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Received December 15, 2023, accepted July 17 2024, date of publication July 25 2024.

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

Hui Ling Chua and Audrey Huong

Department of Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor

ABSTRACT

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 net-
work 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 longitudi-
nal 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. 

Keywords – Microcirculation perfusion; photoacoustic; smoking; Alexnet; LSTM

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

J Global Clinical Engineering Vol.6 Issue 3: 2024  38

INTRODUCTION

Smoking increases the risks of various conditions, in-
cluding lung cancer, heart disease, respiratory problems, 
and other significant health issues. It induces vasoconstric-
tion, narrowing the blood vessels in microcirculation and 
reducing blood flow to organs and tissues. Consequently, 
oxygen delivery to the tissues is diminished, impairing their 
proper function. Smoking also increases blood viscosity, 
hindering nutrient and oxygen delivery through narrow 
microvessels; it promotes the formation of blood clots, 
obstructs microcirculatory vessels, and damages tissue. 
Tobacco smoke contains carbon monoxide (CO) as one of 
its toxic components. When tobacco is burned and inhaled, 
CO is absorbed into the bloodstream, significantly affecting 
microcirculation. CO causes adverse effects in humans by 
combining with hemoglobin to form carboxyhemoglobin 
(COHb), preventing blood from carrying oxygen.1-2 Based 
on the reports of Silva,3 there exists a close association 
between tobacco use and microvascular dysfunction, 
which is manifested by impeded blood flow. 

Conventional technologies available to investigate 
microcirculation in the smoking population include 
Magnetic Resonance Imaging (MRI),4 Pulse CO-oximetry,5 
and spectroscopy.6–7 However, the use of MRI is limited 
because of its high operational cost and rigid working 
conditions. Investigation of microcirculatory performances 
based on tissue gas saturation using CO-oximeter and 
spectroscopy is limited by the light penetration depth and 
variable degree of light scattering from tissue heteroge-
neity.8 Photoacoustic (PA) imaging, which combines the 
features of optical spectroscopy and deep penetration of 
acoustic technologies, has gained increasing interest as an 
alternative method in microcirculation flow abnormalities 
detection. The light illuminating a sample absorbed by 
chromophores in the skin produces thermal expansion, 
which generates acoustic waves that a transducer can 
detect. The peaks of PA signals are linearly associated with 
the sample properties, while the temporal characteristic 
of PA signals would disclose the physiological proper-
ties of the tissues. Traditionally, physicians, particularly 
radiologists, would review and examine medical images 
before deciding on treatment planning. These tasks are 
crucial in diagnostic radiography, involving challenging 

analysis and diagnosis based on visual images. Artificial 
Intelligence (AI) has become essential in assisting and 
enhancing these decision-making processes by providing 
accurate, reliable, and efficient interpretation of results. 
This technology has been actively studied for different 
PA applications; some recent AI efforts include Sumit 
et al. 9, who demonstrated using deep learning (U-Net 
model) for multi-target detection with simulated PA 
imaging datasets. Warrier et al.10combined optimization 
and deep learning approaches for detecting and classify-
ing cancer tissues using multispectral PA imaging. The 
study by Mohajerani et al.11 proposed a novel machine 
learning-empowered optoacoustic sensor for recognizing 
diabetes with different complications based on the signals 
recorded from phantom and skin surfaces (in the human 
experiments). The adopted machine learning approach 
used bagged ensemble trees to find the correlation and 
best fit between the data and its labels. Similar works 
were carried out by Liakat et al.12 and Sei et al.13; the 
former developed an in-vivo noninvasive glucose sensor 
to predict glucose concentration using the least square 
regression technique and based on the photoacoustic 
measurement in the skin, whereas the latter study used 
regression technique to determine blood saturation using 
PA signals of blood samples. 

