Deep Learning and Photoacoustic Technology for Microcirculation Classification: Comparison Between Smoking and Nonsmoking Groups

Abstract

Smoking has a significant impact on microcirculation, but existing tools for monitoring circulation perfusion in the smoking group have different shortcomings. This preliminary study explores the feasibility of using an in-house assembled multispectral photoacoustic (PA) system to investigate and compare the microcirculation performance between smoking and nonsmoking subjects. For this purpose, pretrained Alexnet, Long Short-Term Memory (LSTM), and a hybrid Alexnet-LSTM network were employed for the prediction task. This research included five smoking and thirty-two nonsmoking participants in the investigations that involved two experimental conditions, i.e., at rest and arterial blood flow occlusion. The findings showed that the PA signals produced in the smoking group have generally smaller magnitudes and negligible differences (when comparing between the two experiment conditions) than their nonsmoking counterpart. The employed models performed superiorly with the highest accuracy of 90 % given by the hybrid model, followed by 80 % recorded for Alexnet and LSTM using nonsmoking data. The performance of these models is reduced when they are trained and tested using smoking data. Our study highlights the task complexity and difficulty in determining tissue microcirculation status in heavy smoking individuals, which has been attributed to their possibly pre-existing atherosclerotic conditions and the high carboxyhemoglobin (COHb) level. A longitudinal study of smoking habit-dependent microcirculation abnormalities in smokers could offer further avenues for investigation. Future research includes incorporating systematic experimental protocols and access to the participant’s medical records to improve the performance of the clinical decision-making system used for field applications.