AI駆動のモバイルヘルスアルゴリズムがスマホのカメラで血管の酸素濃度を検出する(AI-driven mobile health algorithm uses phone camera to detect blood vessel oxygen levels)


2023-06-07 パデュー大学



mHealthハイパースペクトル学習による血行動態の瞬間的なスペシオスペクトルイメージング mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics

Yuhyun Ji, Sang Mok Park, Semin Kwon, Jung Woo Leem, Vidhya Vijayakrishnan Nair, Yunjie Tong, Young L Kim
PNAS Nexus Published:29 March 2023

Schematic illustration of hyperspectral learning for instantaneous imaging using a conventional trichromatic camera (i.e. three-color image sensor). A sampling of detailed spectral (hyperspectral) information in a small yet representative subarea enables hyperspectral learning to recover a hypercube in the entire image as well as to perform spectrally driven machine learning for physical or biological parameter extractions. A) Hyperspectral learning for hypercube recovery. Hyperspectral data in a small subarea are used to train a learning algorithm that takes the RGB values in each pixel as the input and returns a spectrum with a high spectral resolution (also known as spectral superresolution). Hyperspectral learning is not affected by an intrinsic trade-off between spatial and spectral resolutions, which often limits conventional snapshot hyperspectral imaging. B) Subarea sampling of hyperspectral data. A dual-channel spectrograph with a photometric slit (Materials and methods) simultaneously acquires an RGB image in the entire field of view and a hyperspectral line scan in a small subarea (i.e. the central line) in a single-shot manner. The sampled hyperspectral data serve as prior information or physical constraints for training the hyperspectral learning algorithm. The spectrally driven informed machine learning algorithm trained by the sampled hyperspectral data transforms the RGB image into a hyperspectral image data set (also known as a hypercube), which can further be used to extract physical or biological parameters.


Hyperspectral imaging acquires data in both the spatial and frequency domains to offer abundant physical or biological information. However, conventional hyperspectral imaging has intrinsic limitations of bulky instruments, slow data acquisition rate, and spatiospectral trade-off. Here we introduce hyperspectral learning for snapshot hyperspectral imaging in which sampled hyperspectral data in a small subarea are incorporated into a learning algorithm to recover the hypercube. Hyperspectral learning exploits the idea that a photograph is more than merely a picture and contains detailed spectral information. A small sampling of hyperspectral data enables spectrally informed learning to recover a hypercube from a red–green–blue (RGB) image without complete hyperspectral measurements. Hyperspectral learning is capable of recovering full spectroscopic resolution in the hypercube, comparable to high spectral resolutions of scientific spectrometers. Hyperspectral learning also enables ultrafast dynamic imaging, leveraging ultraslow video recording in an off-the-shelf smartphone, given that a video comprises a time series of multiple RGB images. To demonstrate its versatility, an experimental model of vascular development is used to extract hemodynamic parameters via statistical and deep learning approaches. Subsequently, the hemodynamics of peripheral microcirculation is assessed at an ultrafast temporal resolution up to a millisecond, using a conventional smartphone camera. This spectrally informed learning method is analogous to compressed sensing; however, it further allows for reliable hypercube recovery and key feature extractions with a transparent learning algorithm. This learning-powered snapshot hyperspectral imaging method yields high spectral and temporal resolutions and eliminates the spatiospectral trade-off, offering simple hardware requirements and potential applications of various machine learning techniques.