To the authors’ best knowledge, no works have been 
carried out to use deep learning and PA techniques to 
compare the microcirculation changes or flow abnor-
malities between smoking and nonsmoking subjects. This 
work aims to investigate and compare the performance 
of different deep learning models for microcirculatory 
status (i.e., during at-rest and perfusion occlusion in 
smoking and nonsmoking groups) classification using 
the PA method. All the computations were performed 
on a DELL laptop with 64-bit Windows 10, Intel® Xeon™ 
i7-1700M CPU @3.20 GHz. All simulations were done in 
MATLAB (2022b). 

METHODS

Ethical statements: This study was approved by the 
local research ethics committee at Universiti Tun Hussein 
Onn Malaysia (RMC.100-9/139,4).



39 J Global Clinical Engineering Vol.6 Issue 3: 2024

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

2.1 Photoacoustic (PA) detection system 

The schematic diagram of the experimental setup is 
shown in Figure 1 (top). The illumination system consisted 
of two 5 mm ultra-bright transparent white light emit-
ting diodes (model: 5LED-UL-W) filtered by color filters 
(model: FKB-VIS-10, Thorlabs) to generate five primary 
colors with a center light wavelength of 450 nm, 500 nm, 
550 nm, 600 nm, and 650 nm. These wavelengths were 
selected as they encompass the absorption spectra of 
hemoglobin (i.e., oxyhemoglobin and deoxyhemoglobin) 
required for analysis. The light modulation for illumina-
tion of the target area was achieved using an Acousto 
Optic Modulator (AOM) controlled by a radiofrequency 
(RF) driver with a carrier frequency of 15 MHz. The AOM 
produced modulating signals required to illuminate the 
subject. The acoustic energy generated in the medium 
was detected using an ultrasonic flaw detector (EPOCH 
650, Olympus Corp, Japan). A wideband bandpass filter 
with fixed cut-off frequencies (0.5–4 MHz) built into the 
flaw detector was used to filter out high-frequency noise. 
Figure 1 (bottom) shows the actual setup in the laboratory. 
During measurements, a transducer head was placed in 
contact with the skin, and acoustic gel was the coupling 
medium. The signals were recorded using the EPOCH 650 
flaw detector for offline analysis.

FIGURE 1. Schematic diagram of the PA system experiment 
setup (top) and a photograph of a color-tunable LED illumi-
nating a subject’s forearm during the measurement (bottom).

2.2 Subjects and protocol

Thirty-seven healthy individuals (19 males and 18 
females, aged 21-30 years) were invited to participate in 
this research study. Among them, 32 were nonsmoking, 
and five were smoking participants, whose number of 
cigarette smoking years ranged from two to five years. 
The number of cigarettes smoked was between five to 
fifteen cigarettes a week. The local research ethics com-
mittee at Universiti Tun Hussein Onn Malaysia approved 
the study protocol (RMC.100-9/139,4). Before the study, 
these participants reported no known illnesses and were 
provided information about the experimental procedures, 
objectives, and potential risks. Upon enrolment, they pro-
vided their informed consent by signing a printed form. 
The experiment was conducted under two conditions: at 
rest and brachial artery blood flow occlusion to represent 
varying microcirculatory states. 

The study commenced with the at rest experiment, 
where each participant was instructed to position the 
selected site beneath the illuminated light beam, starting 
with the light wavelength of 450 nm. The distance was 
maintained at 1 cm, and the angle of incidence was set at 
45° from the source, as depicted in Figure 1 (bottom). Five 
signals were recorded from the same target site before 
varying the incident light wavelength. During the systolic 
occlusion experiment, a blood pressure cuff (model no. 
CK-110) was applied to the participants’ upper left arm, 
i.e., by exerting a pressure of 140 mmHg for 30 seconds to 
induce ischemia, before the same data collection protocol 
was repeated. Inflating the cuff around the arm tempo-
rarily blocked blood flow, inducing an ischemic state in 
tissues below the cuff. This process promotes changes in 
the functional microcirculation by reducing the supply 
of oxygen-carrying blood to the lower extremities. This 
produces pathological conditions similar in patients with 
peripheral artery and vascular diseases. These procedures 
were applied to both smoking and nonsmoking (as the 
control group) individuals. The recorded screenshot sig-
nals were saved onto a microSD memory card using the 
flaw detector’s built-in function for subsequent offline 
processing and analysis. 



