Diagnosing Musculoskeletal Conditions using Artifical Intelligence and Machine Learning to Aid Interpretation of Clinical Imaging

Dr. Rakesh Kumar is working as a Professor in the Department of Computer Science Engineering at Chandigarh University, Punjab, India. He did his Ph.D. from Punjab Technical University, Jalandhar, in Computer Science and Engineering in 2017. He has published many authored books with reputed publishers. His research interests are IoT, Machine Learning, and Natural Language Processing. A total of 19+ Years of Academic / Research Experience with more than 100 Publications in various National/International Conferences and Journals. He also organized three international conferences under the banner of IEEE Explore and AIP publisher. Dr. Meenu Gupta is an Associate Professor at the UIE-CSE Department, Chandigarh University, India. She completed her Ph.D. in Computer Science and Engineering with an emphasis on Traffic Accident Severity Problems from Ansal University, Gurgaon, India, in 2020. She has more than 15 years of teaching experience. Her research areas cover Machine Learning, Intelligent Systems, and Data mining, with a specific interest in Artificial Intelligence, Image Processing and Analysis, Smart Cities, Data Analysis, and Human/Brain-machine Interaction (BMI). She has five edited and four authored books. She has also authored or co-authored more than 20 book chapters and over 80 papers in refereed international journals and conferences. She has five filled patents and was awarded the best faculty and department researcher in 2021 and 2022. Dr. Ajith Abraham is a Pro Vice-Chancellor at Bennette University. He is the director of Machine Intelligence Research Labs (MIR Labs), Australia. MIR Labs are a not-for-profit scientific network for innovation and research excellence connecting industry and academia. His research focuses on real world problems in the fields of machine intelligence, cyber-physical systems, Internet of things, network security, sensor networks, Web intelligence, Web services, and data mining. He is the Chair of the IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing. He is editor-in-chief of Engineering Applications of Artificial Intelligence (EAAI) and serves on the editorial board of several international journals. He received his PhD in Computer Science from Monash University, Melbourne, Australia. Dr. Paul Antony is working as Senior and Lead consultant in Department of Orthopaedics at Apollo Super speciality Hospital Muscat, Oman. He did his postgraduate degree from Christian Medical college, Ludhiana from Panjab University in 1998 and did his SICOT & AO fellowship in shoulder and knee surgeries from Germany . He has more than 24 years experience in the field of arthroplasty and arthroscopic surgeries of shoulder, knee and hip joints.

1. An Introductory approach for Bone Deformities, Osteoarthrosis, Osteoporosis, and Spondylosis of Spine using Machine Learning
2. Identification and Classification of Musculoskeletal Conditions using Artificial Intelligence and Machine Learning
3. Early-stage detection of osteoarthritis of the joints (hip and knee) using Machine Learning
4. Bone cancer classification and detection using Machine Learning techniques
5. Identification of the risk of osteoporosis in older Vietnamese women using Artificial Intelligence and Machine Learning
6. Identification of Paget's Disease in the Human body using Artificial Intelligence and Machine Learning
7. Identification of Osteonecrosis using Artificial Intelligence and Machine Learning
8. Identification and Classification of Rheumatoid Arthritis using Artificial Intelligence and Machine Learning
9. Early detection approach for Analysis of Osteoarthritis using Artificial Intelligence and Machine Learning
10. Pre-analysis of ankylosing spondylitis using Machine Learning
11. Analysis and identification of gout flares using Machine Learning
12. Impact of Amyloidosis on Bones and its relationship with dementia
13. Real-world application of Machine learning in Bone Deformities identification
14. Conclusion