Real-World Verification of Artificial Intelligence Algorithm-Assisted Auscultation of Breath Sounds in Children

An explainable and accurate transformer-based deep learning model for wheeze classification utilizing real-world pediatric data

Auscultation is a method that involves listening to sounds from the patient's body, mainly using a stethoscope, to diagnose diseases. The stethoscope allows for non-invasive, real-time diagnosis, and it is ideal for diagnosing respiratory diseases and first aid. However, accurate interpretation of respiratory sounds using a stethoscope is a subjective process that requires considerable expertise from clinicians. To overcome the shortcomings of existing stethoscopes, research is actively being conducted to develop an artificial intelligence deep learning model that can interpret breathing sounds recorded through electronic stethoscopes. Most recent studies in this area have focused on CNN-based respiratory sound classification models. However, such CNN models are limited in their ability to accurately interpret conditions that require longer overall length and more detailed context. Therefore, in the present work, we apply the Transformer model-based Audio Spectrogram Transformer (AST) model to our actual clinical practice data. This prospective study targeted children who visited the pediatric departments of two university hospitals in South Korea from 2019 to 2020. A pediatric pulmonologist recorded breath sounds, and a pediatric breath sound dataset was constructed through double-blind verification. We then developed a deep learning model that applied the pre-trained weights of the AST model to our data with a total of 194 wheezes and 531 other respiratory sounds. We compared the performance of the proposed model with that of a previously published CNN-based model and also conducted performance tests using previous datasets. To ensure the reliability of the proposed model, we visualized the classification process using Score-Class Activation Mapping (Score-CAM). Our model had an accuracy of 91.1%, area under the curve (AUC) of 86.6%, precision of 88.2%, recall of 76.9%, and F1-score of 82.2%. Ultimately, the proposed transformer-based model showed high accuracy in wheezing detection, and the decision-making process of the model was also verified to be reliable. The artificial intelligence deep learning model we have developed and described in this study is expected to help accurately diagnose pediatric respiratory diseases in real-world clinical practice.

Early Diagnosis of Pneumonia and Chronic Obstructive Pulmonary Disease with a Smart Stethoscope with Cloud Server-Embedded Machine Learning in the Post-COVID-19 Era

Background/Objectives: Respiratory diseases are common and result in high mortality, especially in the elderly, with pneumonia and chronic obstructive pulmonary disease (COPD). Auscultation of lung sounds using a stethoscope is a crucial method for diagnosis, but it may require specialized training and the involvement of pulmonologists. This study aims to assist medical professionals who are non-pulmonologist doctors in early screening for pneumonia and COPD by developing a smart stethoscope with cloud server-embedded machine learning to diagnose lung sounds. Methods: The smart stethoscope was developed using a Micro-Electro-Mechanical system (MEMS) microphone to record lung sounds in the mobile application and then send them wirelessly to a cloud server for real-time machine learning classification. Results: The model of the smart stethoscope classifies lung sounds into four categories: normal, pneumonia, COPD, and other respiratory diseases. It achieved an accuracy of 89%, a sensitivity of 89.75%, and a specificity of 95%. In addition, testing with healthy volunteers yielded an accuracy of 80% in distinguishing normal and diseased lungs. Moreover, the performance comparison between the smart stethoscope and two commercial auscultation stethoscopes showed comparable sound quality and loudness results. Conclusions: The smart stethoscope holds great promise for improving healthcare delivery in the post-COVID-19 era, offering the probability of the most likely respiratory conditions for early diagnosis of pneumonia, COPD, and other respiratory diseases. Its user-friendly design and machine learning capabilities provide a valuable resource for non-pulmonologist doctors by delivering timely, evidence-based diagnoses, aiding treatment decisions, and paving the way for more accessible respiratory care.

Artificial intelligence and wheezing in children: where are we now?

Wheezing is a common condition in childhood, and its prevalence has increased in the last decade. Up to one-third of preschoolers develop recurrent wheezing, significantly impacting their quality of life and healthcare resources. Artificial Intelligence (AI) technologies have recently been applied in paediatric allergology and pulmonology, contributing to disease recognition, risk stratification, and decision support. Additionally, the COVID-19 pandemic has shaped healthcare systems, resulting in an increased workload and the necessity to reduce access to hospital facilities. In this view, AI and Machine Learning (ML) approaches can help address current issues in managing preschool wheezing, from its recognition with AI-augmented stethoscopes and monitoring with smartphone applications, aiming to improve parent-led/self-management and reducing economic and social costs. Moreover, in the last decade, ML algorithms have been applied in wheezing phenotyping, also contributing to identifying specific genes, and have been proven to even predict asthma in preschoolers. This minireview aims to update our knowledge on recent advancements of AI applications in childhood wheezing, summarizing and discussing the current evidence in recognition, diagnosis, phenotyping, and asthma prediction, with an overview of home monitoring and tele-management.

Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis

BACKGROUND

The interpretation of lung sounds plays a crucial role in the appropriate diagnosis and management of pediatric asthma. Applying artificial intelligence (AI) to this task has the potential to better standardize assessment and may even improve its predictive potential.

OBJECTIVE

This study aims to objectively review the literature on AI-assisted lung auscultation for pediatric asthma and provide a balanced assessment of its strengths, weaknesses, opportunities, and threats.

METHODS

A scoping review on AI-assisted lung sound analysis in children with asthma was conducted across 4 major scientific databases (PubMed, MEDLINE Ovid, Embase, and Web of Science), supplemented by a gray literature search on Google Scholar, to identify relevant studies published from January 1, 2000, until May 23, 2023. The search strategy incorporated a combination of keywords related to AI, pulmonary auscultation, children, and asthma. The quality of eligible studies was assessed using the ChAMAI (Checklist for the Assessment of Medical Artificial Intelligence).

RESULTS

The search identified 7 relevant studies out of 82 (9%) to be included through an academic literature search, while 11 of 250 (4.4%) studies from the gray literature search were considered but not included in the subsequent review and quality assessment. All had poor to medium ChAMAI scores, mostly due to the absence of external validation. Identified strengths were improved predictive accuracy of AI to allow for prompt and early diagnosis, personalized management strategies, and remote monitoring capabilities. Weaknesses were the heterogeneity between studies and the lack of standardization in data collection and interpretation. Opportunities were the potential of coordinated surveillance, growing data sets, and new ways of collaboratively learning from distributed data. Threats were both generic for the field of medical AI (loss of interpretability) but also specific to the use case, as clinicians might lose the skill of auscultation.

CONCLUSIONS

To achieve the opportunities of automated lung auscultation, there is a need to address weaknesses and threats with large-scale coordinated data collection in globally representative populations and leveraging new approaches to collaborative learning.

Deep learning-based lung sound analysis for intelligent stethoscope

Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine. The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education. On this basis, machine learning, particularly deep learning, enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes. This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence (AI) in this field. We focus on each component of deep learning-based lung sound analysis systems, including the task categories, public datasets, denoising methods, and, most importantly, existing deep learning methods, i.e., the state-of-the-art approaches to convert lung sounds into two-dimensional (2D) spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds. Additionally, this review highlights current challenges in this field, including the variety of devices, noise sensitivity, and poor interpretability of deep models. To address the poor reproducibility and variety of deep learning in this field, this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension: https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis .

Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging

Respiratory disorders, being one of the leading causes of disability worldwide, account for constant evolution in management technologies, resulting in the incorporation of artificial intelligence (AI) in the recording and analysis of lung sounds to aid diagnosis in clinical pulmonology practice. Although lung sound auscultation is a common clinical practice, its use in diagnosis is limited due to its high variability and subjectivity. We review the origin of lung sounds, various auscultation and processing methods over the years and their clinical applications to understand the potential for a lung sound auscultation and analysis device. Respiratory sounds result from the intra-pulmonary collision of molecules contained in the air, leading to turbulent flow and subsequent sound production. These sounds have been recorded via an electronic stethoscope and analyzed using back-propagation neural networks, wavelet transform models, Gaussian mixture models and recently with machine learning and deep learning models with possible use in asthma, COVID-19, asbestosis and interstitial lung disease. The purpose of this review was to summarize lung sound physiology, recording technologies and diagnostics methods using AI for digital pulmonology practice. Future research and development in recording and analyzing respiratory sounds in real time could revolutionize clinical practice for both the patients and the healthcare personnel.

Chest Examination 3.0 With Wireless Technology in a Clinical Case Based on Literature Review

Physicians use auscultation as a standard method of thoracic examination: it is simple, reliable, non-invasive, and widely accepted. Artificial intelligence (AI) is the new frontier of thoracic examination as it makes it possible to integrate all available data (clinical, instrumental, laboratory, functional), allowing for objective assessments, precise diagnoses, and even the phenotypical characterization of lung diseases. Increasing the sensitivity and specificity of examinations helps provide tailored diagnostic and therapeutic indications, which also take into account the patient's clinical history and comorbidities. Several clinical studies, mainly conducted in children, have shown a good concordance between traditional and AI-assisted auscultation in detecting fibrotic diseases. On the other hand, the use of AI for the diagnosis of obstructive pulmonary disease is still debated as it gave inconsistent results when detecting certain types of lung noises, such as wet and dry crackles. Therefore, the application of AI in clinical practice needs further investigation. In particular, the pilot case report aims to address the use of this technology in restrictive lung disease, which in this specific case is pulmonary sarcoidosis. In the case we present, data integration allowed us to make the right diagnosis, avoid invasive procedures, and reduce the costs for the national health system; we show that integrating technologies can improve the diagnosis of restrictive lung disease. Randomized controlled trials will be needed to confirm the conclusions of this preliminary work.

