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ORIGINAL
Usefulness of new acoustic respiratory sound monitoring withartificial intelligence for upper airway assessment in obese
patients during monitored anesthesia care
Yoshitaka Shimizu D.D.S., PhD1, Noboru Saeki M. D., PhD2, Shinichiro Ohshimo M. D., PhD3, Mitsuru Doi D.D.S.1,
Kana Oue, D.D.S., PhD4, Mitsuhiro Yoshida D.D.S., PhD4, Tamayo Takahashi D.D.S., PhD4, Aya Oda D.D.S., PhD4,
Takuma Sadamori M. D., PhD3, Yasuo M. Tsutsumi M. D., PhD2, and Nobuaki Shime M. D., PhD.3
1
Department of Dental Anesthesiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan, 2Department of Anesthesiology and Critical Care, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan,
3
Department of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima,
Japan, 4Department of Dental Anesthesiology, Division of Oral & Maxillofacial Surgery and Oral Medicine, Hiroshima University Hospital,
Hiroshima, Japan

Abstract : Monitored anesthesia care (MAC) often causes airway complications, particularly posing an elevatedrisk of aspiration and airway obstruction in obese patients. This study aimed to quantify the levels of aspiration
and airway obstruction using an artificial intelligence (AI)-based acoustic analysis algorithm, assessing its utility
in identifying airway complications in obese patients. To verify the correlation between the stridor quantitativevalue (STQV) calculated by acoustic analysis and body weight, and to further evaluate fluid retention and airway
obstruction, STQV calculated exhaled breath sounds collected at the neck region, was compared before and after injection of 3 ml of water in the oral cavity and at the start and end of the MAC procedures. STQV measured
immediately following the initiation of MAC exhibited a weak correlation with body mass index. Furhtermore,
STQV values before and after water injection increased predominantly after injection, further increased at theend of MAC. AI-based analysis of cervical respiratory sounds can enhance the safety of airway management
during MAC by quantifying airway obstruction and fluid retention in obese patients. J. Med. Invest. 70 : 430-435,
August, 2023
Keywords : Airway Obstruction, Artificial Intelligence, Obesity, Monitored anesthesia care

INTRODUCTION
In Monitored Anesthesia Care (MAC) for procedures such as
gastrointestinal endoscopy and dental treatments, ventilation
issues often occur due to upper airway obstruction caused by
the overlap between the airway and the operative field, andobesity or overweight with a narrow airway space is a major
relevant risk factor. Upper airway obstruction occurs intermittently during MAC because of the effects of treatment procedures ; however, oxygen administration through a nasal cannulaor high-flow causes a slow decline in oxygen saturation, delaying
arterial oxygen saturation (SpO2) monitoring to detect upper airway obstruction (1). Moreover, when a capnogram sampling tube
for respiratory monitoring is inserted into the nose, respiratory
detection becomes impossible during mouth breathing. However,many studies on sleep or airway management have reported thatthe sinking of the tongue’s base occurs due to a decrease in consciousness levels in many obese patients, making them unable tobreathe nasally (2, 3). It is important to recognize partial upper
airway obstruction and implement measures such as adjusting
the chin position and reducing the dosage of sedatives to preventa transition to mouth breathing.
The acoustic respiratory rate (RRa) monitoring device
Abbreviations :MAC, Monitored Anesthesia Care ; AI, Artificial Intelligence ; RRa,
acoustic respiration rate ; STQV, stridor quantitative value ; jRCTs,
Japan Registry of clinical trials ; SpO2 , percutaneous oxygen saturation ; OAA/S, Observer’s Assessment of Alertness/Sedation ; CI,
confidence interval ; BMI, Body mass index.

(Masimo Corp., Irvine, CA, USA), which is currently available
for sale, can measure the respiratory rate using the magnitude
of respiratory sounds (4, 5). Upper airway obstruction can be
identified by listening to snoring or wheezing sounds ; however,this requires subjective evaluation and expertise. In contrast,
artificial intelligence (AI) acoustic analysis is a technique thatquantifies abnormal respiratory sounds and that anyone can use
instantly to diagnose airway obstruction (6, 7).
This study aimed to capture the early signs of airway complications using machine learning technology. AI analysis has been
gradually introduced in various medical fields in recent years,
including radiological diagnosis, endoscopy, and histopathological diagnosis (8). Machine learning technology is also gradually
being applied in breath sound analysis (9-11). Our researchgroup has developed an AI-based algorithm to quantify fine andcoarse crackles, wheezes, and similar snoring sounds using an
AI machine learning-based breath sound analyzer (6, 7).
This developed AI analysis algorithm can instantly identify
changes in the frequency of sounds due to airflow restriction(partial obstruction) during upper airway obstruction. Furthermore, for verification, this machine-learning algorithm includes
changes in the sound elements of inspiratory flow generated by
the retention of fluid, including saliva, in the upper airway. We
Received for publication May 23, 2023 ; accepted July 5, 2023.
Address correspondence and reprint requests to Yoshitaka Shimizu,
Department of Dental Anesthesiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi,
Minami-ku, Hiroshima, 734-8553, Japan and Fax : +81-82-257-5779.
E-mail : yshimizu@hiroshima-u.ac.jp

The Journal of Medical Investigation Vol. 70 2023

The Journal of Medical Investigation Vol. 70 August 2023
aimed to investigate the feasibility of detecting respiratory adverse events using an AI-based acoustic monitoring algorithm.

