Dr. Ernest Lam

Session Date:  Friday October 25, 2024

Session Time:  1:30 PM – 4:30 PM

Presenter Name: Dr. Ernest Lam


When you discover something that looks unusual on a radiologic image, do you attempt to investigate the finding further yourself, or do you refer the patient to another practitioner for a diagnosis?  And when you receive the diagnosis, do you know what to do next?  In this session, we will discuss and explore the key radiologic features of 10 entities, some of which may be commonly seen, some of which may be variations of normal anatomy or hamartomas, and others of which may be “true lesions” that may require action and follow-up.

Learning objectives:

  • Develop a systematic strategy for investigating a radiologic finding.
  • Identify key radiologic features of common anomalies or abnormalities and understand these features in the context of the biologic process from which they have arisen.
  • Understand the necessity or urgency of a radiologic finding for action and follow-up.

About The Speaker

Professor Ernest Lam is a full-time, tenured full professor at the Faculty of Dentistry, University of Toronto.  He received DMD and MSc degrees from the University of British Columbia, and a Certificate in Oral and Maxillofacial Radiology and PhD in Radiation Biology from the University of Iowa.  Dr. Lam is a Fellow of the Royal College of Dentists of Canada and a Diplomate of the American Board of Oral and Maxillofacial Radiology.  Professor Lam is engaged in undergraduate and graduate teaching, and graduate research supervision. His research program has two broad objectives; to make oral and maxillofacial imaging safer for patients and to improve the diagnostic accuracy of clinicians. His work has included validating novel 3D imaging protocols in the oral and maxillofacial region, and identifying and assigning relevance to key radiologic features of disease. Most recently, Professor Lam’s work has quantified image artifacts from highly attenuating materials placed in the teeth and jaws in an attempt to better understand and evaluate the effectiveness of various artifact reduction strategies.