Frontiers Abstracts

Using 3D Modeling and Neural Networks to Predict Time-to-Heal for Chronic, Nonhealing Wounds

[+] Author and Article Information
Sagar Kamarthi

Northeastern University,
334 Snell Engineering,
360 Huntington Ave.,
Boston, MA 02115

1Corresponding author.

Manuscript received September 19, 2013; final manuscript received September 30, 2013; published online December 5, 2013. Editor: Gerald E. Miller.

J. Med. Devices 7(4), 040902 (Dec 05, 2013) (1 page) Paper No: MED-13-1213; doi: 10.1115/1.4025631 History: Received September 19, 2013; Revised September 30, 2013


Chronic wound is an important national healthcare problem, compounded by the fact that patients with chronic diseases such as diabetes are always vulnerable to develop chronic wounds. Wound care research has two strands: clinical and computational. On the clinical side, research has been focusing on how to effectively treat wounds. This includes measuring wounds, tracking their progression with time, and assessing their health. On the computational side, little has been done to treat a wound as an engineering system that needs to be modeled and analyzed with the ultimate goal of predicting the progress of wound healing and determining the factors that influence wound healing.

Copyright © 2013 by ASME
Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Fig. 1

Top and side view of wound shape and boundary



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In