PATIENTS AND METHODSThis prospective cohort study was designed and conducted inaccordance with the Declaration of Helsinki and its guidelines.
This study was approved by the Hiroshima Certified Review
Board (approval number : CRB2022-0002), and written informed consent was obtained from all subjects participating in
the trial. The trial was registered prior to patient enrollment at
the Japan Registry of clinical trials (jRCTs062220054).

Study design
Participants who underwent dental treatment under MAC
at the Hiroshima University Hospital between September andNovember 2022 were included. The inclusion criteria were anage ≥18 years, capability to undergo dental procedures on an outpatient basis, phobia of dental treatment, abnormal gag reflex,
and ability to undergo invasive dental procedures. The exclusion
criteria were allergy and history of serious adverse reactions to
midazolam or propofol, dysphagia, poor oral hygiene, and mental
disability.
In accordance with the Japanese guidelines for the management of obesity, patients with a body mass index (BMI) ≤ 25
kg / m2 and > 25 kg / m2 were assigned to the non-obese and obese
groups, respectively (12).

Calculation of STQVThe AI analysis algorithm for calculating the stridor quantitative value (STQV) included 56 labeled sounds comprisingcharacteristic frequency bands / continuations as teacher data formachine learning. Specifically, feature parameters were extracted from the snoring caused by upper airway obstruction due tothe base of the tongue falling back and from the stridor sounds
generated by the resonance of water-soluble reservoirs between
inspiration and expiration using frequency analysis, cepstrum
analysis, liftering process, and other methods. Subsequently, coefficients of the feature parameters were derived using AdaBoost
as a machine learning algorithm and by inputting 56 pieces oflabeled teacher data (Figure. 1).

Figure 1. Machine learning algorithms for the values of the stridor
component. Based on a training dataset of stridor sounds recorded
from patients and labeled by experts, features were extracted using
frequency, local variance, and cepstrum analyses, and machinelearning algorithms were applied.

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Study protocol
A bedside monitor, CARESCAPETM B850 (GE Healthcare,
Chicago, IL, USA), was used to measure blood pressure andSpO2 and obtain an electrocardiogram and capnogram. Subsequently, the venous route was secured, and propofol (10 or 20
mg bolus, followed by 0.8–4 mg / kg / h as continuous dosage) andmidazolam (1–2.5 mg bolus) were administered. RRa sensor in
Radical 7 (Masimo Corp., Irvine, CA, USA) was then attachedwith adhesive tape to the skin of the left paralarynx and sensor
of the continuous monitoring system for respiratory sounds tothe skin of the right paralarynx (Figure 2). Sedation levels weremanaged in the range of 3–4 on the Observer’s Assessment of
Alertness / Sedation (OAA / S) scale. All patients received oxygenat 3 L / min through a nasal cannula. Cervical respiratory sounds
were recorded after sedation was induced, and 3 ml of water was
thereafter injected intraorally with a syringe. Cervical respiratory sounds were recorded, and dental treatment was subsequently initiated. During dental treatment, cervical respiratory
sounds were recorded at the onset of coughing (choking), at thedetection of apnea for 20 s each using a capnogram and RRa,
and at the end of MAC. Dental treatment was interrupted for
cervical inspiratory sound recording (STQV analysis in a quiet
environment) only the first time each event occurred (Figure 3).

Statistical analysis and sample size calculation
The sample size estimation in this study was based on a previous interstitial lung disease detection power analysis using
fine crackle sound AI analysis published by this study’s researchgroup (7), which showed that the fine crackle sound AI index was
0.032 ± 0.023 in the control group and 0.121 ± 0.090 in the interstitial lung disease group. From these values, the effect size was

Figure 2.

Photograph of the nasal cannula for oxygen
administration. Nasal capnogram sampling tube, a sensor forcontinuous monitoring system for respiratory sounds device, and asensor of acoustic respiration rate when fitted are shown.

