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Government Owned Inventions Available for Licensing/Collaboration: Using Artificial Intelligence To Diagnose Uveitis

89 Fed. Reg. 86343 (Oct. 30, 2024)

Government Owned Inventions Available for Licensing/Collaboration: Using Artificial Intelligence To Diagnose Uveitis

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Federal RegisterOct 30, 2024
89 Fed. Reg. 86343 (Oct. 30, 2024)
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    Department of Health and Human Services National Institutes of Health

    AGENCY:

    National Institutes of Health, HHS.

    ACTION:

    Notice.

    SUMMARY:

    The National Eye Institute seeks (NEI), an institute of the National Institutes of Health (NIH), Department of Health and Human Services (HHS), is giving notice of the licensing and collaboration opportunity for the inventions listed below, which are owned by an agency of the U.S. Government and are available for licensing/collaboration in the U.S. to achieve expeditious commercialization of results of federally-funded research and development.

    FOR FURTHER INFORMATION CONTACT:

    Inquiries related to this licensing/collaboration opportunity should be directed to: Hiba Alsaffar, Ph.D., Technology Transfer Manager, NCI, Technology Transfer Center, Email: hiba.alsaffar@nih.gov or Phone: 240-276-7489.

    SUPPLEMENTARY INFORMATION:

    Uveitis is caused by inflammation in the eye that can cause pain and reduce vision. The rate of uveitis in the United States is 1 in every 200 people with eye-related irritation. Permanent symptoms such as vision loss can occur if untreated. Therefore, early detection is crucial. In certain uveitis cases, fluorescein angiography (FA) is essential for the diagnosis and management due to its ability to display retinal vascular leakage (RVL). Although proven to be critical in diagnosing and assessing severity, FA is invasive and side effects have been reported. Additionally, the procedure is time-consuming and imposes economic burdens to patients, physicians and payors. Scientists at the NEI have developed a deep learning tool to non-invasively detect RVL using ultrawide-field color fundus photos. This algorithm identifies fundus images with and without RVL with high accuracy (79%) and sensitivity (85%). Compared to the current gold standard of assessing RVL (clinician interpretation), this deep learning tool provides an improved method of detecting RVL for patients with uveitis.

    This Notice is in accordance with 35 U.S.C. 209 and 37 CFR part 404.

    NIH Reference Number: E-005-2023-0.

    Potential Commercial Applications:

    • Diagnostic tool to predict uveitis.
    • Add-on to current color fundus imaging modalities.

    Competitive Advantages:

    • Greater accuracy and sensitivity versus current gold standard to assess RVL (clinician assessment).
    • Deep learning tool to assess RVL.
    • Deep learning to assess ultrawide-field color fundus images and assess RVL.

    Publication: Young LH, et al. Automated Detection of Vascular Leakage in Fluorescein Angiography—A Proof of Concept. (PMID 35877095).

    Patent Status: US Provisional Application 65/599,446 filed on November 15, 2023.

    Development Stage: Prototype.

    Therapeutic Area(s): Eye, Ear, Nose, Throat.

    Dated: October 24, 2024.

    Richard U. Rodriguez,

    Associate Director, Technology Transfer Center, National Cancer Institute.

    [FR Doc. 2024-25162 Filed 10-29-24; 8:45 am]

    BILLING CODE 4140-01-P