This is a machine learning-based Knee osteoarthritis progression prediction model that utilizes radiographic data, clinical examination results and previous medical history of the patient. This model predicts risk of progression of Knee osteoarthritis which can help in early diagnosis and prevention of knee replacement surgery in future.Our method employs a Deep Convolutional Neural Network (CNN) that evaluates the probability of Osteoarthritis progression from a radiograph.Along with this we also use Gradient Boosting Machine to use clinical data for predictions.
Input variables : Patient Demographics, Clinical Data, Radiograph image
Output Variables : Knee osteoarthritis progression prediction
| Statistical | : | Somers D | Accuracy | Precision and Recall | Confusion Matrix | F1 Score | Roc and Auc | Prevalence | Detection Rate | Balanced Accuracy | Cohen's Kappa | Concordance | Gini Coefficent | KS Statistic | Youden's J Index |
| Business | : | Population at High Risk of Disease | Risk by Geography | Risk by Demographics | Risk by Clinical Parameters |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : github.com
Additional links : arxiv.org
| Model Category | : | Public |
| Date Published | : | May, 2019 |
| Healthcare Domain | : |
Medical Technology
Provider |
| Code | : | github.com |
| Health Risk Management |
| Chronic Care Management |
| Risk Progression |