Lab of Medical Imaging and Computation

To Become Leaders in AI, Radiologists Must Address a Variety of Challenges

Radiology Business, Feb 16, 2018, Michael Walter Artificial intelligence (AI) is one of the biggest topics in healthcare today, and the authors of a recent analysis published in the Journal of the American College of Radiology (“Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success” James H. Thrall, Xiang Li, Quanzheng Li, Cinthia Cruz, Synho Do, Keith…

Four Paper Abstracts Accepted for Presentation at the 2018 Annual Meeting of the Society for Imaging Informatics in Medicine (SIIM)

“Patient- and Organ-Specific CT Radiation Dose Estimation Using AI-based Body Part Classifier : MGH 23 Body-Part and NCICT” Authors: Myeongchan Kim, Sehyo Yune, Hyunkwang Lee, Bob Liu, Xinhua Li, Choonsik Lee, Synho Do “What Machines Can Read: Gender Identification from Hand and Wrist Radiographs in Children” Authors: Sehyo Yune, Hyunkwang Lee, Myeongchan Kim, Shahein Tajmir, Michael Gee, Michael Lev,…

Forum for Advanced Biomedical Computation

Forum for Advanced Biomedical Computation Join us for an in-depth discussion of computational methods applied to the understanding, diagnosis and treatment of human disease. MONDAY, FEBRUARY 5, 2018, 4:00PM-5:30 PM RAMZI COTRAN CONFERENCE CENTER AMORY 3, 75 FRANCIS STREET, BRIGHAM AND WOMEN’S HOSPITAL The Hype and Hope of Artificial Intelligence in Medicine A panel discussion on…

SIIM 2017 New Investigator Travel Award Winner

SIIM 2017 Awards: New Investigator Travel Travel awards are presented to new investigators with an area of study in imaging informatics and who either are currently in a full-time training program or have completed their training program within 2 years of the SIIM Annual Meeting. Congratulations to Hyunkwang Lee, Harvard John A. Paulson School of…

2018 ICT Industry Prospect Conference

MGH Clinical Research Day 2017 Team Award Winner

Clinical Research Day is an annual celebration of clinical and translational investigators and their accomplishments over the past twelve months at the Massachusetts General Hospital (MGH). This year, among 340 abstract submissions reflecting the exciting work being conducted by clinical research teams here at MGH, “Deep Learning for Rapid, Accurate, Automated Detection & Characterization of Intracranial…

“A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection” Published in Journal of Digital Imaging

“A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection” is now available to view at Springer Nature SharedIt. Please follow the link below. Dear Author, Congratulations on publishing “A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection” in…

AI Gives One-Stop Shopping for Urinary Stone Evaluation

http://www.auntminnie.com/index.aspx?sec=sup&sub=aic&pag=dis&ItemID=118413 AuntMinnie.com, 10/3/2017, Erik L. Ridley An artificial intelligence (AI) algorithm can accurately detect and classify urinary stones based solely on images from noncontrast single-energy CT scans, according to research presented at last week’s Society for Imaging Informatics in Medicine’s Conference on Machine Intelligence in Medical Imaging (C-MIMI) in Baltimore. In a proof-of-concept study, an…

Bone Age Assessment with Artificial Intelligence

http://www.massgeneral.org/imaging/news/radiology-rounds/september-2017/bone-age-assessment-with-artificial-intelligence/ https://link.springer.com/article/10.1007/s10278-017-9955-8 Radiology Rounds, September 2017 – Volume 15, Issue 9, Gary Boas Bone age assessment is the evaluation of skeletal maturity. For decades, it was determined by examining the X-ray of a patient’s hand and wrist and matching it using an atlas of 200 images. Artificial intelligence (AI) is manifest when machines develop an…

Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis

https://link.springer.com/article/10.1007/s10278-017-9988-z/fulltext.html Paper published titled, “Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis” Lee, H., Troschel, F.M., Tajmir, S. et al. J Digit Imaging (2017). doi:10.1007/s10278-017-9988-z. Pretreatment risk stratification is key for personalized medicine. While many physicians rely on an “eyeball test” to assess whether patients will tolerate major surgery or…