Diagnosis of Duchenne Muscular Dystrophy using Raman Hyperspectroscopy

Abstract

Duchenne muscular dystrophy (DMD) is the most common and severe form of muscular dystrophy and affects boys in infancy or early childhood. DMD is known to trigger progressive muscle weakness due to skeletal muscle degeneration and ultimately causes death. There are limited treatment regimens available that can either slow or stop the progression of DMD. An accurate and specific method for diagnosing DMD in its earliest stages is needed to prevent progressive muscle degeneration and death. Current methods for diagnosing DMD are often laborious, expensive, invasive, and typically diagnose the disease later on it is progression. In an effort to improve the accuracy and ease of diagnosis, this study focused on developing a novel method for diagnosing DMD which combines Raman hyperspectroscopic analysis of blood serum with advanced statistical analysis. Partial Least Squares Discriminant Analysis (PLS-DA), was applied to the spectral dataset acquired from control and mdx blood serum of 3-and 12-month old mice to build a diagnostic algorithm. Internal cross-validation showed 95.2% sensitivity and 94.6% specificity for identifying diseased spectra. These results were verified using external validation, which achieved 100% successful classification efficiency at the level of individual donor. This proof-of-concept study presents Raman hyperspectroscopic analysis of blood serum as a fast, nonexpensive, minimally invasive and early detection method for the diagnosis of Duchenne muscular dystrophy.

A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances

“…CNN was achieved to distinguish the Raman spectra of human and animal blood. [ 58 ] The proposed network consists of two preprocessing layers (noise reduction and baseline correction layer) and a fully connected classifier. [ 59 ] By optimizing the network structure, Sohn et al proposed a method based on a single‐layer multiple‐kernel‐based convolutional neural network (SLMK‐CNN) as an analytical tool for biological Raman spectroscopy.…”