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Y. Shimizu, et al. New acoustic respiratory sound monitoring with artificial intelligence

Figure 3. Schematic diagram of the study protocol. Cervical intake sounds were recorded in the boxes painted inorange, and a post-analysis of the values of the stridor component was performed. RRa, acoustic respiratory rate.

calculated to be 0.94 using the software G*Power version 3.1.9.6.
The final target sample size was estimated to be nine subjects
using an alpha level of 0.05 and power of 0.8.
Student’s t-test was used to compare continuous variables
between two groups, whereas the one-way analysis of variance
was used to compare multiple groups, and Pearson’s correlation
coefficient was used for bivariate analysis. All analyses were performed using SPSS version 25 for Mac (IBM Corp. ; Armonk, NY,
USA). A P-value < 0.05 was considered statistically significant.

RESULTS
The study flowchart is shown in Figure 4. Sixty-seven patients
who underwent dental treatment under MAC at the Hiroshima
University Hospital between September 22 and November 14,
2022 were included. Of these patients, five who did not give consent and two whose anesthesiologist made an error in the study
procedure were excluded.
Participants’ baseline characteristics, including age, height,
weight, BMI, and male-to-female ratio, are presented in Table
1. Regarding the implementation status of MAC, the averageamounts of propofol and midazolam administered and averageanesthesia time are also presented in Table 1. The STQV measured immediately after the start of MAC showed a weak correlation with BMI (R = 0.35, P = 0.006), and when the BMI wasclassified into four categories and compared using an analysis
of variance, the STQV values significantly increased with BMI(P = 0.046) (Figure 5).
To confirm the feasibility of detecting respiratory adverse
events using STQV, the STQV before and after injecting 3 ml ofwater into the mouth of obese patients with a BMI of ≥25 kg / m2
were compared. STQV values before and after injection were
0.0679 ± 0.056 and 0.1415 ± 0.111, respectively (P < 0.007 ; 95%
confidence interval [CI], -0.125 to -0.0225), indicating a significant increase in STQV after water injection into the mouth(Figure 6).
Furthermore, STQV values at the start and end of IVS werecompared to investigate the feasibility of using STQV to detect
upper airway changes caused by anesthetic accumulation. The
STQV value at the start and end of MAC were 0.0679 ± 0.056
and 0.173 ± 0.129, respectively (P < 0.007, 95% [CI] -0.172 to-0.0392). It was confirmed that STQV could help detect upper
airway narrowing caused by sedation levels (Figure 7).

Table 1. Participants’ background and anesthesia dosage
47.9 ± 15 [20− 84]

Age (years)
Sex (female / male)

35/25

Height (cm)

161 ± 9.5 [140− 183]

Weight (kg)60.6 ± 16.3 [38− 118]

BMI (kg / m2)

23.3 ± 5.4 [14.3− 40.8]
Drug (dosage)

Propofol (mg)100.2 ± 43.6 [37.4− 239.7]

Midazolam (mg)

2.2 ± 0.6 [1− 5]

Anesthesia time (min)50.4 ± 16.5 [21− 97]

Values are presented as mean ± standard deviation [min-max] or
number (proportion). BMI, body mass index.

Figure 4. Schematic diagram of the study. BMI, body mass index.

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433

Figure 5. Left figure ; scatter plots and regression lines of BMI and STQV immediately following the initiation of MAC. BMI and STQV

were weakly correlated (R = 0.35, P = 0.006). Right figure : BMI was classified into four categories (BMI ≤ 20 [n = 18], 20 < BMI ≤ 25 [n = 23],
25 ≤ BMI < 30 [n = 10], and BMI ≥ 30 kg/m2 [n = 9]) and compared using an analysis of variance. STQV value significantly increased with BMI(P = 0.046). The horizontal line indicates the mean deviation. Statistical analyses were performed using a one-way Analysis of Variance. BMI,
body mass index ; STQV, quantitative values of the stridor component.

Figure 6. Comparison of STQV before and after water injection into
the mouth of obese patients. The STQV values pre- and post-injection
were 0.0679 ± 0.056 and 0.1415 ± 0.111, respectively (P < 0.007, 95%
confidence interval [CI] -0.125 to -0.0225), indicating a significant
increase in STQV following water injection into the mouth. The
horizontal line indicates the mean deviation. Statistical analysis were
performed using a student’s t-test. BMI, body mass index ; STQV,
quantitative values of the stridor component.

Figure 7. Comparison of STQV at the start and end of MAC inobese patients. The STQV value at the start and end of MAC were
0.0679 ± 0.056 and 0.173 ± 0.129, respectively (P < 0.007, 95% [CI]-0.172 to -0.0392), confirming that STQV can help detect upper airway
narrowing caused by sedation levels. The horizontal lines represent
mean deviations. Statistical analysis was performed using a student’s
t-test. BMI, body mass index ; MAC, monitored anesthesia care;
STQV, quantitative values of the stridor component.

