Deep learning techniques for diffuse correlation spectroscopy: A review

Diffuse correlation spectroscopy (DCS) is an optical technology for extracting blood flow index (BFi) by measuring intensity fluctuations of the back-scattered light emitted from tissues. The remarkable characteristics of DCS, such as its noninvasiveness, deep penetration depth, and cost-effectiveness, have led to its widespread application for human health evaluation. However, traditional DCS data processing utilizes the analytical solution of the correlation diffusion equation to fit the measured autocorrelation function [Formula: see text], which is computationally demanding and susceptible to noise, especially when using the multi-layer analytical models to detect cerebral BFi. These drawbacks limit its further application for human BFi monitoring. To accelerate BFi extraction, deep learning (DL) techniques have been introduced. DL-assisted DCS has demonstrated fast and improved noise robustness for BFi extraction compared to traditional curve-fitting methods. In this review, the development of DL models for DCS data processing, current challenges and outlooks will be discussed, aiming to provide a reference for further promotion of the development of DL-based DCS technology for human health analysis.