The number of osteoporotic fracture cases is ever increasing, and it ranks among the top-five conditions causing disability and prolonged hospital stay. Porous bones break easily and heal badly, so an elderly person with a fracture may have to stay permanently in a rest home. The detrimental effect of long-standing pain and diminished quality of life, especially when followed by loss of independence, should not be underestimated. Elderly bone fracture takes a huge personal toll and social burden, but the current prevention method of osteoporotic fracture, as a golden diagnosis method, dual-energy x-ray absorptiometry (DXA) fails to effectively assess bone fracture risk, the clinical cinerary rate is less than 5.5% and the proportion of fractures attributable to osteoporosis (based on a standard definition of osteoporosis) is modest, ranging from 10% to 44% based on the most commonly used definition of osteoporosis (BMD T-score < -2.5).
To deal with this issue, based on our more than 20-years research experience in the area of osteoporosis mechanisms, we have a series of research and experimental findings in pathology, medical imaging, and biomechanics of osteoporosis and osteoporotic fracture. Therefore, we are developing a systematic clinical approach to assess elderly osteoporotic bone fracture risk. With a multidisciplinary team of experienced computer vision and image processing scientists, biomedical engineers, mechanics scientists and clinicians with a good track record of deep collaboration, the objectives are:
1. Develop a software for clinical practice on precise bone radiomics and biomechanical analysis of osteoporotic fracture risk by machine learning and image processing techniques;
2. Invent a novel patentable BMD phantom-less method for osteoporotic fracture risk evaluation by Localized BMD and radiomics biomarker analysis, and further improve the accuracy and sensitivity of the system.
Currently we have work in High-precision bone density partition measurement (Computer Vision) and 3D geometric morphology measurement (Computer Graphics) but may extend to related areas such as pattern classification and machine learning in automatic diagnosis of Osteoporotic fracture. With the real patient database from our partner hospitals, our software system should be able to early diagnosis elderly osteoporotic fracture risk. This will ultimately reduce the healthcare costs and improve the elderly’s quality of life.
We prefer the candidates who have the background on:
1. Research background: Computer Graphics/Vision, Medical Imaging Analysis, Radiology
2. Programming language: Good at Python, MATLAB
3. Software libraries: Familiar with OpenCV ITK, VTK, Scikit-learn
4. Deep learning framework: Familiar with TensorFlow/Keras/PyTorch
5. Familiar with 3D Meshing Algorithms, Mathematical Morphology Algorithms (Prefer)
William W. Lu, PhD, is Professor and Chairman of Departmental Research Postgraduate Committee of Department of Orthopedics and Traumatology, The University of Hong Kong. In 1995, he founded the Orthopaedic Research Center, and has been honored as one of the top 1% of scholars in medical engineering from 2009-2018 by ISI’s essential science indicators. In 2013, he was selected as Ng Chun-Man Professor in Orthopaedis Bioengineering. Prof Lu is also the Chief Scientist at Chinese Academy of Sciences and Honorary Professor of Faculty of Engineering, The University of Hong Kong.
His research interests are Digital orthopedics, biomechanics, biomaterials and the application of biomaterials for musculoskeletal degenerative diseases. He has been invited as visiting professor and speaker on multiple occasions to international meetings to lecture on these topics, and he has published more than 200 papers in top scientific journals with H-index = 53. Prof Lu sits on the editorial board of some top orthopedic research journals such as JBMR. He is the past Chairman of BME Division, The Hong Kong Institution of Engineers. He is member of the Biology and Medicine Panel of the Research Grants Council of Hong Kong to October 2021. Professor Lu has consistently secured significant funding for innovative research, including more than HK$47 millions in Hong Kong and $RMB20 million in PR China from a number of major funding bodies, such as National Key R&D Program of China, National Natural Science Foundation of China.
Supervisor’s information webpage link: https://www.ortho.hku.hk/biography/lu-weijia-william/
Faculty information, funding opportunities and application deadlines: https://www.findaphd.com/phds/program/biomedical-research-hku-li-ka-shing-faculty-of-medicine/?i586p4119