DISCUSSION

including lifting the chin, extending the neck, and elevating the
body (14-16).
Furthermore, propofol and midazolam suppress swallowing
function and endoscopic instruments, including mouth openers,
make swallowing difficult, leading to the retention of fluid, suchas saliva, in the upper airway, which increases the risk of aspiration (17).
Therefore, a comparison of STQV before and after oral water
injection was conducted, confirming an increase in STQV following water injection, thereby demonstrating the feasibility ofusing STQV to quantify upper airway fluid retention.
Gemma et al. reported that sedation using propofol, in particular, suppresses swallowing function, causing fluid retention
in the pharynx and increasing the risk of aspiration (18). The
anesthesiologists must manage fluid retention in the pharyngeal

This study developed the STQV index for analyzing respiratory sounds in the neck using AI technology to quantify adverse
sounds in the respiratory system. The effectiveness of this system in evaluating airway function in obese patients under IVS
was examined. Results showed that STQV immediately after
IVS correlated with BMI and that airway narrowing due to obesity could be quantified using STQV.
Sedative drugs, including midazolam, can cause upper airway collapse and increase airway resistance, and a significant
increase in nasal resistance leads to mouth breathing, making
it impossible for capnogram to detect exhalation (13). STQV
quantifies airway patency, and adding it to airway assessment
during IVS suggests its usefulness in assessing the necessity,

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Y. Shimizu, et al. New acoustic respiratory sound monitoring with artificial intelligenceregion by suctioning, adjusting the head and body position, andadjusting the sedation levels for procedures involving intraoral
water injection and bleeding ; however, it is difficult to detect
fluid retention using the existing monitoring systems, and STQV
may be an important indicator to monitor.
MAC is performed in the supine position with the mouth open,
which increases upper airway collapse during treatment andexamination (16). These limitations are considered risk factors
for apnea owing to airway obstruction and choking during
non-intubated MAC (19). Particularly, the risk is high in obese
patients ; therefore, monitoring devices that can diagnose thelevel of airway patency in real-time accurately are important.
Investigations pertaining to airway patency in obese patients
encompass numerous reports, including assessments of airway
conditions during sleep and airway management under sedation (2, 11). These studies have measured and validated airway
velocity and critical pharyngeal pressure ; however, these measurements require the insertion of sensors, making monitoring
invasive (20, 21). In contrast, our respiratory sound monitoring
system employs a hermetically sealed, noise-cancelling sticktype sensor, allowing for non-invasive and convenient visualization of the STQV (22). This suggests the potential utility of STQV
in airway assessment for MAC.
STQV levels increased after an intraoral injection and remained similarly elevated at the end of the MAC period compared with the start. The present study’s results showed thesame trend in obese and non-obese patients. Considering theseresults, the application of STQV monitoring during MAC may
not be limited to obese patients. In addition, we aim to further
investigate the clinical usefulness of STQV monitoring by validating it in morbidly obese patients (BMI > 35 kg / m2), a highrisk group for anesthetic-induced airway obstruction.
This study had several limitations. First, the treatment criteria (type of sedative, dosage, and level of sedation) for sedation
management in this study were not clearly defined in advanceand were left to each physician’s discretion. Second, the neckinspiratory volume values calculated in this study were absoluteand not calibrated for each patient. Therefore, the basic volume picked by the sensor may differ among patients. However,
objective monitoring of cervical inspiratory volume using the
proposed system may enhance the safety of patients under MAC.
Third, the correlation between STQV and BMI was weak, possibly due to the high false negative rate. In the future, there is aneed to improve specificity by optimizing the validation data set
and improving sensor performance. Finally, this study covered
procedures were limited to dental care, which may not represent
a typical patient receiving MAC. To eliminate this possibility,
the evaluation of STQV was performed evaluated during dental
treatment interruption in a noise-free quiet environment.

CONCLUSION

clinical Research Center at the Hiroshima University Hospitalfor their assistance.

STATEMENT OF ETHICSThis study was approved by the Hiroshima Certified Review
Board (approval number : CRB2022-0002). Written informed
consent was obtained from all subjects participating in the trial.
The trial was registered prior to patient enrollment at the Japan
Registry of clinical trials (jRCTs062220054).

CONFLICT OF INTEREST STATEMENT
The authors declare that they have no competing interests.

FUNDING SOURCES
This work was supported by a Japan Agency for Medical Research and Development (AMED) Grant (21he1602002h0004)
and JSPS KAKENHI Grant (JP 21K10072).

AUTHOR CONTRIBUTIONSYS made a study design, collected the data, and drafted
the paper. NS made a study design and discussed the results.
MD, KO, MY, TT and AO collected the data and discussed theresults. SO and TS advised the study design and discussed theresults. YM T and NS supervised the study, analyzed the data,
and revised the paper. The authors read and approved the final
manuscript.

DATA AVAILABILITY STATEMENT
The data supporting this study’s findings are available fromthe corresponding author upon reasonable request.

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