Algorithm Uses Phone Camera, AI to Detect Blood Oxygen Levels

 Ai driven algorithm captures blood oxygen levels on Smartphone.  Note 'Hyperspectral Learning'.

Algorithm Uses Phone Camera to Detect Blood Oxygen Levels

By Purdue University, June 9, 2023

A smartphone camera paired with an AI-driven algorithm can capture blood oxygen data faster and more efficiently than highly specialized equipment.

The researchers used a computational approach that they described as “hyperspectral learning.”

Purdue University researchers have developed an algorithm to improve the speed and accuracy of medical diagnoses using smartphone sensors.

Although smartphone cameras capture only red, green, and blue wavelengths of light in each pixel, the researchers were able to reconstruct the full spectrum of visible light in each pixel of an image taken by a smartphone camera using deep learning, statistical techniques, and an understanding of light-tissue interactions.

The researchers found their technology produced information about blood oxygen levels in study participants' eyelids faster than commercially available hyperspectral imaging equipment, while being less expensive and just as accurate.

From Purdue University

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Background of Approaches using Deep Learning and Materials

Value of Deep Learning with Materials:  Via ChatGPT  5/14/2023,   See previous post

Deep learning is a subset of machine learning that uses artificial neural networks to model and solve complex problems. It has found numerous applications in materials science and engineering, ranging from the prediction of material properties to the design of novel materials with specific functionalities.

One of the key advantages of deep learning in materials science is its ability to learn from large datasets of materials data, allowing researchers to identify complex patterns and relationships that may be difficult or impossible to discern through traditional methods. This can lead to more accurate predictions of material properties, as well as the discovery of new materials with desirable properties.

Some examples of deep learning applications in materials science include:

Predicting the properties of materials: Deep learning can be used to predict various material properties such as melting point, conductivity, and strength, based on data from previous experiments or simulations. This can help accelerate the development of new materials for specific applications.

Designing new materials: Deep learning algorithms can be used to search for new materials with specific properties, based on desired criteria. For example, researchers have used deep learning to design new thermoelectric materials with high efficiency for energy conversion.

Analyzing images of materials: Deep learning can be used to analyze images of materials at different scales, from atomic structures to microstructures. This can help researchers better understand the properties and behavior of materials, as well as identify defects or anomalies.

Overall, deep learning has the potential to revolutionize materials science and accelerate the development of new materials with desirable properties and functionalities.  ... 

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