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35 pages, 5195 KiB  
Article
A Multimodal AI Framework for Automated Multiclass Lung Disease Diagnosis from Respiratory Sounds with Simulated Biomarker Fusion and Personalized Medication Recommendation
by Abdullah, Zulaikha Fatima, Jawad Abdullah, José Luis Oropeza Rodríguez and Grigori Sidorov
Int. J. Mol. Sci. 2025, 26(15), 7135; https://doi.org/10.3390/ijms26157135 - 24 Jul 2025
Viewed by 463
Abstract
Respiratory diseases represent a persistent global health challenge, underscoring the need for intelligent, accurate, and personalized diagnostic and therapeutic systems. Existing methods frequently suffer from limitations in diagnostic precision, lack of individualized treatment, and constrained adaptability to complex clinical scenarios. To address these [...] Read more.
Respiratory diseases represent a persistent global health challenge, underscoring the need for intelligent, accurate, and personalized diagnostic and therapeutic systems. Existing methods frequently suffer from limitations in diagnostic precision, lack of individualized treatment, and constrained adaptability to complex clinical scenarios. To address these challenges, our study introduces a modular AI-powered framework that integrates an audio-based disease classification model with simulated molecular biomarker profiles to evaluate the feasibility of future multimodal diagnostic extensions, alongside a synthetic-data-driven prescription recommendation engine. The disease classification model analyzes respiratory sound recordings and accurately distinguishes among eight clinical classes: bronchiectasis, pneumonia, upper respiratory tract infection (URTI), lower respiratory tract infection (LRTI), asthma, chronic obstructive pulmonary disease (COPD), bronchiolitis, and healthy respiratory state. The proposed model achieved a classification accuracy of 99.99% on a holdout test set, including 94.2% accuracy on pediatric samples. In parallel, the prescription module provides individualized treatment recommendations comprising drug, dosage, and frequency trained on a carefully constructed synthetic dataset designed to emulate real-world prescribing logic.The model achieved over 99% accuracy in medication prediction tasks, outperforming baseline models such as those discussed in research. Minimal misclassification in the confusion matrix and strong clinician agreement on 200 prescriptions (Cohen’s κ = 0.91 [0.87–0.94] for drug selection, 0.78 [0.74–0.81] for dosage, 0.96 [0.93–0.98] for frequency) further affirm the system’s reliability. Adjusted clinician disagreement rates were 2.7% (drug), 6.4% (dosage), and 1.5% (frequency). SHAP analysis identified age and smoking as key predictors, enhancing model explainability. Dosage accuracy was 91.3%, and most disagreements occurred in renal-impaired and pediatric cases. However, our study is presented strictly as a proof-of-concept. The use of synthetic data and the absence of access to real patient records constitute key limitations. A trialed clinical deployment was conducted under a controlled environment with a positive rate of satisfaction from experts and users, but the proposed system must undergo extensive validation with de-identified electronic medical records (EMRs) and regulatory scrutiny before it can be considered for practical application. Nonetheless, the findings offer a promising foundation for the future development of clinically viable AI-assisted respiratory care tools. Full article
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8 pages, 1216 KiB  
Proceeding Paper
Enhanced Lung Disease Detection Using Double Denoising and 1D Convolutional Neural Networks on Respiratory Sound Analysis
by Reshma Sreejith, R. Kanesaraj Ramasamy, Wan-Noorshahida Mohd-Isa and Junaidi Abdullah
Comput. Sci. Math. Forum 2025, 10(1), 7; https://doi.org/10.3390/cmsf2025010007 - 24 Jun 2025
Viewed by 308
Abstract
The accurate and early detection of respiratory diseases is vital for effective diagnosis and treatment. This study presents a new approach for classifying lung sounds using a double denoising method combined with a 1D Convolutional Neural Network (CNN). The preprocessing uses Fast Fourier [...] Read more.
