Dr. Synho Do is Director of the Laboratory of Medical Imaging and Computation (LMIC). He has a MS in Electrical Engineering (cryptosystem analysis) and a PhD degree in Biomedical Engineering (nonlinear biological system analysis). As a NIH T32 fellow, Dr. Do received clinical training in the Cardiac MR PET CT program, He then built his team of scientists, clinicians, and mentors as an instructor at Massachusetts General Hospital, Harvard Medical School. He is currently an Assistant Professor of Radiology at Harvard Medical School and Assistant Medical Director for Advanced Health Technology Engineering, Research, and Development within the Massachusetts General Physicians Organization (MGPO). His research interests are healthcare data machine learning, high performance computing, nonlinear system identification, complex system modeling, and clinical workflow understanding.
Latest News
- Invited Talk: World Congress on Information Technology (Taipei, Taiwan)
- Our Bone Age Assessment AI is deployed at MGH radiology-rounds-september-2017.pdf
- GPU Technology Conference, AI in Healthcare Summit, Silicon Valley, May 8-11 2017
- Predicting multidisciplinary tumor board recommendations: Initial experience with machine learning in Interventional Oncology
- "Machine Intelligence for Accurate X-ray Screening and Read-out Prioritization: PICC line Detection Study" : Accepted for presentation at the 2017 Annual Meeting of the Society for Imaging Informatics in Medicine (SIIM)
- "Machine Learning Powered Automatic Organ Classification for Patient Specific Organ Dose Estimation" Accepted for presentation at the 2017 Annual Meeting of the Society of Imaging Informatics in Medicine (SIIM)
Recent Publications
- Machine Learning Powered Automatic Organ Classification for Patient Specific Organ Dose Estimation
- Machine Intelligence for Accurate X-ray Screening and Read-out Prioritization: PICC line Detection Study
- Iterative Image Reconstruction Improves the Accuracy of Automated Plaque Burden Assessment in Coronary CT Angiography: A Comparison With Intravascular Ultrasound
- Fully Automated Deep Learning System for Bone Age Assessment
- Predicting multidisciplinary tumor board recommendations: Initial experience with machine learning in interventional oncology
- The future of artificial intelligence for physicians (인공지능과 의사의 미래)