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…

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“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…

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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…

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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…

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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…

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Patient-specific Radiation Dose Estimates@AuntMinnie

http://www.auntminnie.com/index.aspx?sec=sup&sub=cto&pag=dis&ItemID=117679 AI can yield patient-specific radiation dose estimates By Erik L. Ridley, AuntMinnie staff writer Ridley reports,  “A machine-learning algorithm shows potential for facilitating the holy grail of real-time, patient-specific radiation dose estimates from CT scans, according to research presented at the Society for Imaging Informatics in Medicine (SIIM) annual meeting”.  “In a proof-of-concept study,…

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PICC Line Detection @ AuntMinnie

http://www.auntminnie.com/index.aspx?sec=sup&sub=aic&pag=dis&ItemID=117547 Deep-Learning Algorithm May Be Able To Detect An Incorrectly Positioned Peripherally Inserted Central Catheter, Study Suggests. Aunt Minnie (6/12, Ridley) reports “a deep-learning algorithm that can prescreen chest radiographs for an incorrectly positioned peripherally inserted central catheter (PICC) was presented at the recent Society for Imaging Informatics in Medicine (SIIM) annual meeting.” Researchers “developed…

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Positions Available

There are currently positions available for interns, PhD students, and Postdoctoral Fellows in the Laboratory of Medical Imaging and Computation. We invite outstanding individuals to join the vibrant collaborative research setting by working closely with MGH clinicians, researchers, and engineers. Please apply by sending a resume or CV to Dr. Synho Do at sdo@mgh.harvard.edu

“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)

 Authors: Hyunkwang Lee, Jordan Rogers, Junghwan Cho, Dania Daye, Vishala Mishra, Garry Choy, Shahein Tajmir, Michael Lev and Synho Do Congratulations! Your abstract has been accepted for presentation at the 2017 Annual Meeting of the Society for Imaging Informatics in Medicine (SIIM). The meeting will be held Thursday, June 1 – Saturday, June 3, 2017…

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“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)

Authors: Junghwan Cho, Eunmi Lee, Hyunkwang Lee, Bob Liu, Xinhua Li, Shahein Tajmir, Dushyant Sahani, and Synho Do The abstract has been accepted for presentation at the 2017 Annual Meeting of the Society for Imaging Informatics in Medicine (SIIM). The meeting will be held Thursday, June 1 – Saturday, June 3, 2017 at the David…

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