Review on the Advancements of Stethoscope Types in Chest Auscultation

Stethoscopes were originally designed for the auscultation of a patient's chest for the purpose of listening to lung and heart sounds. These aid medical professionals in their evaluation of the cardiovascular and respiratory systems, as well as in other applications, such as listening to bowel sounds in the gastrointestinal system or assessing for vascular bruits. Listening to internal sounds during chest auscultation aids healthcare professionals in their diagnosis of a patient's illness. We performed an extensive literature review on the currently available stethoscopes specifically for use in chest auscultation. By understanding the specificities of the different stethoscopes available, healthcare professionals can capitalize on their beneficial features, to serve both clinical and educational purposes. Additionally, the ongoing COVID-19 pandemic has also highlighted the unique application of digital stethoscopes for telemedicine. Thus, the advantages and limitations of digital stethoscopes are reviewed. Lastly, to determine the best available stethoscopes in the healthcare industry, this literature review explored various benchmarking methods that can be used to identify areas of improvement for existing stethoscopes, as well as to serve as a standard for the general comparison of stethoscope quality. The potential use of digital stethoscopes for telemedicine amidst ongoing technological advancements in wearable sensors and modern communication facilities such as 5G are also discussed. Based on the ongoing trend in advancements in wearable technology, telemedicine, and smart hospitals, understanding the benefits and limitations of the digital stethoscope is an essential consideration for potential equipment deployment, especially during the height of the current COVID-19 pandemic and, more importantly, for future healthcare crises when human and resource mobility is restricted.

SPRSound: Open-Source SJTU Paediatric Respiratory Sound Database

It has proved that the auscultation of respiratory sound has advantage in early respiratory diagnosis. Various methods have been raised to perform automatic respiratory sound analysis to reduce subjective diagnosis and physicians' workload. However, these methods highly rely on the quality of respiratory sound database. In this work, we have developed the first open-access paediatric respiratory sound database, SPRSound. The database consists of 2,683 records and 9,089 respiratory sound events from 292 participants. Accurate label is important to achieve a good prediction for adventitious respiratory sound classification problem. A custom-made sound label annotation software (SoundAnn) has been developed to perform sound editing, sound annotation, and quality assurance evaluation. A team of 11 experienced paediatric physicians is involved in the entire process to establish golden standard reference for the dataset. To verify the robustness and accuracy of the classification model, we have investigated the effects of different feature extraction methods and machine learning classifiers on the classification performance of our dataset. As such, we have achieved a score of 75.22%, 61.57%, 56.71%, and 37.84% for the four different classification challenges at the event level and record level.

A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions

In recent years, artificial intelligence has had a tremendous impact on every field, and several definitions of its different types have been provided. In the literature, most articles focus on the extraordinary capabilities of artificial intelligence. Recently, some challenges such as security, safety, fairness, robustness, and energy consumption have been reported during the development of intelligent systems. As the usage of intelligent systems increases, the number of new challenges increases. Obviously, during the evolution of artificial narrow intelligence to artificial super intelligence, the viewpoint on the challenges such as security will be changed. In addition, the recent development of human-level intelligence cannot appropriately happen without considering whole challenges in designing intelligent systems. Considering the mentioned situation, no study in the literature summarizes the challenges in designing artificial intelligence. In this paper, a review of the challenges is presented. Then, some important research questions about the future dynamism of challenges and their relationships are answered.

The discerning ear: cardiac auscultation in the era of artificial intelligence and telemedicine

Heart murmur, a thoracic auscultatory finding of cardiovascular origin, is extremely common in childhood and can appear at any age from premature newborn to late adolescence. The objective of this review is to provide a modern examination and update of cardiac murmur auscultation in this new era of artificial intelligence (AI) and telemedicine. First, we provide a comprehensive review of the causes and differential diagnosis, clinical features, evaluation, and long-term management of paediatric heart murmurs. Next, we provide a brief history of computer-assisted auscultation and murmur analysis, along with insight into the engineering design of the digital stethoscope. We conclude with a discussion of the paradigm shifting impact of deep learning on murmur analysis, AI-assisted auscultation, and the implications of these technologies on telemedicine in paediatric cardiology. It is our hope that this article provides an updated perspective on the impact of AI on cardiac auscultation for the modern paediatric cardiologist.