Abstract & Details
Description
Award ID: 2127051
The broader impact/commercial potential of this Small Business Technology Transfer Program (STTR) Phase I project is to improve outcomes in dermatology and enable improved clinical care in the absence of specialists. The proposed technology enables cost-effective screening for cancer, psoriasis, atopic dermatitis, and other inflammatory skin conditions. In the United States, psoriasis affects about 8 million people while about 31.6 million people in the United States have some form of eczema, including atopic dermatitis. The proposed system generates a highly magnified image of the skin for analysis by a clinician or an artificial-intelligence based automated system. This Small Business Technology Transfer Program (STTR) Phase I project integrates several subsystems: 1) a scanner and software to capture and reconstruct super high-resolution (dermatoscope level) model of the entire skin surface of patients in a matter of minutes; 2) a multispectral illumination system to provide more information than currently available from a typical white-light systems, potentially leading to superior sensitivity and specify in lesion detection and classification; and 3) a system to find and classify lesions automatically from high-resolution images. This system advances the automated classification of images beyond those filtered previously by a clinician. Moreover, the system's UV illuminations system potentially can be used in a photodynamic treatment regimen. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
NSF Program Director: Henry Ahn
The broader impact/commercial potential of this Small Business Technology Transfer Program (STTR) Phase I project is to improve outcomes in dermatology and enable improved clinical care in the absence of specialists. The proposed technology enables cost-effective screening for cancer, psoriasis, atopic dermatitis, and other inflammatory skin conditions. In the United States, psoriasis affects about 8 million people while about 31.6 million people in the United States have some form of eczema, including atopic dermatitis. The proposed system generates a highly magnified image of the skin for analysis by a clinician or an artificial-intelligence based automated system. This Small Business Technology Transfer Program (STTR) Phase I project integrates several subsystems: 1) a scanner and software to capture and reconstruct super high-resolution (dermatoscope level) model of the entire skin surface of patients in a matter of minutes; 2) a multispectral illumination system to provide more information than currently available from a typical white-light systems, potentially leading to superior sensitivity and specify in lesion detection and classification; and 3) a system to find and classify lesions automatically from high-resolution images. This system advances the automated classification of images beyond those filtered previously by a clinician. Moreover, the system's UV illuminations system potentially can be used in a photodynamic treatment regimen. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
NSF Program Director: Henry Ahn
| Status | Closed |
|---|---|
| Effective start/end date | 08/01/21 → 12/31/22 |
Lead and Sub-Awardee Organization(s)
Funding
- STTR Phase I: $255,999.00
Active Fiscal Year
- FY2023
- FY2022
Start Fiscal Year
- FY2021
TIP Programs
- STTR Phase I
Small Business
- Yes
Key Technology Areas
- Artificial Intelligence
- (confidence score: 87%)
Technology Foci
- Machine Learning Training Data
- (confidence score: 92%)
Congressional District at Award
- District n. 08 of Maryland
Current Congressional District
- District n. 08 of Maryland
United States
- Maryland
Core Based Statistical Area (CBSA)
- Washington-Arlington-Alexandria, DC-VA-MD-WV
County
- County: Montgomery, MD
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint. Learn more about Elsevier's Fingerprint Engine here: https://beta.elsevier.com/products/elsevier-fingerprint-engine