk prediction model. We used the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and precision to evaluate the performance of Deep-KOA. Then, we explored the important features using stepwise feature selection. RESULTS This study included 132,594 KOA patients, 83,111 females (62.68%), 49,483 males (37.32%), mean age 64.2 years, and 1,068,464 non-KOA patients, 545,902 females (51.09%), 522,562 males (48.91%), mean age 51.00 years. The Deep-KOA achieved an overall AUROC, sensitivity, specificity, and precision of 0.97, 0.89, 0.93, and 0.80 respectively. The discriminative analysis of Deep-KOA showed important features from several diseases such as disorders of the eye and adnexa, acute respiratory infection, other metabolic and immunity disorders, and diseases of the musculoskeletal and connective tissue. Age and sex were not found as the most discriminative features, with AUROC of 0.9593 (-0.76% loss) and 0.9644 (-0.25% loss) respectively. Whereas medications including antacid, cough suppressant, and expectorants were identified as discriminative features. CONCLUSION Deep-KOA was developed to predict the risk of KOA within one year earlier, which may provide clues for clinical decision support systems to target patients with high risk of KOA to get precision prevention program.
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Affiliation(s)
- Dina Nur Anggraini Ningrum
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan
- Public Health Department, Faculty of Sport Science, Universitas Negeri Semarang, Semarang City, Indonesia
| | - Woon-Man Kung
- Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei, Taiwan
| | - I-Shiang Tzeng
- Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei, Taiwan
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan
- Department of Statistics, National Taipei University, Taipei, Taiwan
| | - Sheng-Po Yuan
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Department of Otorhinolaryngology, Shuang-Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Chieh-Chen Wu
- Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei, Taiwan
| | - Chu-Ya Huang
- Taiwan College of Healthcare Executives, Taipei, Taiwan
| | - Muhammad Solihuddin Muhtar
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan
| | - Phung-Anh Nguyen
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan
- Department of Healthcare Information and Management, Ming Chuan University, Taoyuan, Taiwan
| | - Jack Yu-Chuan Li
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan
- Department of Dermatology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yao-Chin Wang
- Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei, Taiwan
- Department of Emergency Medicine, Min-Sheng General Hospital, Taoyuan, Taiwan
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Almhdie-Imjabbar A, Podsiadlo P, Ljuhar R, Jennane R, Nguyen KL, Toumi H, Saarakkala S, Lespessailles E. Trabecular bone texture analysis of conventional radiographs in the assessment of knee osteoarthritis: review and viewpoint. Arthritis Res Ther 2021; 23:208. [PMID: 34362427 PMCID: PMC8344203 DOI: 10.1186/s13075-021-02594-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 07/28/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Trabecular bone texture analysis (TBTA) has been identified as an imaging biomarker that provides information on trabecular bone changes due to knee osteoarthritis (KOA). Consequently, it is important to conduct a comprehensive review that would permit a better understanding of this unfamiliar image analysis technique in the area of KOA research. We examined how TBTA, conducted on knee radiographs, is associated to (i) KOA incidence and progression, (ii) total knee arthroplasty, and (iii) KOA treatment responses. The primary aims of this study are twofold: to provide (i) a narrative review of the studies conducted on radiographic KOA using TBTA, and (ii) a viewpoint on future research priorities. METHOD Literature searches were performed in the PubMed electronic database. Studies published between June 1991 and March 2020 and related to traditional and fractal image analysis of trabecular bone texture (TBT) on knee radiographs were identified. RESULTS The search resulted in 219 papers. After title and abstract scanning, 39 studies were found eligible and then classified in accordance to six criteria: cross-sectional evaluation of osteoarthritis and non-osteoarthritis knees, understanding of bone microarchitecture, prediction of KOA progression, KOA incidence, and total knee arthroplasty and association with treatment response. Numerous studies have reported the relevance of TBTA as a potential bioimaging marker in the prediction of KOA incidence and progression. However, only a few studies have focused on the association of TBTA with both OA treatment responses and the prediction of knee joint replacement. CONCLUSION Clear evidence of biological plausibility for TBTA in KOA is already established. The review confirms the consistent association between TBT and important KOA endpoints such as KOA radiographic incidence and progression. TBTA could provide markers for enrichment of clinical trials enhancing the screening of KOA progressors. Major advances were made towards a fully automated assessment of KOA.
