Knee Osteoarthritis Prediction and Progression
A unified decision-support system integrating medical imaging, clinical biomarkers, and wearable IoT sensors for early detection and long-term monitoring of Knee Osteoarthritis.
Research Domain
Bridging clinical assessment, medical imaging, and continuous patient monitoring through advanced AI.
Literature Survey
Analysis of AI in KOA diagnosis using clinical databases, blood biomarkers, and CNN-based radiographic assessment (Kellgren-Lawrence grading).
Research Gap
Conventional methods rely on single-modality info and lack continuous monitoring, making real-life progression tracking difficult.
Research Problem
High cost of MRI/X-ray and impracticality of frequent hospital visits for elderly patients requiring long-term monitoring.
Research Objectives
Developing low-cost, explainable monitoring systems using IoT sensors and multimodal data fusion for clinical decision support.
Methodology
Phased approach: Clinical prediction (XGBoost), Binary Classification (YOLOv8), Severity Grading (EfficientNetB0), and IoT Wearable Monitoring.
Technologies Used
Python, TensorFlow, YOLOv8, XGBoost, ESP32, MPU6050 (Vibration), and MLX90614 (Thermal) sensors.
Project Milestones
Following our journey from the initial proposal to the final assessment. Each milestone represents a key phase in our research, design, and development process.
Project Proposal
Initial scope definition and hypothesis creation. Secured ethical approval from the National Hospital of Sri Lanka for patient data anonymization and research.
Marks: 10%Progress Presentation 1
Focus on binary classification models (YOLOv8) and literature survey completion. Initial preprocessing of X-ray and MRI datasets from hospital archives.
Marks: 15%Progress Presentation 2
Implementation of severity grading using EfficientNetB0 and late-fusion of clinical biomarkers via XGBoost. Integration of IoT sensor calibration for vibration analysis.
Marks: 15%Final Assessment
Comprehensive evaluation of the unified multimodal decision-support system. Final thesis submission and performance benchmarking against KL grading standards.
Marks: 40%Viva & Demonstration
Live demonstration of the wearable IoT subsystem and the web-based doctor monitoring portal. Formal oral examination by the clinical board.
Marks: 20%Project Documentation
Formal reports and research papers produced during the study.
Past Presentations
Visual slides for each evaluation phase.
Proposal Presentation
View SlidesProgress Presentation-1
View SlidesProgress Presentation-2
View SlidesFinal Presentation
View SlidesResearch Team
The innovators behind the KneeCare platform.
Jenny Krishara
Supervisor
jenny.k@sliit.lk
Thamali Dassanayake
Co-supervisor
thamali.d@sliit.lk
Fernando W.D.A
Data Scientist
IT22223708
fernando@kneecare.edu
Jayasinghe J.M.N.S.
IoT Developer
IT22582942
jayasinghe@kneecare.edu
Perera B.B.A.R
Deep Learning Specialist
IT22606792
perera@kneecare.edu
Gamage D.M.G.P.K
Lead Researcher
IT22188472
gamage@kneecare.edu
Contact Us
For research collaborations or inquiries regarding the KneeCare system.