Research ArticleOriginal Research

Home Monitoring of Asthma Exacerbations in Children and Adults With Use of an AI-Aided Stethoscope

Andrzej Emeryk, Eric Derom, Kamil Janeczek, Barbara Kuźnar-Kamińska, Anna Zelent, Mateusz Łukaszyk, Tomasz Grzywalski, Anna Pastusiak, Adam Biniakowski, Krzysztof Szarzyński, Dick Botteldooren, Jędrzej Kociński and Honorata Hafke-Dys
The Annals of Family Medicine November 2023, 21 (6) 517-525; DOI: https://doi.org/10.1370/afm.3039

Article Figures & Data

Figures

  • Figure 1.
    Figure 1.

    Auscultation points on the chest according to age group: (A) <12 years of age, (B) >12 years of age.

  • Figure 2.
    Figure 2.

    Survey completed with a mobile application before each examination.

    PEF = peak expiratory flow.

  • Figure 3.
    Figure 3.

    Receiver operating characteristic curves for classifiers based on different sets of home-collected data (physiologic parameters and survey data).

    AI = artificial intelligence.

    Note: Performance was measured on an independent validation set that included only patients not used during classifier training.

  • Figure 4.
    Figure 4.

    Results of the effectiveness (in terms of AUC) of different sets of home-collected data in decision support systems classifying asthma exacerbations.

    AI = artificial intelligence; AUC = area under the ROC curve; ROC = receiver operating characteristic.

    Note: The central line in each box denotes mean AUC, box edges extend to 1 SD from the mean, and fences (whiskers) denote 95% CI.

Tables

  • Table 1.

    Tools Used and Related Data Collected by Participants at Home During Each Examination

    ToolData
    Electronic stethoscope (StethoMe)Auscultation audio recordings from a set of 6-8 chest points (examination) (Figure 1)
    AI-based algorithm (StethoMe AI) automatic analysis of recorded soundsWheezes intensity
    Rhonchi intensity
    Coarse crackles intensity
    Fine crackles intensity
    RR
    HR
    I/E
    Pulse oximeter (Accurate FS20P, Accurate FS20C)Peripheral capillary oxygen saturation (SpO2)
    Peak flow meter (Mini Wright Peak Flow and Low Range)Peak expiratory flow
    Survey (Figure 2)Breathing quality expressed on a 5-point scale Symptoms

    • AI = artificial intelligence; HR = heart rate; I/E = inspiration-to-expiration duration ratio; RR = respiratory rate.

    • Note: These data were used as input features for the development of autonomous detection of asthma exacerbation (decision support classifiers).

  • Table 2.

    Patients and Examination Information

    DataChildren Aged 0-5 yChildren Aged 6-17 yAdults
    No. of patients    52    38    59
    Male, %    63.5    76.3    27.1
    Age, y, median (1st, 3rd quartiles)      3.0 (3.0, 4.25)      8.5 (7.0, 10.0)    38.0 (32.5, 43.0)
    Weight, kg, median (1st, 3rd quartiles)    17.0 (14.0, 20.0)    32.5 (24.5, 44.2)    65.0 (60.0, 82.0)
    Height, cm, median (1st, 3rd quartiles)  104.5 (98.0, 112.5)  135.5 (126.2, 149.8)  169.0 (162.0, 174.0)
    No. of examinations (no. of individual recordings)2,915 (17,532)1,728 (10,883)1,386 (11,076)
    No. of examinations with moderate or severe exacerbation (see Reference Standard in Methods below for details)  143    82    57
    No. of patients with ≥1 examination with moderate or severe exacerbation    25    17    12
    Auscultation period, d, median (1st, 3rd quartiles)  182.3 (160.0, 228.0)  188.2 (179.5, 223.3)  190.0 (133.4, 218.3)

    • Note: Each examination is a set of data as described in Table 1 and is a set of several recordings taken from multiple auscultation points on the chest.

  • Table 3.

    Effectiveness of Different Sets of Home-Collected Data in Decision Support Systems Classifying Asthma Exacerbations Measured Using AUC

    Parameter or Set of Parameters Used for Classifier DevelopmentAUC, % (95% CI)
    Children Aged 0-5 yChildren Aged 6-17 yAdults
    Wheezes83.8 (82.3-85.4)79.5 (77.2-81.8)71.3 (67.3-75.3)
    Rhonchi77.0 (75.0-79.0)81.3 (79.0-83.5)75.4 (72.6-78.3)
    Fine crackles71.9 (69.7-74.2)77.3 (74.9-79.7)68.3 (64.2-72.5)
    Coarse crackles69.7 (67.3-72.0)79.1 (75.1-83.2)61.9 (55.2-68.6)
    Heart rate61.1 (58.3-63.9)62.8 (60.0-65.6)65.1 (56.9-73.2)
    Respiratory rate61.7 (57.5-65.9)67.6 (64.1-71.0)61.3 (55.7-66.8)
    Inspiration-to-expiration ratio59.9 (57.6-62.2)64.6 (62.0-67.2)62.1 (57.4-66.8)
    All parameters provided by AI-aided stethoscope93.0 (92.1-93.9)92.4 (91.1-93.7)81.0 (75.1-86.8)
    Symptoms (survey)72.0 (70.1-73.9)78.5 (76.8-80.3)92.0 (89.4-94.6)
    Peripheral capillary oxygen saturation66.6 (62.6-70.7)68.1 (65.0-71.1)71.5 (66.5-76.5)
    Peak expiratory flown/aa62.5 (57.2-67.7)67.8 (58.9-76.8)
    All parameters93.2 (92.1-94.4)92.4 (90.9-93.9)93.7 (92.1-95.3)

    • AI = artificial intelligence; AUC = area under the receiver operating characteristic curve; n/a = not applicable.

    • ↵a Acquiring reliable data for younger children poses substantial challenges.1

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