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Affiliation(s)
- Ahmad Almhdie-Imjabbar
- EA 4708- I3MTO Laboratory, University of Orleans, Orleans, France
- Translational Medicine Research Platform, PRIMMO, Regional Hospital of Orleans, Orleans, France
| | - Pawel Podsiadlo
- Tribology Laboratory, School of Civil and Mechanical Engineering, Curtin University, Bentley, WA, 6102, Australia
| | | | - Rachid Jennane
- EA 4708- I3MTO Laboratory, University of Orleans, Orleans, France
- Translational Medicine Research Platform, PRIMMO, Regional Hospital of Orleans, Orleans, France
| | - Khac-Lan Nguyen
- EA 4708- I3MTO Laboratory, University of Orleans, Orleans, France
- Translational Medicine Research Platform, PRIMMO, Regional Hospital of Orleans, Orleans, France
| | - Hechmi Toumi
- EA 4708- I3MTO Laboratory, University of Orleans, Orleans, France
- Translational Medicine Research Platform, PRIMMO, Regional Hospital of Orleans, Orleans, France
- Department of Rheumatology, Regional Hospital of Orleans, Orleans, France
| | - Simo Saarakkala
- Physics and Technology, Research Unit of Medical Imaging, University of Oulu, Oulu, Finland
- Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Eric Lespessailles
- EA 4708- I3MTO Laboratory, University of Orleans, Orleans, France.
- Translational Medicine Research Platform, PRIMMO, Regional Hospital of Orleans, Orleans, France.
- Department of Rheumatology, Regional Hospital of Orleans, Orleans, France.
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Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D. Machine Learning in Agriculture: A Comprehensive Updated Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:3758. [PMID: 34071553 PMCID: PMC8198852 DOI: 10.3390/s21113758] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/21/2021] [Accepted: 05/24/2021] [Indexed: 01/05/2023]
Abstract
The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords' combinations of "machine learning" along with "crop management", "water management", "soil management", and "livestock management", and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018-2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.
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Affiliation(s)
- Lefteris Benos
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
| | - Aristotelis C. Tagarakis
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
| | - Georgios Dolias
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
| | - Remigio Berruto
- Department of Agriculture, Forestry and Food Science (DISAFA), University of Turin, Largo Braccini 2, 10095 Grugliasco, Italy;
| | - Dimitrios Kateris
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
| | - Dionysis Bochtis
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
- FarmB Digital Agriculture P.C., Doiranis 17, GR 54639 Thessaloniki, Greece
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Kokkotis C, Moustakidis S, Baltzopoulos V, Giakas G, Tsaopoulos D. Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach. Healthcare (Basel) 2021; 9:260. [PMID: 33804560 PMCID: PMC8000487 DOI: 10.3390/healthcare9030260] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 02/22/2021] [Accepted: 02/23/2021] [Indexed: 12/27/2022] Open
Abstract
Knee osteoarthritis (KOA) is a multifactorial disease which is responsible for more than 80% of the osteoarthritis disease's total burden. KOA is heterogeneous in terms of rates of progression with several different phenotypes and a large number of risk factors, which often interact with each other. A number of modifiable and non-modifiable systemic and mechanical parameters along with comorbidities as well as pain-related factors contribute to the development of KOA. Although models exist to predict the onset of the disease or discriminate between asymptotic and OA patients, there are just a few studies in the recent literature that focused on the identification of risk factors associated with KOA progression. This paper contributes to the identification of risk factors for KOA progression via a robust feature selection (FS) methodology that overcomes two crucial challenges: (i) the observed high dimensionality and heterogeneity of the available data that are obtained from the Osteoarthritis Initiative (OAI) database and (ii) a severe class imbalance problem posed by the fact that the KOA progressors class is significantly smaller than the non-progressors' class. The proposed feature selection methodology relies on a combination of evolutionary algorithms and machine learning (ML) models, leading to the selection of a relatively small feature subset of 35 risk factors that generalizes well on the whole dataset (mean accuracy of 71.25%). We investigated the effectiveness of the proposed approach in a comparative analysis with well-known FS techniques with respect to metrics related to both prediction accuracy and generalization capability. The impact of the selected risk factors on the prediction output was further investigated using SHapley Additive exPlanations (SHAP). The proposed FS methodology may contribute to the development of new, efficient risk stratification strategies and identification of risk phenotypes of each KOA patient to enable appropriate interventions.
