Specialty: Orthopaedic Surgery
Sub-specialties: Adult Reconstruction Service
Conditions Treated by this Doctor:
Enhanced Recovery After Surgery (ERAS) Hip and Knee Replacement, Hip and Knee Osteoarthritis, Hip Arthroplasty, Joint Replacement, Knee Arthritis and Hip Arthritis, Knee Arthroplasty, Minimally Invasive Surgery (MIS) Joint Replacement, Outpatient Total and Unicompartmental Knee Replacement (Day Surgery), Robotic-assisted Knee Arthroplasty, Total Hip Replacement, Total Knee Replacement.
Dr Lincoln Liow is a Consultant Orthopaedic surgeon (Adult Reconstruction) in Singapore General Hospital. He performs 300-400 operations per year and specializes in Enhanced Recovery After Surgery (ERAS) for hip and knee replacements, robotic-assisted knee replacement and partial knee replacement surgery.
Dr Lincoln Liow was awarded the Singapore Armed Forces Medicine Scholarship to pursue medicine. He is currently a Fellow of the Royal College of Surgeons (Orthopaedic Surgery). During senior residency, he was appointed as the Chairman of the Singapore Orthopaedic Association Trainee Committee. He was also selected to undergo an Orthopaedic Biomechanics Research Fellowship at Massachusetts General Hospital (MGH), Harvard Medical School (HMS), Boston, USA in 2015. He has certification from the United States Educational Commission of Foreign Medical Graduates and Federation of State Medical Boards. He has secured a clinical fellowship position in Complex Joint Reconstruction at the Hospital of Special Surgery, New York, USA. He is currently pursuing a Master’s in Clinical Investigation at NUS.
Dr Liow is an academic arthroplasty surgeon and has published >100 peer reviewed articles. His work has been recognized at the international level, being nominated for the New Investigator Recognition Award (NIRA) during the 2016 Orthopaedic Research Society Annual Meeting, awarded the Jacques Duparc Award at the 2019 European Federation of National Associations of Orthopaedics and Traumatology (EFORT) and Top 100 posters of American Association of Hip and Knee Surgeons (AAHKS) Annual Meeting 2020. He serves as an editorial board member on several top-ranked Orthopaedic journals and is an invited peer-review for the British Medical Journal and Journal of Arthroplasty. He shares his knowledge on hip and knee replacement surgery as an invited speaker at international conference and webinars. As a clinical physician faculty, he is active in education of orthopaedic surgery residents.
Dr Liow has been awarded the Nurturing Clinician Scientist Scheme grant in 2020 to study the use of artificial intelligence in Orthopaedic imaging. He is currently interested in immunopathology of osteoarthritis, medical device development for osteoarthritis treatment, use of augmented reality in orthopaedic surgery and arthroplasty related biomechanics.
He is constantly studying the clinical outcomes of primary and revision knee and hip arthroplasty to provide the best care for his patients
RESEARCH GRANTSFY2020 Singhealth Duke-NUS Musculoskeletal Sciences Academic Clinical Programme Nurturing Clinician Scientist Scheme (NCSS) Research Support Grant – 13/FY2020/P1/18-A33Quantum: S$225,000
RESEARCH AND CLINICAL TRIALS AS PRINCIPAL INVESTIGATOR (ONGOING)
CIRB Ref No. : 2020/2866Protocol Title : A Propective Randomised, Controlled Clinical Trial to Compare the Functional Outcomes of Patients Undergoing Total Knee Replacement Using the Zimmer-Biomet Persona Total Knee System with Cruciate-Retaining or Medial Congruent Bearing
CIRB Ref No. : 2020/2750Protocol Title : A Review of the Functional Outcomes and Quality of Life after Revision Total Hip Arthroplasty
CIRB Ref No. : 2020/2237Protocol Title : A Review of the Functional Outcomes and Quality of Life after Revision Total Knee Arthroplasty
CIRB Ref No. : 2020/2316Protocol Title : Cost-Effectiveness of Robot-Assisted Total Knee Arthroplasty: A Markov Decision Analysis
CIRB Ref No. : 2019/2878Protocol Title : Enhancing appropriateness of surgical referral to improve accessibility and value in knee osteoarthritis: Development and validation of a deep-learning algorithm
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