| Project Id
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BITS025F001500
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| Project Detail
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| Project Title
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Early Detection of Knee Osteoarthritis from MRI scans and clinical data using deep learning
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| Senior Supervision Team (BITS)
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| Supervisor name and Title
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Sougata Sen
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School or Department (or company, if applicable)
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BITS PILANI, GOA CAMPUS
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|
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Email ID
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sougatas@goa.bits-pilani.ac.in
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| URL for more info
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http://www.sougata-sen.com/
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| a) Are you currently supervising a BITS or RMIT HDR student?
|
YES
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| Please comment how many you are supervising
|
2
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| b) Have you supervised an offshore candidate before?
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NO
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| If no, what support structures do you have in place?
|
|
| If yes, please elaborate
|
N
|
| Senior Supervision Team (RMIT)
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| Supervisor name and Title
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Dr Priya Rani
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School or Department (or company, if applicable)
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STEM
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|
|
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Email ID
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priya.rani@rmit.edu.au
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| URL for more info
|
https://www.rmit.edu.au/contact/staff-contacts/academic-staff/r/rani-dr-priya
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| a) Are you currently supervising a BITS or RMIT HDR student?
|
YES
|
| Please comment how many you are supervising
|
3
|
| b) Have you supervised an offshore candidate before?
|
NO
|
| If no, what support structures do you have in place?
|
|
| If yes, please elaborate
|
|
| Other Supervisors (BITS)
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| Supervisor name and Title
|
|
School or Department (or company, if applicable)
|
|
| Phone Number (Optional)
|
|
Email ID
|
|
| URL for more info
|
|
| Other Supervisors (RMIT)
|
| Supervisor name and Title
|
|
School or Department (or company, if applicable)
|
|
| Phone Number (Optional)
|
|
Email ID
|
|
| URL for more info
|
|
| Field of Research (For Codes)
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| 400399 | Biomedical engineering not elsewhere classified | 30.00 |
| 460307 | Multimodal analysis and synthesis | 30.00 |
| 461199 | Machine learning not elsewhere classified | 40.00 |
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| Project Description
|
|
Knee Osteoarthritis (OA) is the most common type of diagnosed osteoarthritis and is typically the result of wear and tear, and progressive loss of articular cartilage in the knee. Early detection of knee OA is important so that therapeutic approaches can be considered to halt
knee OA progression. Diagnosis of the disease is currently performed using X-ray and MRI that confirm and distinguish knee OA from other forms of arthritis. However, plain radiographs do not detect definite evidence of knee OA in the early stages because the bony changes on the radiograph only appear in advanced conditions. MRI scans, in comparison, have excellent tissue contrast, and distinct resolution on knee joints that could help in the early detection of the disease. This project aims to develop a novel deep learning model that would use MRI scans along with clinical information (demographic information, medical history, etc.) to perform early detection of knee osteoarthritis. The study will first implement open access databases to test the developed model and then implement it on the data collected from the hospitals who will be collaborators on this project. This would ensure that the developed model gets validated by clinicians and can be implemented in real world settings. The developed application has the potential to revolutionize detection and diagnosis of the most common type of osteoarthritis across the globe.
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| Project Deliverable/Outcomes
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1. Collaboration with hospitals for data collection and to test the final outcome of the model in a clinical setting.
2. Collection and integration of multimodal data from hospitals.
3. Development of image processing algorithms and deep learning models that can accurately predict Knee Osteoarthritis using multi-modal data obtained from multiple online databases.
4. Testing of the developed algorithms on real data collected from the hospitals.
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| Research Impact Themes
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| BETTER HEALTH OUTCOMES | AFFORDABLE HEALTH AND PREVENTABLE DISEASES |
| ADVANCED DIGITAL TECHNOLOGIES AND BUSINESS TRANSFORMATION | DEEP LEARNING AND PREDICTIVE MODELLING |
| AI/ML and Data Analytics / Data Science with a focus on applications/translation in | Healthcare & Biotechnology |
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| Which RMIT Sustainable Development Goal (SDG) does your project align to
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|
GOOD HEALTH AND WELLBEING
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| Which RMIT Enabling Impact Platform (EIP) does your project align to
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BIOMEDICAL AND HEALTH INNOVATION
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| Which RMIT Program code will this project sit under?
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| Student Capabilities and Qualifications
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Competency in coding in Matlab/ Python, competency in basic image processing skills
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Interest and desire to learn image processing, machine learning and deep learning
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MTech in Software Engineering, Biomedical Engineering, Electrical/ Electronic Engineering or a related Engineering field.
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| Preferred discipline of Student
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| Artificial Intelligence, Deep Learning, Information Extraction & Knowledge Extraction, Machine Learning, Natural Language Processing |
| Biochemistry, Bioengineering, Biomaterials, Biotech, Biomed Eng/Sciences, Bioinformatics |
| Electrical and Electronics Engineering, Power Engineering |
| Health, Digital Health |
|