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Affiliation(s)
- Christos Kokkotis
- Institute for Bio-Economy & Agri-Technology, Center for Research and Technology Hellas, 60361 Volos, Greece;
- Department of Physical Education & Sport Science, University of Thessaly, 38221 Trikala, Greece;
| | | | - Vasilios Baltzopoulos
- Research Institute for Sport and Exercises Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK;
| | - Giannis Giakas
- Department of Physical Education & Sport Science, University of Thessaly, 38221 Trikala, Greece;
| | - Dimitrios Tsaopoulos
- Institute for Bio-Economy & Agri-Technology, Center for Research and Technology Hellas, 60361 Volos, Greece;
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Ntakolia C, Kokkotis C, Moustakidis S, Tsaopoulos D. Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients. Diagnostics (Basel) 2021; 11:285. [PMID: 33670414 PMCID: PMC7917818 DOI: 10.3390/diagnostics11020285] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/03/2021] [Accepted: 02/09/2021] [Indexed: 02/08/2023] Open
Abstract
Osteoarthritis is a joint disease that commonly occurs in the knee (KOA). The continuous increase in medical data regarding KOA has triggered researchers to incorporate artificial intelligence analytics for KOA prognosis or treatment. In this study, two approaches are presented to predict the progression of knee joint space narrowing (JSN) in each knee and in both knees combined. A machine learning approach is proposed with the use of multidisciplinary data from the osteoarthritis initiative database. The proposed methodology employs: (i) A clustering process to identify groups of people with progressing and non-progressing JSN; (ii) a robust feature selection (FS) process consisting of filter, wrapper, and embedded techniques that identifies the most informative risk factors; (iii) a decision making process based on the evaluation and comparison of various classification algorithms towards the selection and development of the final predictive model for JSN; and (iv) post-hoc interpretation of the features' impact on the best performing model. The results showed that bounding the JSN progression of both knees can result to more robust prediction models with a higher accuracy (83.3%) and with fewer risk factors (29) compared to the right knee (77.7%, 88 risk factors) and the left knee (78.3%, 164 risk factors), separately.
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Affiliation(s)
- Charis Ntakolia
- Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131 Lamia, Greece;
| | - Christos Kokkotis
- Institute for Bio-Economy & Agri-Technology, Center for Research and Technology Hellas, 38333 Volos, Greece;
- Department of Physical Education & Sport Science, University of Thessaly, 42100 Trikala, Greece
| | | | - Dimitrios Tsaopoulos
- Institute for Bio-Economy & Agri-Technology, Center for Research and Technology Hellas, 38333 Volos, Greece;
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Moustakidis S, Papandrianos NI, Christodolou E, Papageorgiou E, Tsaopoulos D. Dense neural networks in knee osteoarthritis classification: a study on accuracy and fairness. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05459-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Bernotiene E, Bagdonas E, Kirdaite G, Bernotas P, Kalvaityte U, Uzieliene I, Thudium CS, Hannula H, Lorite GS, Dvir-Ginzberg M, Guermazi A, Mobasheri A. Emerging Technologies and Platforms for the Immunodetection of Multiple Biochemical Markers in Osteoarthritis Research and Therapy. Front Med (Lausanne) 2020; 7:572977. [PMID: 33195320 PMCID: PMC7609858 DOI: 10.3389/fmed.2020.572977] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 08/31/2020] [Indexed: 12/12/2022] Open
Abstract
Biomarkers, especially biochemical markers, are important in osteoarthritis (OA) research, clinical trials, and drug development and have potential for more extensive use in therapeutic monitoring. However, they have not yet had any significant impact on disease diagnosis and follow-up in a clinical context. Nevertheless, the development of immunoassays for the detection and measurement of biochemical markers in OA research and therapy is an active area of research and development. The evaluation of biochemical markers representing low-grade inflammation or extracellular matrix turnover may permit OA prognosis and expedite the development of personalized treatment tailored to fit particular disease severities. However, currently detection methods have failed to overcome specific hurdles such as low biochemical marker concentrations, patient-specific variation, and limited utility of single biochemical markers for definitive characterization of disease status. These challenges require new and innovative approaches for development of detection and quantification systems that