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STTR Phase I: A Machine Learning Framework for Comprehensive Dental Caries Detection

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2013846
Agency Tracking Number: 2013846
Amount: $224,999.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: DH
Solicitation Number: N/A
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-07-01
Award End Date (Contract End Date): 2021-03-31
Small Business Information
651 N BROAD ST STE 205 #677, MIDDLETOWN, DE, 19709
DUNS: 117149862
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Daniel Lee
 (240) 271-8394
Business Contact
 Daniel Lee
Phone: (240) 271-8394
Research Institution
 University of Maryland at Baltimore
 Jeffery B Price
 620 W Lexington St, 4th Floor
Baltimore, MD, 21201
 Nonprofit college or university
The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project will be the development of an artificial intelligence software solution that enables automated detection of dental cavities in digital X-rays. Routine misdiagnosis of dental cavities (tooth decay) is a global challenge; cavities alone account for over 5% of healthcare costs in developed countries, with dental care focused on repairing rather than preventing tooth decay. This project will develop an add-on solution for software already in use by 200,000 dentists nationally. The technology resulting from this project will allow non-expert assistants to automate the triaging, screening, and tracking of patients, increasing access to oral care for underserved communities nationally and throughout the world. This Small Business Technology Transfer (STTR) Phase I project will demonstrate the feasibility of two key innovations: (1) a novel software framework using an innovative neural network algorithm for the detection of cavities in X-rays, and (2) the world’s largest database of dental radiographs annotated by specialists in oral radiology. The goals of R&D are to achieve high sensitivity and specificity in cavity detection and to ensure consistent high-quality annotations. Outcomes include: (1) achieving state-of-the-art performance in cavity detection, (2) outperforming domain experts in detecting all stages of cavities, and (3) enabling professionals and non-experts alike to interpret pathologies using a visual heatmap of prediction confidence. The proposed technology features an innovative neural network structure for learning visual representations of dental radiographs that jointly characterize the data while highlighting their most salient attributes. Using a new and original training procedure, the technology will maximize the benefit of existing unlabeled data. Technical challenges include scaling performance while maintaining a minimal false-negative rate, establishing interoperability under various calibration settings, and achieving the desired level of results on the types of machines used by customers with reasonable resource costs. 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.

* Information listed above is at the time of submission. *

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