Research Projects
AI-Driven Digitalized Postoperative Outcome Prediction and Evaluation for Spine Surgery

Project Overview:
As spine surgery advances toward precision and personalization, postoperative outcome prediction and evaluation remain critical yet challenging tasks. Variability in surgical techniques, patient-specific anatomy, and postoperative recovery can significantly influence treatment success. Traditional assessment methods rely heavily on subjective clinical judgment and limited imaging follow-ups, making it difficult to achieve consistent, objective, and early predictions of surgical outcomes.

This project aims to integrate artificial intelligence (AI) into digital spine surgery workflows, developing a predictive model that leverages multimodal clinical data (preoperative imaging, intraoperative parameters, and postoperative assessments) to forecast patient recovery trajectories and evaluate surgical success. By combining deep learning, biomechanical modeling, and automated image analysis, we seek to transform the way spine surgery outcomes are monitored and optimized.

Key Objectives:

1. Develop an AI-powered predictive framework that utilizes preoperative and/or intraoperative imaging, patient-specific biomechanics, and clinical factors to estimate surgical outcomes and recovery trajectories.
2. Establish a multimodal dataset integrating radiological imaging, surgical parameters, and postoperative functional scores to train robust AI models for spine surgery assessment.
3. Design an intelligent evaluation system that automatically detects and quantifies postoperative spinal alignment, hardware placement accuracy, and potential complications from follow-up medical imaging.
4. Validate the AI-driven system through retrospective and prospective clinical studies, collaborating with spine surgeons to assess its real-world applicability and decision-support potential.

Why This Project?

• Cutting-Edge AI in Healthcare: Gain hands-on experience in deep learning, computer vision, and medical image analysis applied to real-world clinical challenges.
• Multidisciplinary Innovation: Work at the intersection of AI, biomechanics, and spine surgery, collaborating with engineers, radiologists, and surgeons.
• Clinical Impact: Contribute to developing next-generation surgical decision-support systems, ultimately improving patient outcomes and treatment precision.

This project offers an exciting opportunity for postgraduate students passionate about AI-driven healthcare innovations, medical image processing, and intelligent clinical decision-making. Passionate students with backgrounds in clinical medicine, bioinformatics, computer graphics, computer science, computational modelling, biomechanics, etc, are welcomed to apply. Join us in shaping the future of digital spine surgery

Dr N Meng, Department of Orthopaedics and Traumatology

Dr. Nan Meng is currently a Research Assistant Professor at HKU. His primary research interests include light field imaging, medical image analysis, digital health, and intelligent healthcare systems.

To date, he has published over 30 research papers, with his work appearing in top-tier international conferences and journals in computer science, such as IEEE T-PAMI, IEEE TIP, IEEE TCSVT, and AAAI, as well as in renowned medical journals including The Lancet and JAMA journals (eClinicalMedicine and JAMA Network Open).

Dr. Meng is currently leading three research projects and co-leading two additional projects, all funded by the Hong Kong Research Grants Council (RGC) and the National Natural Science Foundation of China (NSFC).

He serves as a Review Editor for MDPI Bioengineering and Frontiers in Surgery and has been an active reviewer for prestigious journals and conferences, including IEEE TIP, TCSVT, TNNLS, TCI, TMM, TMI, and The Lancet eClinicalMedicine. Additionally, he has been a regular reviewer for major international conferences such as IEEE EMBC, ISBI, and JBHI.

Biography
HKU Scholars Hub
Lab
ORCID
nanmeng@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.