The accurate and early detection of respiratory diseases is vital for effective diagnosis and treatment. This study presents a new approach for classifying lung sounds using a double denoising method combined with a 1D Convolutional Neural Network (CNN). The preprocessing uses Fast Fourier Transform to clean up sounds and High-Pass Filtering to improve the quality of breathing sounds by eliminating noise and low-frequency interruptions. The Short-Time Fourier Transform (STFT) extracts features that capture localised frequency variations, crucial for distinguishing normal and abnormal respiratory sounds. These features are input into the 1D CNN, which classifies diseases such as bronchiectasis, pneumonia, asthma, COPD, healthy, and URTI. The dual denoising method enhances signal clarity and classification performance. The model achieved 96% validation accuracy, highlighting its reliability in detecting respiratory conditions. The results emphasise the effectiveness of combining signal augmentation with deep learning for automated respiratory sound analysis, with future research focusing on dataset expansion and model refinement for clinical use. Full article
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28 pages, 11981 KiB  
Review
Artificial Intelligence in Respiratory Health: A Review of AI-Driven Analysis of Oral and Nasal Breathing Sounds for Pulmonary Assessment
by Shiva Shokouhmand, Smriti Bhatt and Miad Faezipour
Electronics 2025, 14(10), 1994; https://doi.org/10.3390/electronics14101994 - 14 May 2025
Cited by 1 | Viewed by 2207
Abstract
Continuous monitoring of pulmonary function is crucial for effective respiratory disease management. The COVID-19 pandemic has also underscored the need for accessible and convenient diagnostic tools for respiratory health assessment. While traditional lung sound auscultation has been the primary method for evaluating pulmonary [...] Read more.
Continuous monitoring of pulmonary function is crucial for effective respiratory disease management. The COVID-19 pandemic has also underscored the need for accessible and convenient diagnostic tools for respiratory health assessment. While traditional lung sound auscultation has been the primary method for evaluating pulmonary function, emerging research highlights the diagnostic potential of nasal and oral breathing sounds. These sounds, shaped by the upper airway, serve as valuable non-invasive biomarkers for pulmonary health and disease detection. Recent advancements in artificial intelligence (AI) have significantly enhanced respiratory sound analysis by enabling automated feature extraction and pattern recognition from spectral and temporal characteristics or even raw acoustic signals. AI-driven models have demonstrated promising accuracy in detecting respiratory conditions, paving the way for real-time, smartphone-based respiratory monitoring. This review examines the potential of AI-enhanced respiratory sound analysis, discussing methodologies, available datasets, and future directions toward scalable and accessible diagnostic solutions. Full article
(This article belongs to the Special Issue Medical Applications of Artificial Intelligence)
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12 pages, 725 KiB  
Article
Use of Ultrasonography for the Evaluation of Lung Lesions in Lambs with Respiratory Complex
by Alejandro Sánchez-Fernández, Juan Carlos Gardón, Carla Ibáñez and Joel Bueso-Ródenas
Animals 2025, 15(8), 1153; https://doi.org/10.3390/ani15081153 - 17 Apr 2025
Viewed by 653
Abstract
The ovine respiratory complex significantly affects lamb welfare and production efficiency, necessitating accurate diagnostic methods for pulmonary lesions. This study explores the relationship between clinical scoring, auscultation, ultrasonography, and macroscopic post-mortem evaluation to assess respiratory disease in 111 lambs. A standardized clinical scoring [...] Read more.
The ovine respiratory complex significantly affects lamb welfare and production efficiency, necessitating accurate diagnostic methods for pulmonary lesions. This study explores the relationship between clinical scoring, auscultation, ultrasonography, and macroscopic post-mortem evaluation to assess respiratory disease in 111 lambs. A standardized clinical scoring system, adapted from bovine models, evaluated ocular and nasal discharge, head tilt, cough, and rectal temperature. Auscultation categorized pulmonary sounds, while ultrasonography identified lung abnormalities, including B-lines, consolidations, pleural effusion, and abscesses. Macroscopic post-mortem examinations confirmed lesion extent. Kendall–Tau-B correlation coefficient analysis revealed significant associations between the methods (p < 0.01), with a high correlation between auscultation and clinical scoring τ of 0.634 (95% CI: 0.489 to 0.765), auscultation and ultrasonography τ of 0.611 (95% CI: 0.500 to 0.710), and ultrasonography and post-mortem findings τ 0.608 (95% CI: 0.460 to 0.731). While auscultation and clinical scoring provided useful insights, ultrasonography exhibited superior sensitivity in detecting subclinical and early-stage lesions, aligning closely with post-mortem evaluations. These findings emphasize ultrasonography as an effective tool for diagnosing respiratory disease in lambs, improving diagnostic accuracy and enabling timely interventions to mitigate disease impact and reduce antimicrobial use. Full article
(This article belongs to the Collection Diseases of Small Ruminants)
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24 pages, 4555 KiB  
Review
Biophysics of Voice Onset: A Comprehensive Overview
by Philippe H. DeJonckere and Jean Lebacq
Bioengineering 2025, 12(2), 155; https://doi.org/10.3390/bioengineering12020155 - 6 Feb 2025
Viewed by 1568
Abstract
Voice onset is the sequence of events between the first detectable movement of the vocal folds (VFs) and the stable vibration of the vocal folds. It is considered a critical phase of phonation, and the different modalities of voice onset and their distinctive [...] Read more.
