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

Main Article Content

Hui Ling Chua
Audrey Huong

Keywords

Microcirculation perfusion, photoacoustic, Alexnet, LSTM, smoking

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.

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References

1. Prockop, LD and Chichkova, RI. Carbon monoxide intoxication: an updated review. J. Neurol. Sci. 2007; 262:122–130. doi: 10.1016/j.jns.2007.06.037.
2. Fagerstrom, KO. Effects of a nicotine-enriched cigarette on nicotine titration, daily cigarette consumption, and levels of carbon monoxide, cotinine, and nicotine. Psychopharmacology (Berl) 1982; 77:164–167. doi: 10.1007/BF00431941.
3. Silva H. Tobacco Use and Periodontal Disease—The Role of Microvascular Dysfunction. Microcirculationin Health and Disease 2021; 10(5): 441. doi: 10.3390/biology10050441
4. Chen NC, Chang WN, Lui CC, et al. Detection of gray matter damage using brain MRI and SPECT in carbon monoxide intoxication: a comparison study with neuropsychological correlation. Clinical Nuclear Medicine 2013; 38: e53-e59. doi: 10.1097/RLU.0b013e31827082a7.
5. Ozcan N, Ozcam G, Kosar P, et al. Correlation of computed tomography, magnetic resonance imaging and clinical outcome in acute carbon monoxide poisoning. Rev. Bras. Anestesiol 2016; 66: 529–532. doi: 10.1016/j.bjane.2014.05.006.
6. Parks J and Worth HG. Carboxyhemoglobin determination by second-derivative spectroscopy. Clin. Chem. 1985; 31: 279–281.
7. Huong AKC, Mahmud WMH, Tay KG, et al. Smoking status classification by optical spectroscopy and partial least square regression, Journal of Physics: Conference Series. 2019;1372:012031. doi: 10.1088/1742-6596/1372/1/012031.
8. Culnan DM, B. Craft-Cffman B, Bitz GH, et al. Carbon monoxide and cyanide poisoning in the burned pregnant patient: an indication for hyperbaric oxygen therapy. Ann. Plast. Surg. 2018; 80(S106). doi: 10.1097/SAP.0000000000001351.
9. Sumit A, Suresh T, Garikipat A, et al. Modeling combined ultrasound and photoacoustic imaging: Simulations aiding device development and artificial intelligence. Photoacoustics 2021; 24. doi: 10.1016/j.pacs.2021.100304.
10. Warrier GS, Amirthalakshmi TM, Nimala K, et al. Automated Recognition of Cancer Tissues through Deep Learning Framework from the Photoacoustic Specimen. Contrast Media and Molecular Imaging 2022. doi: 10.1155/2022/4356744
11. Mohajerani P, Aguirre J, Omar M, et al. Machine-Learning Powered Optoacoustic Sensor for Diabetes Progression. Medrxiv. 2021. doi: 10.1101/2021.03.17.21253779
12. Liakat S, Bors KA, Xu L, et al. Noninvasive in vivo glucose sensing on human subjects using mid-infrared light. Biomed. Opt. Express. 2014; 5:2397–2404. doi: 10.1364/BOE.5.002397.
13. Sei K, Fujita M, Hirasawa T, et al. Measurement of blood-oxygen saturation using a photoacoustic technique in the rabbit hypoxemia model. Journal Clinical Monitoring Computer. 2018; 33: 269–279. doi: 0.1007/s10877-018-0166-8.
14. Nguyen DD, Pang JY, Madill C, et al. Effects of 445‐nm Laser on Vessels of Chick Chorioallantoic Membrane with Implications to Microlaryngeal Laser Surgery. The Laryngoscope 2021; 131. doi: 10.1002/lary.29354.
15. Friedmann D and Verma KK. Enhanced Perception of Deoxygenated Hemoglobin for the Visualization of Lower-Extremity Reticular Veins. Dermatologic Surgery 2024; 50(2): 207-209. doi: 10.1097/DSS.0000000000003974.
16. Azizah RN, Puspitasari A and Lestari I. Relationship of Carboxyhemoglobin (CoHb) And Hemoglobin (Hb) Levels In Active Smokers In Gresik Regency. International Journal of Advanced Health Science and Technology 2024; 4(1): 8–11. doi: 10.35882/ijahst.v4i1.291.
17. Low BH, Lin YD, Huang BW, et al. Impaired Microvascular Response to Muscle Stretching in Chronic Smokers With Type 2 Diabetes. Frontier Bioengineering Biotechnology: Section Biomechanics 2020; 8. doi: 10.3389/fbioe.2020.00602.
18. Hashimoto H. Impaired Microvascular Vasodilator Reserve in Chronic Cigarette Smokers: A Study of Post-occlusive Reactive Hyperemia in the Human Finger. Japanese Circulation Journal 1994; 58: 29-33.