Research Projects
Artificial intelligence in arthritis, joint replacement and long term follow-up

Aim and Significance

Aim:
The primary aim of this project is to leverage artificial intelligence (AI) to enhance the diagnosis, treatment, and long-term management of arthritis and joint replacement. Specifically, the project seeks to:

• develop AI models that can predict the onset and progression of arthritis;
• improve the precision and outcomes of joint replacement surgeries through AI-driven planning and intraoperative guidance; and
• create an AI-based system for long-term follow-up to monitor patient recovery and predict complications.

Significance:
The significance of this project lies in its potential to revolutionize the field of orthopedics by:

• Early Diagnosis and Personalized Treatment: AI can identify early signs of arthritis, allowing for timely intervention and personalized treatment plans that can slow disease progression and improve patient quality of life.
• Enhanced Surgical Outcomes: AI can assist surgeons in planning and executing joint replacement surgeries with higher precision, potentially reducing complications and improving implant longevity.
• Improved Long-Term Care: AI systems can continuously monitor patients’ post-surgery, predicting complications before they become severe and ensuring that patients receive appropriate follow-up care, thereby improving overall outcomes and patient satisfaction.

Research Plan and Methodology

Research Plan:
Phase 1: Data Collection and Preprocessing

• Gather a comprehensive dataset from medical records, imaging studies (e.g., X-rays, MRIs), and patient history related to arthritis and joint replacements.
• Anonymize and preprocess the data to ensure quality and consistency.

Phase 2: AI Model Development

• Sub-phase 2.1: Arthritis Prediction Models
  Develop and train machine learning models using the collected data to predict the onset and progression of arthritis.
• Sub-phase 2.2: Surgical Planning and Guidance
  Create AI algorithms to assist in preoperative planning and intraoperative guidance for joint replacement surgeries.
• Sub-phase 2.3: Long-Term Follow-Up Systems
  Design an AI-based monitoring system to track patient recovery post-surgery, capable of predicting potential complications.

Phase 3: Validation and Testing

• Validate the developed models using a separate dataset to ensure accuracy and reliability.
• Conduct clinical trials to test the efficacy of AI-assisted surgical planning and long-term follow-up systems.

Phase 4: Implementation and Evaluation

• Implement the AI systems in clinical settings and evaluate their impact on patient outcomes, surgical success rates, and overall healthcare costs.

Methodology:
Data Collection and Preprocessing:

• Collaborate with hospitals and clinics to obtain relevant patient data.
• Use data cleaning techniques to handle missing values, normalize data, and ensure dataset quality.
• Apply data augmentation methods to enhance the dataset for model training.

Model Development:

• Utilize supervised learning techniques for arthritis prediction, employing algorithms such as logistic regression, decision trees, and neural networks.
• Integrate computer vision techniques for analyzing imaging data in surgical planning and guidance.
• Employ reinforcement learning and continuous monitoring algorithms for the long-term follow-up system.

Model Validation:

• Split the dataset into training, validation, and test sets to prevent overfitting and ensure model generalizability.
• Use cross-validation techniques to further validate the models.

Clinical Trials:

• Design and conduct randomized controlled trials to evaluate the performance of AI-assisted surgical planning and follow-up systems in real-world settings.
• Collect feedback from surgeons and patients to refine the AI models and systems.

Implementation:

• Develop user-friendly interfaces for the AI systems to facilitate their adoption in clinical practice.
• Train healthcare providers on the use of these AI tools to ensure smooth integration into existing workflows.

Professor LCM Lau, Department of Orthopaedics and Traumatology

Dr. Lawrence Lau graduated from the Chinese University of Hong Kong in 2014. He has undergone further postgraduate research study with special interest in arthritis and joint replacement and was awarded Doctor of Philosophy (PhD in Orthopaedics and Traumatology) in 2022. He completed the Joint Specialty Fellowship Examination in year 2021 by the Hong Kong College of Orthopaedic Surgeons and Royal College of Surgeons of Edinburgh. And he obtained his fellowship from Hong Kong College of Orthopaedic Surgeons, Royal College of Surgeons of Edinburgh and Hong Kong Academy of Medicine. He subsequently joined the University of Hong Kong in 2023 as Clinical Assistant Professor. An active surgeon and researcher, he has been invited to be Hong Kong Orthopaedics Association Ambassador to discuss his arthroplasty research in Singapore Orthopaedics Association and Japanese Orthopaedics Association.

He has won The David Fang Trophy as first author for the Best Adult Joint Reconstruction Specialist paper and Dr. Yeung Sai Hung Award as co-author in the Adult Joint Reconstruction Chapter of Hong Kong Orthopaedics Association. And his research on adult joint reconstruction has won a Gold Medal with Congratulation of the Jury, and a Bronze Medal in the 48th International Exhibition of Inventions Geneva. He is interested in developing cross-disciplinary collaboration with other medical, science and engineering experts to tackle problems on aging, osteoporosis, sarcopenia, arthritis, and joint replacement. He is regularly involved in undergraduate and postgraduate medical education, and experienced in nurturing medical students, juniors, and students from other disciplines to conduct independent research to develop their careers. He is also interested in promoting health knowledge to the public and serving in low resource areas.

Biography
HKU Scholars Hub
Lab
ORCID
laucml@hku.hk

For more information or to express interest for this project, please email the supervisor or the specified contact point in the project description.  Interested candidates are advised to enclose with your email:

1. your CV,
2. a brief description of your research interest and experience, and
3. two reference letters (not required for HKUMed UG students seeking MRes[Med]/URIS projects).

Information on the research programme, funding support and admission documentations could be referenced online at the Research Postgraduate Admissions website. General admission enquiries should be directed to rpgmed@hku.hk.

HKUMed MBBS students interested in the Master of Research in Medicine (MRes[Med]) programme may visit the programme website for more information.  

HKUMed UG students interested in the Undergraduate Research Internship Scheme (URIS) may visit the scheme’s website for more information.