Voice onset is the sequence of events between the first detectable movement of the vocal folds (VFs) and the stable vibration of the vocal folds. It is considered a critical phase of phonation, and the different modalities of voice onset and their distinctive characteristics are analysed. Oscillation of the VFs can start from either a closed glottis with no airflow or an open glottis with airflow. The objective of this article is to provide a comprehensive survey of this transient phenomenon, from a biomechanical point of view, in normal modal (i.e., nonpathological) conditions of vocal emission. This synthetic overview mainly relies upon a number of recent experimental studies, all based on in vivo physiological measurements, and using a common, original and consistent methodology which combines high-speed imaging, sound analysis, electro-, photo-, flow- and ultrasound glottography. In this way, the two basic parameters—the instantaneous glottal area and the airflow—can be measured, and the instantaneous intraglottal pressure can be automatically calculated from the combined records, which gives a detailed insight, both qualitative and quantitative, into the onset phenomenon. The similarity of the methodology enables a link to be made with the biomechanics of sustained phonation. Essential is the temporal relationship between the glottal area and intraglottal pressure. The three key findings are (1) From the initial onset cycles onwards, the intraglottal pressure signal leads that of the opening signal, as in sustained voicing, which is the basic condition for an energy transfer from the lung pressure to the VF tissue. (2) This phase lead is primarily due to the skewing of the airflow curve to the right with respect to the glottal area curve, a consequence of the compressibility of air and the inertance of the vocal tract. (3) In case of a soft, physiological onset, the glottis shows a spindle-shaped configuration just before the oscillation begins. Using the same parameters (airflow, glottal area, intraglottal pressure), the mechanism of triggering the oscillation can be explained by the intraglottal aerodynamic condition. From the first cycles on, the VFs oscillate on either side of a paramedian axis. The amplitude of these free oscillations increases progressively before the first contact on the midline. Whether the first movement is lateral or medial cannot be defined. Moreover, this comprehensive synthesis of onset biomechanics and the links it creates sheds new light on comparable phenomena at the level of sound attack in wind instruments, as well as phenomena such as the production of intervals in the sung voice. Full article
(This article belongs to the Special Issue The Biophysics of Vocal Onset)
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4 pages, 1765 KiB  
Interesting Images
Dynamic Digital Radiography (DDR) in the Diagnosis of a Diaphragm Dysfunction
by Elisa Calabrò, Tiana Lisnic, Maurizio Cè, Laura Macrì, Francesca Lucrezia Rabaiotti and Michaela Cellina
Diagnostics 2025, 15(1), 2; https://doi.org/10.3390/diagnostics15010002 - 24 Dec 2024
Cited by 1 | Viewed by 1425
Abstract
Dynamic digital radiography (DDR) is a recent imaging technique that allows for real-time visualization of thoracic and pulmonary movement in synchronization with the breathing cycle, providing useful clinical information. A 46-year-old male, a former smoker, was evaluated for unexplained dyspnea and reduced exercise [...] Read more.
