How ALA Students Are Solving Real-World Problems: Golden Hue

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In many under-resourced hospitals, the problem is not treatment but detection. Without early diagnosis, jaundice can escalate quickly and silently.

Temitope set out to design a solution.

His initial concept explored using artificial intelligence to analyze photographs of a baby’s forehead to detect yellowing of the skin. However, the approach proved unreliable. Differences in lighting conditions and variations in skin tone made it difficult to produce consistent results.

The project soon pivoted toward a more precise scientific approach.

Golden Hue now uses spectrometry, a light-based technology similar to what is used in many smartwatches. By analyzing how light is reflected and absorbed by the skin, the system can estimate bilirubin levels in the blood. A regression model interprets these light patterns to determine bilirubin concentration without requiring invasive blood tests.

The project is currently in the deep software development phase as Temitope works to refine the model and improve accuracy.

The vision is clear: a low-cost, non-invasive tool that could help clinics detect dangerous bilirubin levels earlier and prevent a treatable condition from becoming a lifelong tragedy.

The next step is building a large and diverse dataset that will allow the AI model to be trained across different environments and skin tones. With enough data, Golden Hue could become a powerful diagnostic tool capable of improving neonatal care across the continent.

At ALA, innovation often begins with a simple observation. In Temitope’s case, a personal story has grown into a scientific project with the potential to save lives.

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