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The Effectiveness of Automated Multimodal Imaging in High Grade Squamous Intraepithelial Lesions (HSIL) Diagnosis for People Living with HIV: An International Trial

US · IL NIH grant awarded #nih-5R01CA232890-07

Summary

This project aims to develop and evaluate an automated, AI-based multimodal imaging platform (HRA-HRME) for the accurate and efficient diagnosis of high-grade squamous intraepithelial lesions (HSIL) in people living with HIV, particularly in low-resource settings.

What they want

The project involves developing computer-assisted AI-based identification of HRA-suspicious areas to enhance a Mobile High-Resolution Micro-Endoscope (HRME) into a fully automated AI-based HRA-HRME platform. This platform will then be evaluated in diverse clinical environments in the USA and Brazil to assess its efficiency, accuracy, and clinical impact, especially for novice HRA providers. The research will also assess barriers and facilitators for adoption and utilization of the AI HRA-HRME in various global health settings.
Technical requirements
  • Mobile High-Resolution Micro-Endoscope (HRME) optimization and validation
  • Faster-frame capture HRME (70 frames/sec)
  • Automated, machine learning-based algorithms for image interpretation
  • Computer-assisted AI-based identification of HRA-suspicious areas
  • AI-based HRA-HRME platform

Market context

inferred from NAICS
R&D in Physical, Engineering, Life Sciences (except Nanotech & Biotech)
NAICS 541715
US market size
$95B
Typical award
$100K – $50M+
Typical buyers
DoDNSFNIHNASADOE
Commonly required
DCAA-compliant accountingITARCMMC L2
The Effectiveness of Automated Multimodal …
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