Endoscopy System
Past New Requirement Updated
- Agency
-
Food and Drug Administration
Department of Health and Human Services
- Solicitation expected
- Estimated value
- $25,000 to $250,000
- Set-aside
- No set-aside
Forecast facts
- Anticipated award
- NAICS code
- 111333 Strawberry Farming
- Points of contact
-
- CDRH Acquisition Liaison cdhracquisitions@fda.hhs.gov
Forecast details come from HHS's procurement forecast. Agencies' dates and values are estimates.
About this forecast
The Food and Drug Administration is seeking to procure an endoscopy system as a new requirement to support research and development activities within the Office of Science and Engineering Laboratories (OSEL) and the Division of Imaging, Diagnostics, and Software Reliability (DIDSR). The endoscope will be utilized to develop bench testing methods for evaluating the safety and effectiveness of endoscopy devices, with particular emphasis on color performance testing and artificial intelligence/machine learning standalone test development. The solicitation is estimated to be released on March 1, 2026, with an estimated award date of May 1, 2026.
The estimated award value for this contract opportunity ranges from $25,000 to $250,000. This procurement is critical to enabling OSEL researchers to recommend practical and sufficient testing methodologies in regulatory submissions for sponsors and to evaluate how endoscopy properties and components impact AI/ML performance. Inquiries regarding this opportunity should be directed to the Food and Drug Administration's CDHR Acquisitions mailbox at cdhracquisitions@fda.hhs.gov.
Forecast text
Under FDA guidance, this collaboration between OSEL and DIDSR seeks to purchase an endoscope to ensure maximum scientific research capacity across the center and agency. An endoscope will enable OSEL researchers to develop bench testing methods for evaluating the safety and effectiveness of endoscopy devices. The system will be used for testing and developing color performance testing methods and for AI standalone test development as part of an AI/ML project under Feng Yang and Wei-Chung Cheng. Without this purchase, OSEL will be unable to recommend practical and sufficient testing methods in RSTs for sponsors to adopt and will be unable to evaluate how endoscopy properties and components impact AI/ML performance.
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