incorporate clinically relevant biochemical marker panels. Emerging platforms and technologies that are already on the way to implementation in routine diagnostics and monitoring of other diseases could potentially serve as good technological and strategic examples for better assessment of OA. State-of-the-art technologies such as advanced multiplex assays, enhanced immunoassays, and biosensors ensure simultaneous screening of a range of biochemical marker targets, the expansion of detection limits, low costs, and rapid analysis. This paper explores the implementation of such technologies in OA research and therapy. Application of novel immunoassay-based technologies may shed light on poorly understood mechanisms in disease pathogenesis and lead to the development of clinically relevant biochemical marker panels. More sensitive and specific biochemical marker immunodetection will complement imaging biomarkers and ensure evidence-based comparisons of intervention efficacy. We discuss the challenges hindering the development, testing, and implementation of new OA biochemical marker assays utilizing emerging multiplexing technologies and biosensors.
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Affiliation(s)
- Eiva Bernotiene
- Department of Regenerative Medicine, State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania
| | - Edvardas Bagdonas
- Department of Regenerative Medicine, State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania
| | - Gailute Kirdaite
- Department of Experimental, Preventive and Clinical Medicine, State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania
| | - Paulius Bernotas
- Department of Regenerative Medicine, State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania
| | - Ursule Kalvaityte
- Department of Regenerative Medicine, State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania
| | - Ilona Uzieliene
- Department of Regenerative Medicine, State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania
| | | | - Heidi Hannula
- Microelectronics Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
| | - Gabriela S. Lorite
- Microelectronics Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
| | - Mona Dvir-Ginzberg
- Laboratory of Cartilage Biology, Institute of Dental Sciences, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ali Guermazi
- Department of Radiology, Veterans Affairs Boston Healthcare System, Boston University School of Medicine, Boston, MA, United States
| | - Ali Mobasheri
- Department of Regenerative Medicine, State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Departments of Orthopedics, Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht, Netherlands
- Centre for Sport, Exercise and Osteoarthritis Versus Arthritis, Queen's Medical Centre, Nottingham, United Kingdom
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Identification of Risk Factors and Machine Learning-Based Prediction Models for Knee Osteoarthritis Patients. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10196797] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Knee Osteoarthritis (KOA) is a multifactorial disease that causes low quality of life, poor psychology and resignation from life. Furthermore, KOA is a big data problem in terms of data complexity, heterogeneity and size as it has been commonly considered in the literature with most of the reported studies being limited in the amount of information they can adequately process. The aim of this paper is: (i) To provide a robust feature selection (FS) approach that could identify important risk factors which contribute to the prediction of KOA and (ii) to develop machine learning (ML) prediction models for KOA. The current study considers multidisciplinary data from the osteoarthritis initiative (OAI) database, the available features of which come from heterogeneous sources such as questionnaire data, physical activity indexes, self-reported data about joint symptoms, disability and function as well as general health and physical exams’ data. The novelty of the proposed FS methodology lies on the combination of different well-known approaches including filter, wrapper and embedded techniques, whereas feature ranking is decided on the basis of a majority vote scheme to avoid bias. The validation of the selected factors was performed in data subgroups employing seven well-known classifiers in five different approaches. A 74.07% classification accuracy was achieved by SVM on the group of the first fifty-five selected risk factors. The effectiveness of the proposed approach was evaluated in a comparative analysis with respect to classification errors and confusion matrices to confirm its clinical relevance. The results are the basis for the development of reliable tools for the prediction of KOA progression.
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