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

J Global Clinical Engineering Vol.6 Issue 3: 2024  40

2.3 Signal pre-processing and dataset handling 

Even though the produced PA signals are in 1D time 
series, the EPOCH 650 device has no function to save the 
raw signals. Therefore, a signal restoration approach was 
employed to convert the screenshot of the image saved 
on a microSD card into a vector representation or matrix 
format suitable for use with a time-based deep neural 
network (i.e., LSTM). This was facilitated by leveraging the 
distinct color contrast in the image, wherein the measured 
signal is depicted in green against a dark background. 
The image was first converted into a binary image using 
the im2bw function before the 1D matrix was obtained 
from the rows and columns of the image. The PA signal, X, 
from each measurement is of size 1 × 494 (i.e., X1 …, X494), 
which was fed into the network input layer for further 
classification and analysis. 

  The original PA signals did not provide a satisfactory 
result in the pre-experiment investigations using the 
LSTM network. Therefore, time-dependent moments were 
used. The moments’ extraction is by using the tfsmoment 
function, and this study considered signal variance (order, 
n=2), skewness (n=3), and kurtosis (n=4) as the input 
features in the prediction of microcirculation status. The 
smoking PA images and the corresponding moments were 
randomly divided into a 40/20/40 % split for training, 
validation, and testing sets, rendering 20/10/20 images 
for convolutional-based models and 60/30/60 signals for 
the LSTM network. The nonsmoking dataset divided using 
the split ratio of 46/28/26 % giving 160/100/90 images 
and 480/300/270 signals used for convolutional-based 
models and LSTM, respectively. 

2.4 Deep learning networks and model training

This study recruited pre-trained Alexnet, LSTM, and 
a hybrid model for microcirculation flow abnormalities 
classification based on the measured PA signal. AlexNet 
and the hybrid model take color (RGB) images as the input, 
while the moments calculated from 1D PA signal in section 
2.3 is used as the input of LSTM. Modification and the use 
of these models are described in sections 2.4.1 and 2.4.2; 
these models were optimized for the problem by search-
ing for the best hyperparameter settings in section 2.4.3. 

2.4.1 Convolutional-based models

The convolutional-based models used in this work 
comprised the pretrained Alexnet and hybrid CNN-LSTM 
model shown in Figures 2 and 3. The input of these models 
was changed to 494 × 329 × 3, consistent with the original 
PA image size recorded from the system. 

 In Alexnet, the network’s last fully connected (FC) was 
modified to two neurons representing: “0” for the normal 
class and “1” for the pathological (or abnormal) class. A 
dropout layer of 0.50 was placed between each FC layer 
in Figure 2 to reduce network overfitting. Meanwhile, the 
Alexnet-LSTM shown in Figure 3 was proposed to extract 
spatial and temporal features from the screenshot PA im-
ages. A batch normalization layer is added to this hybrid 
model to normalize inputs for the subsequent layer. The 
upper layers of this architecture (i.e., Alexnet) are to extract 
spatial information from the input image. The extracted 
abstract information is passed through a flattened layer, 
converting the feature map into one-dimensional data. 
This sequential data is fed into the LSTM network to 
extract the temporal patterns. This time-recurrent net-

work consists of 500 hidden layers, which was decided 

during pre-experiment tests. These layers are connected 
to FC layers and dropout layers of 0.2 to improve model 
generalization. The output of the FCs is fed to a Softmax 
to calculate class probabilities. 
FIGURE 2. Architecture of Alexnet model.



41 J Global Clinical Engineering Vol.6 Issue 3: 2024

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

FIGURE 3. Architecture of the proposed hybrid Alexnet-LSTM 
model.

