Please Summary: Applying artificial intelligence $($AI$)$ in healthcare : Kim said the work reported in PNAS Nexus focused on building the smartphone hyperspectral imaging algorithm rather than specific applications. But in other studies, the team has used its approach to measure blood hemoglobin for tissue oximetry and inflammation. Kim's lab used a computational approach that the researchers have dubbed "hyperspectral learning."

The process begins with a smartphone camera on an ultra$-$slow$-$motion setting that produces video at about $1,000$ frames per second. Each pixel in each frame contains information for red, green and blue color intensity. The information is fed through a machine learning algorithm that infers full$-$spectrum information for each pixel.

That is used to produce the measurements of blood flow, particularly of the amount of oxygenated and deoxygenated hemoglobin in each pixel. These hemodynamic parameters can also be used to produce images and video that show oxygen saturation in their subjects over time.

As with conventional machine learning, the team trains its algorithms on a data set, feeding it smartphone images and the corresponding hyperspectral images and fine$-$tuning the algorithm until it can predict the correct relationship between the two data sets. But by building the algorithms with equations derived from tissue optics$$an approach sometimes called "informed learning"$$the researchers require a far smaller training data set.

And whereas conventional hyperspectral imaging equipment must gather massive amounts of data, limiting either the spectral resolution or temporal resolution, the team's approach begins with video files that are hundreds of times smaller than hyperspectral imaging files, allowing them to maintain a high standard on both fronts.

"Usually there's a trade$-$off to collect this information in an efficient manner. But with our approach, we have high spatial and spectral resolution at the same time," said Yuhyun Ji$,$ first author and a graduate student in Kim's lab, which is currently working on applying this method to other mobile health applications, such as cervix colposcopy and retinal fundus imaging.