Darrell Ingram
Papers
1
Total Citations
4
H-Index
1
About
Darrell Ingram is a researcher at the intersection of biomedical diagnostics and machine learning, with a focus on point-of-care technologies and automated image interpretation. His most notable work, "Adaptable Automated Interpretation of Rapid Diagnostic Tests Using Few-Shot Learning" (2021), addresses a critical bottleneck in deploying lateral-flow assays (LFAs) at scale: ensuring correct assay operation and result interpretation without human error. By leveraging few-shot learning, Ingram’s approach enables adaptable, automated analysis of rapid diagnostic tests, reducing the need for extensive training data and making these tools more accessible in resource-limited settings. This work has garnered 4 citations and highlights his contribution to making point-of-care diagnostics more reliable and scalable. Ingram’s research bridges computer vision and global health, offering practical solutions for timely disease surveillance and patient diagnosis. His efforts are particularly relevant in the context of pandemic preparedness, where rapid, accurate testing is paramount. Through his innovative use of AI, Ingram is helping to democratize diagnostic accuracy, ensuring that even in low-resource environments, test results can be trusted and acted upon swiftly.
Research Focus
Key Achievements
Top Papers
- 1