2.4.2 Time-based deep neural network 

The temporal recursive network, LSTM, was also chosen 
for the task due to its adeptness in handling sequential 
data and addressing short-term memory challenges. 
Its architecture consists of an input layer, taking the 
calculated moment features described in section 2.2 as 
the input signals, followed by a sequence of 155 hidden 
layers to extract their essential temporal features. These 
are followed by six FC layers, whose sizes progressively 
decrease from 40 to 30, 20, 10, 5, 3, and 2. While no 
definitive method for determining FC sizes exists, this 
study adopted a diminishing sequence to simplify the 

model’s structure, as shown in Figure 4. A dropout layer 
with a value of 0.1 is incorporated after each FC layer to 
prevent the risk of overfitting, except for the final layer. 
The output from the final FC layers is fed into a Softmax 
classifier to classify a signal into two categories (i.e., 0: 
normal and 1: pathological condition). 
FIGURE 4. Architecture of the LSTM model.

2.4.3 Hyperparameters selection and model 
training

The classification models in Figures 2–4 were trained 
using the ADAM optimizer, known for its fast computation 
and quick convergence, while other important hyperpa-
rameters in Table 1 were adjusted manually. 
TABLE 1. The Tuning Range of the Considered Hyperparam-
eters and the Chosen Values

Parameter Models Limit Step of 
change

Optimum hy-
perparameter

Lower Upper
Epoch 

number Alexnet 1 100 10 50

LSTM 1 4000 100 500
Hybrid 1 100 10 50

Mini Batch 
size Alexnet 2 128 2n, where n 

= 2,3…7 16

LSTM 2 2048 2n, where n 
= 2,3…11 32

Hybrid 2 128 2n, where n 
= 2,3…7 16

Initial learn-
ing rate

Alexnet LSTM 
Hybrid 5 × 10-4 1 5 × 10-4 5 × 10-4

Gradient 
descent 

threshold
Alexnet LSTM 

Hybrid 1 × 10-3 1 1e-n, n = 3, 2, 
1, 0 1 × 10-3

The optimal hyperparameters setting differed depend-
ing on the datasets and models used. Two-hundred sets of 
combinations consisting of different values in Table 1 were 
attempted in search for the best hyperparameters. The 
prediction accuracy fluctuated between 19% and 100%, 
while the training times varied from 56 to 154 minutes. 
This study identified the best combination based on the 
set that produced the highest training accuracy, such as 
100 % for data of all wavelengths. The same optimum 
hyperparameters setting {epoch no., minibatch size, 
learning rate and gradient threshold} has been found for 
Alexnet and hybrid model as {50, 16, 0.0005, and 0.001} 
and {70, 16, 0.0001, and 0.001}, respectively, for smoking 
and nonsmoking data, and {500, 32, 0.0005, and 0.001} 
and {3000, 256, 0.0001, and 0.001}, respectively, for LSTM. 

2.5 Score fusion strategy

This study used a combined prediction score to enhance 
the system’s classification confidence. The class probabil-
ity from models trained with each wavelength (i.e., 450 
to 650 nm) was combined through summing to give the 
final score. This strategy is coined as the fusion method. 
An example of the fusion technique is shown in Figure 5.
FIGURE 5. Fusion technique for final classification of micro-
circulatory status.



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

J Global Clinical Engineering Vol.6 Issue 3: 2024  42

2.6 Performance metric

The effectiveness of the trained models used in this 
study is evaluated using classification accuracy shown 
in Equation (1). This performance metric measures the 
degree of closeness of predictions to actual values.

                                (1)

T denotes the total number of data and N denotes the 
total number of class labels (N = 2). A true positive (TPi ) 
is when the abnormality for signal i is correctly detected. 
A false positive (FP) is the percentage of normal data 
misclassified as abnormal, a false negative (FN) is an 
abnormal signal class member incorrectly classified as 
normal, and a true negative (TN) is the correct prediction 
of normal PA signal.

