Dataset Information

Development and Clinical Application of a Novel Non-contact Early Airflow Limitation Screening System Using an Infrared Time-of-Flight Depth Image Sensor.

ABSTRACT: Obstructive pulmonary diseases, such as diffuse panbronchiolitis (DPB), asthma, chronic obstructive pulmonary disease (COPD), and asthma COPD overlap syndrome (ACOS) trigger a severe reaction at some situations. Detecting early airflow limitation caused by diseases above is critical to stop the progression. Thus, there is a need for tools to enable self-screening of early airflow limitation at home. Here, we developed a novel non-contact early airflow limitation screening system (EAFL-SS) that does not require calibration to the individual by a spirometer. The system is based on an infrared time-of-flight (ToF) depth image sensor, which is integrated into several smartphones for photography focusing or augmented reality. The EAFL-SS comprised an 850 nm infrared ToF depth image sensor (224 × 171 pixels) and custom-built data processing algorithms to visualize anterior-thorax three-dimensional motions in real-time. Multiple linear regression analysis was used to determine the amount of air compulsorily exhaled after maximal inspiration (referred to as the forced vital capacity, FVC EAFL -SS) from the ToF-derived anterior-thorax forced vital capacity (FVC), height, and body mass index as explanatory variables and spirometer-derived FVC as the objective variable. The non-contact measurement is automatically started when an examinee is sitting 35 cm away from the EAFL-SS. A clinical test was conducted with 32 COPD patients (27/5 M/F, 67-93 years) as typical airflow limitation cases recruited at St. Marianna University Hospital and 21 healthy volunteers (10/11 M/F, 23-79 years). The EAFL-SS was used to monitor the respiration of examinees during forced exhalation while sitting still, and a spirometer was used simultaneously as a reference. The forced expiratory volume in 1 s (FEV1% EAFL -SS) was evaluated as a percentage of the FVC EAFL -SS, where values less than 70% indicated suspected airflow limitation. Leave-one-out cross-validation analysis revealed that this system provided 81% sensitivity and 90% specificity. Further, the FEV1 EAFL -SS values were closely correlated with that measured using a spirometer (r = 0.85, p < 0.0001). Hence, EAFL-SS appears promising for early airflow limitation screening at home.

SUBMITTER: Takamoto H

PROVIDER: S-EPMC7516262 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Development and Clinical Application of a Novel Non-contact Early Airflow Limitation Screening System Using an Infrared Time-of-Flight Depth Image Sensor.

Takamoto Hiroki H Nishine Hiroki H Sato Shohei S Sun Guanghao G Watanabe Sadao S Seokjin Kim K Asai Masahito M Mineshita Masamichi M Matsui Takemi T

Frontiers in physiology 20200911

Obstructive pulmonary diseases, such as diffuse panbronchiolitis (DPB), asthma, chronic obstructive pulmonary disease (COPD), and asthma COPD overlap syndrome (ACOS) trigger a severe reaction at some situations. Detecting early airflow limitation caused by diseases above is critical to stop the progression. Thus, there is a need for tools to enable self-screening of early airflow limitation at home. Here, we developed a novel non-contact early airflow limitation screening system (EAFL-SS) that d ...[more]

PMID: 33013479

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Project description:Study objectivesThe presence of flow limitation during sleep is associated with adverse health consequences independent of obstructive sleep apnea (OSA) severity (apnea-hypopnea index, AHI), but remains extremely challenging to quantify. Here we present a unique library and an accompanying automated method that we apply to investigate flow limitation during sleep.MethodsA library of 117,871 breaths (N = 40 participants) were visually classified (certain flow limitation, possible flow limitation, normal) using airflow shape and physiological signals (ventilatory drive per intra-esophageal diaphragm EMG). An ordinal regression model was developed to quantify flow limitation certainty using flow-shape features (e.g. flattening, scooping); breath-by-breath agreement (Cohen's ƙ); and overnight flow limitation frequency (R2, %breaths in certain or possible categories during sleep) were compared against visual scoring. Subsequent application examined flow limitation frequency during arousals and stable breathing, and associations with ventilatory drive.ResultsThe model (23 features) assessed flow limitation with good agreement (breath-by-breath ƙ = 0.572, p < 0.001) and minimal error (overnight flow limitation frequency R2 = 0.86, error = 7.2%). Flow limitation frequency was largely independent of AHI (R2 = 0.16) and varied widely within individuals with OSA (74[32-95]%breaths, mean[range], AHI > 15/h, N = 22). Flow limitation was unexpectedly frequent but variable during arousals (40[5-85]%breaths) and stable breathing (58[12-91]%breaths), and was associated with elevated ventilatory drive (R2 = 0.26-0.29; R2 < 0.01 AHI v. drive).ConclusionsOur method enables quantification of flow limitation frequency, a key aspect of obstructive sleep-disordered breathing that is independent of the AHI and often unavailable. Flow limitation frequency varies widely between individuals, is prevalent during arousals and stable breathing, and reveals elevated ventilatory drive. Clinical trial registration: The current observational physiology study does not qualify as a clinical trial.

Project description:OBJECTIVES:Subglottic stenosis (SGS) is one of the most common airway disorders in pediatric patients. Currently, treatment decisions rely primarily on the Cotton-Myer scale, which classifies SGS severity based on percentage reduction in airspace cross-sectional area (CSA). However, the precise relationship between upper airway resistance and subglottic CSA is unknown. We hypothesize that airway resistance can be described by the Bernoulli Obstruction Theory, which predicts that airway resistance is inversely proportional to airspace CSA ( R∝A-1) in cases of severe constriction. METHODS:Computed tomography (CT) scans of six healthy subjects and five SGS patients were used to create three-dimensional models of the respiratory tract from nostrils to carina. Cylindrical segments of varying lengths and varying diameters were digitally inserted in the subglottis of the healthy subjects to create simulated SGS models. Computational fluid dynamics simulations were run, and airway resistance was computed in the simulated SGS models and actual SGS models. RESULTS:Constriction diameter had a greater impact in airway resistance than constriction length. In agreement with the Bernoulli Obstruction Theory, airway resistance in the simulated SGS models was well represented by the power law R=aAb, where a is a constant and the exponent b ranged from -0.85 to -1.07. The percentage reduction in airflow (QOBSTRUCTIONQHEALTHY) at a constant pressure drop was found to be directly proportional to the percentage reduction in CSA (AOBSTRUCTIONAHEALTHY) in the limit of severe constrictions, namely QOBSTRUCTIONQHEALTHY=kAOBSTRUCTIONAHEALTHY, where k=2.25 ± 0.15. Airway resistances in the simulated SGS models were similar to resistances in models based on CT scans of actual SGS patients, suggesting that our simulated SGS models were representative of airway resistance in actual SGS patients. CONCLUSION:Our computer simulations suggest that the degree of airflow limitation in SGS patients may be estimated based on anatomic measurements alone. Future studies are recommended to test these predictions in larger cohorts. LEVEL OF EVIDENCE:4. Laryngoscope, 128:1551-1557, 2018.

Dataset Information

Development and Clinical Application of a Novel Non-contact Early Airflow Limitation Screening System Using an Infrared Time-of-Flight Depth Image Sensor.

Publications

Development and Clinical Application of a Novel Non-contact Early Airflow Limitation Screening System Using an Infrared Time-of-Flight Depth Image Sensor.

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