ArticleInternational journal of environmental research and public health2019
A Deep Neural Network-Based Method for Early Detection of Osteoarthritis Using Statistical Data.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it, 97 citations in OpenAlex.
- Machine Learning for the Analysis of Healthy Lifestyle Data: Scoping Review and Guidelines.JMIR human factors · 2026Guideline
- Pooled it
- Early Diagnosis of Knee Osteoarthritis With a Natural Language Processing-Driven Approach Based on Clinician Notes: Development and Validation Study.JMIR formative research · 2025Article
- A metaheuristic optimization-based approach for accurate prediction and classification of knee osteoarthritis.Scientific reports · 2025Article
- Clinical Progress in Mesenchymal Stem Cell Therapy: A Focus on Rheumatic Diseases.Immunity, inflammation and disease · 2025Review
- Advancing osteoarthritis research: the role of AI in clinical, imaging and omics fields.Bone research · 2025Review
- OA-HybridCNN (OHC): An advanced deep learning fusion model for enhanced diagnostic accuracy in knee osteoarthritis imaging.PloS one · 2025Article
- XGBoost-SHAP-based interpretable diagnostic framework for knee osteoarthritis: a population-based retrospective cohort study.Arthritis research & therapy · 2024Article
- Deep-NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data.CPT: pharmacometrics & systems pharmacology · 2024Article
- Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations.Cureus · 2024Review
- Regenerative Effect of Mesenchymal Stem Cell on Cartilage Damage in a Porcine Model.Biomedicines · 2023Article
- An Adaptive Early Stopping Technique for DenseNet169-Based Knee Osteoarthritis Detection Model.Diagnostics (Basel, Switzerland) · 2023Article
- Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach.Healthcare (Basel, Switzerland) · 2023Article
- Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- Digital Health Applications to Establish a Remote Diagnosis of Orthopedic Knee Disorders: Scoping Review.Journal of medical Internet research · 2023Article
- Advances in Mesenchymal Stem Cell Therapy for Osteoarthritis: From Preclinical and Clinical Perspectives.Bioengineering (Basel, Switzerland) · 2023Review
- Prediction Models for Knee Osteoarthritis: Review of Current Models and Future Directions.The archives of bone and joint surgery · 2023Review
- The application of machine learning in early diagnosis of osteoarthritis: a narrative review.Therapeutic advances in musculoskeletal disease · 2023Review
- Identifying Severity Grading of Knee Osteoarthritis from X-ray Images Using an Efficient Mixture of Deep Learning and Machine Learning Models.Diagnostics (Basel, Switzerland) · 2022Article
- A Comprehensive Survey on Bone Segmentation Techniques in Knee Osteoarthritis Research: From Conventional Methods to Deep Learning.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A large number of people suffer from certain types of osteoarthritis, such as knee, hip, and spine osteoarthritis. A correct prediction of osteoarthritis is an essential step to effectively diagnose and prevent severe osteoarthritis. Osteoarthritis is commonly diagnosed by experts through manual inspection of patients' medical images, which are usually collected in hospitals. Checking the occurrence of osteoarthritis is somewhat time-consuming for patients. In addition, the current studies are focused on automatically detecting osteoarthritis through image-based deep learning algorithms. This needs patients' medical images, which requires patients to visit the hospital. However, medical utilization and health behavior information as statistical data are easier to collect and access than medical images. Using indirect statistical data without any medical images to predict the occurrence of diverse forms of OA can have significant impacts on pro-active and preventive medical care. In this study, we used a deep neural network for detecting the occurrence of osteoarthritis using patient's statistical data of medical utilization and health behavior information. The study was based on 5749 subjects. Principal component analysis with quantile transformer scaling was employed to generate features from the patients' simple background medical records and identify the occurrence of osteoarthritis. Our experiments showed that the proposed method using deep neural network with scaled PCA resulted in 76.8% of area under the curve (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.