Myocardial Amyloidosis Detection With Terahertz Spectroscopy

The application of THz technique into detecting myocardial amyloidosis exists some challenges: the irregularity of tissues, the overlap of spectra, the few sampling points and low signal-to-noise ratio (SNR) of spectra. In this paper, we propose an efficient method to construct the THz dataset of myocardial amyloidosis, which includes 4319 samples. Besides, we propose a convolutional neural networks (CNNs) based framework to detect myocardial amyloidosis. Benefiting from both the THz technique and CNNs, an accuracy of 98.37%, a precision of 97.94%, a recall of 98.85% and a F1-score of 98.39% are achieved. In 10 independent repeated tests, the extreme deviations of the four indicators obtained are within 0.65%, which proves that the proposed model has prominent stability. Furthermore, we have compared it with the existing sensors, which shows that our method provides innovation and improvement in the simplicity of operation, the efficiency of detection and the reliability of discrimination. To verify the comparability of CNNs, we compare it with the state-of-the-art (SOTA) algorithms and has achieved all the best performance under different number of samples. Not only that, under the circumstances of fewest sampling points and lowest SNR, the CNNs can still achieve a F1-score of 93.74% and 94.68%, respectively. This preliminary work indicates that the combination of THz-TDS and CNNs is of great potential in accurately and efficiently detecting myocardial amyloidosis.