RESULTS

Smoking individuals are known for having a high risk 
for vascular diseases; thus, the blood occlusion procedure 
is applied to these individuals to allow investigation of 
system sensitivity in this group of populations. Figure 6 
compares the peak of PA signals at different wavelengths 
for smokers and nonsmokers under at-rest and occlusion 
conditions. It can be observed that the PA signals from 
nonsmoking subjects have overall higher amplitude values 
under both at-rest and occlusion conditions as compared 
to smoking subjects.

The PA produced from both (smoking and nonsmoking) 
groups exhibit the same pattern. The signals produced 

under at-rest and occlusion conditions peak at 450 nm 

and 550 nm, respectively, and the differences (between 
the different experimental conditions) are considerably 
negligible for wavelengths 500 nm, 600 nm, and 650 nm, 
as shown in the figure. 

The classification results following the fine-tuning of 
the employed models using the nonsmoking and smok-
ing data and from score fusion technique are shown in 
Figures 7 and 8, respectively. The training and testing of 
the Alexnet and hybrid model used screenshot images, 
while signal moments described in section 2.3 are used 
as the input to the LSTM.

DISCUSSION

PA technologies, such as skin glucose and oxygen satu-
ration detections, have been extensively tested in various 

diagnostic imaging applications. However, the use of this 
technology in the detection of compromised microcircula-
tion, especially in the smoking population, has not been 
investigated. This research compares changes in blood 
perfusion under induced pressure in individuals with 

FIGURE 6. Mean and standard deviation (represented by error 
bar) of PA echo amplitudes produced in smokers and nonsmok-
ers under different illumination wavelengths.

FIGURE 7. Confusion matrix of (a) Alexnet, (b) LSTM, and (c) 
hybrid model in classifying microcirculation status in nonsmok-
ing subjects based on PA images and signals (class 0: normal, 
1: abnormal).

FIGURE 8. Confusion matrix of (a) Alexnet, (b) LSTM, and (c) 
hybrid model for classification of microcirculation status (class 
0: normal, 1: abnormal) in smokers based on PA  images and 
signals.



43 J Global Clinical Engineering Vol.6 Issue 3: 2024

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

different smoking habits based on PA signals produced 
under visible wavelengths illumination. 

The fundamental principle of the adopted PA technol-
ogy is that the magnitude of the PA signal produced by 
tissue depends on the hemoglobin variants’ absorption 
properties, wherein the oxyhemoglobin light absorption 
peaks at 450 nm14 while deoxyhemoglobin absorption 
peaks at 550–560 nm.15 The absorption properties of 
these hemoglobin variants are similar for the remainder 
employed wavelengths (i.e., 500, 600, 650 nm). This trend 
was observed in Figure 6. This diagram revealed the high-
est PA signal magnitude recorded at 450 nm illumination, 
while 550 nm produced, generally, the highest ultrasonic 
echo amplitude under blood flow occlusion condition, 
where the regional tissue deoxygenated blood is rich due 
to impeded oxygen-carrying blood from flowing into the 
lower arm (measurement site). The PA signals obtained 
from smokers have considerably weaker echoes than 
nonsmokers, with a mean relative percent difference of 
9.5% across all wavelengths and experiment conditions. 
This relative percent difference between nonsmok-
ing and smoking results is divided by the two results. 
There is also high consistency in PA signals obtained 
from smoking subjects for both experiments. The COHb 
level is generally high in smokers, and the absorption 
spectrum of this hemoglobin variant, associated with 
the risk of inadequate oxygen delivery,16 is considerably 
less prominent as it overlaps with that of oxyhemoglobin 
and deoxyhemoglobin in the visible wavelength range. 
While the dominance of light absorption of COHb could 
be the primary cause of the observations on the lower 
magnitude in the produced signals, we do not rule out 
the possibility of the already impaired microcirculation 
function or pre-existing atherosclerotic conditions in this 
group of participants, causing negligible differences in 
the readings between the at rest and external exerting 
pressure experiment. 

