Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis by Using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative
| Autor: | Krzysztof J. Geras, Cem M. Deniz, Kyunghyun Cho, Gregory Chang, Kevin Leung, Yiqiu Shen, Bofei Zhang, James S. Babb, Jimin Tan |
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| Rok vydání: | 2020 |
| Předmět: |
musculoskeletal diseases
Male medicine.medical_specialty Knee Joint medicine.medical_treatment Radiography Total knee replacement Osteoarthritis Article 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Deep Learning Risk Factors Image Interpretation Computer-Assisted medicine Humans Radiology Nuclear Medicine and imaging Arthroplasty Replacement Knee Aged Retrospective Studies Extramural Binary outcome business.industry Deep learning Retrospective cohort study Middle Aged Osteoarthritis Knee musculoskeletal system medicine.disease Arthroplasty 030220 oncology & carcinogenesis Physical therapy Female Artificial intelligence business human activities |
| Zdroj: | Radiology |
| ISSN: | 1527-1315 |
| Popis: | BACKGROUND: The methods for assessing knee osteoarthritis (OA) do not provide enough comprehensive information to make robust and accurate outcome predictions. PURPOSE: To develop a deep learning (DL) prediction model for risk of OA progression by using knee radiographs in patients who underwent total knee replacement (TKR) and matched control patients who did not undergo TKR. MATERIALS AND METHODS: In this retrospective analysis that used data from the OA Initiative, a DL model on knee radiographs was developed to predict both the likelihood of a patient undergoing TKR within 9 years and Kellgren-Lawrence (KL) grade. Study participants included a case-control matched subcohort between 45 and 79 years. Patients were matched to control patients according to age, sex, ethnicity, and body mass index. The proposed model used a transfer learning approach based on the ResNet34 architecture with sevenfold nested cross-validation. Receiver operating characteristic curve analysis and conditional logistic regression assessed model performance for predicting probability and risk of TKR compared with clinical observations and two binary outcome prediction models on the basis of radiographic readings: KL grade and OA Research Society International (OARSI) grade. RESULTS: Evaluated were 728 participants including 324 patients (mean age, 64 years ± 8 [standard deviation]; 222 women) and 324 control patients (mean age, 64 years ± 8; 222 women). The prediction model based on DL achieved an area under the receiver operating characteristic curve (AUC) of 0.87 (95% confidence interval [CI]: 0.85, 0.90), outperforming a baseline prediction model by using KL grade with an AUC of 0.74 (95% CI: 0.71, 0.77; P < .001). The risk for TKR increased with probability that a person will undergo TKR from the DL model (odds ratio [OR], 7.7; 95% CI: 2.3, 25; P < .001), KL grade (OR, 1.92; 95% CI: 1.17, 3.13; P = .009), and OARSI grade (OR, 1.20; 95% CI: 0.41, 3.50; P = .73). CONCLUSION: The proposed deep learning model better predicted risk of total knee replacement in osteoarthritis than did binary outcome models by using standard grading systems. |
| Databáze: | OpenAIRE |
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