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PUBLICATIONS

LATEST NEWS

 

AWS Machine Learning Blog: Modernizing wound care with Spectral MD, powered by Amazon SageMaker - Aug 2019

https://aws.amazon.com/blogs/machine-learning/modernizing-wound-care-with-spectral-md-powered-by-amazon-sagemaker/

SpectralMD Press Release - July 2019

ASPR's Biomedical Advanced Research and Development Authority (BARDA) Press Release - July 2019

https://www.phe.gov/Preparedness/news/Pages/burn-imaging-18July19.aspx

CBRNE Central: U.S. invests in burn imaging device for mass casualty preparedness Article - July 2019

https://cbrnecentral.com/u-s-invests-27m-in-burn-imaging-device-for-mass-casualty-preparedness/19757/

 

Twitter News - July 2019:

https://twitter.com/PHEgov/status/1151859453635047425

 

https://twitter.com/BARDA/status/1151859864265797633

 

LinkedIn News - July 2019:

https://www.linkedin.com/feed/update/urn:li:activity:6557624536693514241

 

https://www.linkedin.com/feed/update/urn:li:activity:6557625835099078657

 

PEER REVIEWED MANUSCRIPTS

 

Thatcher et al. Imaging techniques for clinical burn assessment with a focus on multispectral imaging. Advances in Wound Care 2016; epub ahead of print. (Publication)

 

Thatcher et al. Multispectral and photoplethysmography optical imaging techniques identify important tissue characteristics in an animal model of tangential burn excision. Journal of Burn Care & Research 2016;37:38-52. (Publication)

 

Li et al. Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging. Journal of Biomedical Optics 2015;20:121305. (Publication)

 

King et al. Surgical wound debridement sequentially characterized in a porcine burn model with multispectral imaging. Burns 2015;41:1478-1487. (Publication)

 

Moza et al. Deep-tissue dynamic monitoring of decubitus ulcers: wound care and assessment. IEEE Engineering in Medicine and Biology Magazine 2010;29:71-77. (Publication)

 

CONFERENCE PROCEEDINGS

 

Squiers et al. Multispectral imaging burn wound tissue classification system: a comparison of test accuracies of several common machine learning algorithms. SPIE Proceedings Vol. 9785, Medical imaging 2016: Computer-Aided Diagnosis 2016:97853L. (Publication)

 

Li et al. Burn injury diagnostic imaging device's accuracy improved by outlier detection and removal. SPIE Proceedings Vol. 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI 2015:947206. (Publication)

 

Mo et al. The importance of illumination in a non-contact photoplethysmography imaging system for burn wound assessment. SPIE Proceedings Vol. 9303, Photonic Therapeutics and Diagnostics XI2015:9303M. (Publication)

 

Thatcher et al. Dynamic tissue phantoms and their use in assessment of a noninvasive optical plethysmography imaging device. SPIE Proceedings Vol. 9107, Smart Biomedical and Physiological Sensor Technology XI 2014:910718. (Publication)

CASE STUDIES

 

IBM Case Study Articles: