A forced cough sound based pulmonary function assessment method by using machine learning

Cough Sounds in Screening and Diagnostics: A Scoping Review

OBJECTIVE

The aim of the study was to examine applications of cough sounds towards screening tools and diagnostics in the biomedical and engineering literature, with particular focus on disease types, acoustic data collection protocols, data processing and analytics, accuracy, and limitations.

DATA SOURCES

PubMed, EMBASE, Web of Science, Scopus, Cochrane Library, IEEE Xplore, Engineering Village, and ACM Digital Library were searched from inception to August 2021.

REVIEW METHODS

A scoping review was conducted on screening and diagnostic uses of cough sounds in adults, children, and animals, in English peer-reviewed and gray literature of any design.

RESULTS

From a total of 438 abstracts screened, 108 articles met inclusion criteria. Human studies were most common (77.8%); the majority focused on adults (57.3%). Single-modality acoustic data collection was most common (71.2%), with few multimodal studies, including plethysmography (15.7%) and clinico-demographic data (7.4%). Data analytics methods were highly variable, with 61.1% using machine learning, the majority of which (78.8%) were published after 2010. Studies commonly focused on cough detection (41.7%) and screening of COVID-19 (11.1%); among pediatric studies, the most common focus was diagnosis of asthma (52.6%).

CONCLUSION

Though the use of cough sounds in diagnostics is not new, academic interest has accelerated in the past decade. Cough sound offers the possibility of an accessible, noninvasive, and low-cost disease biomarker, particularly in the era of rapid development of machine learning capabilities in combination with the ubiquity of cellular technology with high-quality recording capability. However, most cough sound literature hinges on nonstandardized data collection protocols and small, nondiverse, single-modality datasets, with limited external validity. Laryngoscope, 134:1023-1031, 2024.

A noval pulmonary function evaluation method based on ResNet50 + SVR model and cough

Traditionally, the clinical evaluation of respiratory diseases was pulmonary function testing, which can be used for the detection of severity and prognosis through pulmonary function parameters. However, this method is limited by the complex process, which is impossible for patients to monitor daily. In order to evaluate pulmonary function parameters conveniently with less time and location restrictions, cough sound is the substitute parameter. In this paper, 371 cough sounds segments from 150 individuals were separated into 309 and 62 as the training and test samples. Short-time Fourier transform (STFT) was applied to transform cough sound into spectrogram, and ResNet50 model was used to extract 2048-dimensional features. Through support vector regression (SVR) model with biological attributes, the data were regressed with pulmonary function parameters, FEV1, FEV1%, FEV1/FVC, FVC, FVC%, and the performance of this models was evaluated with fivefold cross-validation. Combines with deep learning and machine learning technologies, the better results in the case of small samples were achieved. Using the coefficient of determination (R2), the ResNet50 + SVR model shows best performance in five basic pulmonary function parameters evaluation as FEV1(0.94), FEV1%(0.84), FEV1/FVC(0.68), FVC(0.92), and FVC%(0.72). This ResNet50 + SVR hybrid model shows excellent evaluation of pulmonary function parameters during coughing, making it possible to realize a simple and rapid evaluation for pneumonia patients. The technology implemented in this paper is beneficial in judge the patient's condition, realize early screening of respiratory diseases, evaluate postoperative disease changes and detect respiratory infectious diseases without time and location restrictions.

Research on the Application of Artificial Intelligence in Public Health Management: Leveraging Artificial Intelligence to Improve COVID-19 CT Image Diagnosis

Since the start of 2020, the outbreak of the Coronavirus disease (COVID-19) has been a global public health emergency, and it has caused unprecedented economic and social disaster. In order to improve the diagnosis efficiency of COVID-19 patients, a number of researchers have conducted extensive studies on applying artificial intelligence techniques to the analysis of COVID-19-related medical images. The automatic segmentation of lesions from computed tomography (CT) images using deep learning provides an important basis for the quantification and diagnosis of COVID-19 cases. For a deep learning-based CT diagnostic method, a few of accurate pixel-level labels are essential for the training process of a model. However, the translucent ground-glass area of the lesion usually leads to mislabeling while performing the manual labeling operation, which weakens the accuracy of the model. In this work, we propose a method for correcting rough labels; that is, to hierarchize these rough labels into precise ones by performing an analysis on the pixel distribution of the infected and normal areas in the lung. The proposed method corrects the incorrectly labeled pixels and enables the deep learning model to learn the infected degree of each infected pixel, with which an aiding system (named DLShelper) for COVID-19 CT image diagnosis using the hierarchical labels is also proposed. The DLShelper targets lesion segmentation from CT images, as well as the severity grading. The DLShelper assists medical staff in efficient diagnosis by providing rich auxiliary diagnostic information (including the severity grade, the proportions of the lesion and the visualization of the lesion area). A comprehensive experiment based on a public COVID-19 CT image dataset is also conducted, and the experimental results show that the DLShelper significantly improves the accuracy of segmentation for the lesion areas and also achieves a promising accuracy for the severity grading task.

Usefulness of new acoustic respiratory sound monitoring with artificial intelligence for upper airway assessment in obese patients during monitored anesthesia care

Monitored anesthesia care (MAC) often causes airway complications, particularly posing an elevated risk 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 quantitative value (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 the end 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.