Figure 7 shows the models’ classification performance 
tested on nonsmoking (healthy) participants. The networks 
trained and tested on healthy subjects’ data revealed 
considerably good accuracies ranging between 85.6 and 
90%, suggesting the consistent performance between the 
convolutional-based models and temporal-based LSTM, 

and their feasibility in classifying normal and abnormal 
(occluded) microcirculation performance in nonsmoking 
individuals. Meanwhile, the results in Figure 8 reveal that 
the performance of these models decreased in the com-
promised microcirculation status (i.e., occlusion condi-
tion) detection. Even though the hybrid model achieved 
consistent classification accuracy (i.e., 90%), followed by 
Alexnet and LSTM with a classification accuracy of 80%, 
this group’s FN rate is high (~20-40%). An investigation 
was carried out on the misclassified data, and it was found 
that they belong to the same subject, who reportedly 
smokes about fifteen cigarettes a week. All signals from 
this subject, the heaviest smoker among the five recruits, 
were misclassified as normal. One possible reason is that 
the atherosclerotic conditions, one of the known complica-
tions in smokers, could have been detected during at-rest 
condition, so further exerting external pressure on the 
limb during the blood occlusion experiment produced 
near negligible changes, as observed in the smoking 
group in Figure 6.

 For the above-stated reasons, this work does not rule 
out the possibility of the models is overfitting to the normal 
(i.e., at rest) class in the smoking group, largely due to the 
negligible differences in the PA signals between at rest 
and occlusion conditions, compromising the classifica-
tion performance of the models. Although an insufficient 
dataset (i.e., in the smoking group) could lead to biased 
predictions, the good performance of the models using 
nonsmoking data in Figure 7 and the high misclassifica-
tion rates of pathological condition (i.e., between 20-40 
%) that agreed well with the observations of,17 and18 
indicating a certain degree of reliability of our findings. 

It must also be mentioned that this study has no access 
to the participants’ information, such as previous health 
records, and has not included their diet and environmental 
factors in the experimental design; these factors may influ-
ence the results and analyses of the study. Therefore, the 
future of this study includes recruiting more participants 
with various backgrounds and smoking habits and adopt-
ing a systematic experimental procedure (e.g., convenient 
access to patient’s medical records) to investigate the 
blood microcirculatory performance between healthy 
and unhealthy (or patient) groups to enhance the validity 



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

J Global Clinical Engineering Vol.6 Issue 3: 2024  44

of the research findings. The improved clinical decision-
making system can be integrated into the proposed PA 
system and considered an alternative imaging tool to 
facilitate the investigation of tumor angiogenesis and 
microvascular dysfunction, allowing early identification 
of compromised microcirculation and preventing further 
complications.

CONCLUSION

This paper demonstrated the use of deep learning-
incorporated PA technology to investigate blood perfu-
sion in nonsmoking and smoking subjects. The results 
showed that the proposed hybrid Alexnet-LSTM model 
performed better than the conventional Alexnet and LSTM 
models in the classification of microcirculation changes 
in both smoking and nonsmoking groups. These models 
performed inferiorly with high misclassification rates 
of 20–40% in the detection of compromised perfusion 
in the smoking group. This observation is attributed to 
the compromised perfusion in this group of subjects. 
This explains the negligible change observed after exert-
ing external pressure impeding the (oxygen-carrying) 
blood flow and the limited smoking dataset for training 
the models. Future works include recruiting volunteers 
of diverse backgrounds, profiles, and smoking status to 
enhance the validity and practical application of existing 
findings in the healthcare system.

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article 
was reported.

FUNDING

Ministry of Higher Education (MOHE) Malaysia through 
Fundamental Research Grant Scheme (FRGS/1/2020/
TK0/UTHM/02/28)

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