Dynamic digital radiography (DDR) is a recent imaging technique that allows for real-time visualization of thoracic and pulmonary movement in synchronization with the breathing cycle, providing useful clinical information. A 46-year-old male, a former smoker, was evaluated for unexplained dyspnea and reduced exercise tolerance. His medical history included a SARS-CoV-2 infection in 2021. On physical examination, decreased breath sounds were noted at the right-lung base. Spirometry showed results below predicted values. A standard chest radiograph revealed an elevated right hemidiaphragm, a finding not present in a previous CT scan performed during his SARS-CoV-2 infection. To better assess the diaphragmatic function, a posteroanterior DDR study was performed in the standing position with X-ray equipment (AeroDR TX, Konica Minolta Inc., Tokyo, Japan) during forced breath, with the following acquisition parameters: tube voltage, 100 kV; tube current, 50 mA; pulse duration of pulsed X-ray, 1.6 ms; source-to-image distance, 2 m; additional filter, 0.5 mm Al + 0.1 mm Cu. The exposure time was 12 s. The pixel size was 388 × 388 μm, the matrix size was 1024 × 768, and the overall image area was 40 × 30 cm. The dynamic imaging, captured at 15 frames/s, was then assessed on a dedicated workstation (Konica Minolta Inc., Tokyo, Japan). The dynamic acquisition showed a markedly reduced motion of the right diaphragm. The diagnosis of diaphragm dysfunction can be challenging due to its range of symptoms, which can vary from mild to severe dyspnea. The standard chest X-ray is usually the first exam to detect an elevated hemidiaphragm, which may suggest motion impairment or paralysis but fails to predict diaphragm function. Ultrasound (US) allows for the direct visualization of the diaphragm and its motion. Still, its effectiveness depends highly on the operator’s experience and could be limited by gas and abdominal fat. Moreover, ultrasound offers limited information regarding the lung parenchyma. On the other hand, high-resolution CT can be useful in identifying causes of diaphragmatic dysfunction, such as atrophy or eventration. However, it does not allow for the quantitative assessment of diaphragmatic movement and the differentiation between paralysis and dysfunction, especially in bilateral dysfunction, which is often overlooked due to the elevation of both hemidiaphragms. Dynamic Digital Radiography (DDR) has emerged as a valuable and innovative imaging technique due to its unique ability to evaluate diaphragm movement in real time, integrating dynamic functional information with static anatomical data. DDR provides both visual and quantitative analysis of the diaphragm’s motion, including excursion and speed, which leads to a definitive diagnosis. Additionally, DDR offers a range of post-processing techniques that provide information on lung movement and pulmonary ventilation. Based on these findings, the patient was referred to a thoracic surgeon and deemed a candidate for surgical plication of the right diaphragm. Full article
(This article belongs to the Special Issue Diagnosis of Cardio-Thoracic Diseases)
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24 pages, 6200 KiB  
Review
MEMS and ECM Sensor Technologies for Cardiorespiratory Sound Monitoring—A Comprehensive Review
by Yasaman Torabi, Shahram Shirani, James P. Reilly and Gail M. Gauvreau
Sensors 2024, 24(21), 7036; https://doi.org/10.3390/s24217036 - 31 Oct 2024
Cited by 2 | Viewed by 4213
Abstract
This paper presents a comprehensive review of cardiorespiratory auscultation sensing devices (i.e., stethoscopes), which is useful for understanding the theoretical aspects and practical design notes. In this paper, we first introduce the acoustic properties of the heart and lungs, as well as a [...] Read more.
This paper presents a comprehensive review of cardiorespiratory auscultation sensing devices (i.e., stethoscopes), which is useful for understanding the theoretical aspects and practical design notes. In this paper, we first introduce the acoustic properties of the heart and lungs, as well as a brief history of stethoscope evolution. Then, we discuss the basic concept of electret condenser microphones (ECMs) and a stethoscope based on them. Then, we discuss the microelectromechanical systems (MEMSs) technology, particularly focusing on piezoelectric transducer sensors. This paper comprehensively reviews sensing technologies for cardiorespiratory auscultation, emphasizing MEMS-based wearable designs in the past decade. To our knowledge, this is the first paper to summarize ECM and MEMS applications for heart and lung sound analysis. Full article
(This article belongs to the Section Wearables)
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16 pages, 624 KiB  
Article
Towards the Development of the Clinical Decision Support System for the Identification of Respiration Diseases via Lung Sound Classification Using 1D-CNN
by Syed Waqad Ali, Muhammad Munaf Rashid, Muhammad Uzair Yousuf, Sarmad Shams, Muhammad Asif, Muhammad Rehan and Ikram Din Ujjan
Sensors 2024, 24(21), 6887; https://doi.org/10.3390/s24216887 - 27 Oct 2024
Cited by 3 | Viewed by 1717
Abstract
Respiratory disorders are commonly regarded as complex disorders to diagnose due to their multi-factorial nature, encompassing the interplay between hereditary variables, comorbidities, environmental exposures, and therapies, among other contributing factors. This study presents a Clinical Decision Support System (CDSS) for the early detection [...] Read more.
Respiratory disorders are commonly regarded as complex disorders to diagnose due to their multi-factorial nature, encompassing the interplay between hereditary variables, comorbidities, environmental exposures, and therapies, among other contributing factors. This study presents a Clinical Decision Support System (CDSS) for the early detection of respiratory disorders using a one-dimensional convolutional neural network (1D-CNN) model. The ICBHI 2017 Breathing Sound Database, which contains samples of different breathing sounds, was used in this research. During pre-processing, audio clips were resampled to a uniform rate, and breathing cycles were segmented into individual instances of the lung sound. A One-Dimensional Convolutional Neural Network (1D-CNN) consisting of convolutional layers, max pooling layers, dropout layers, and fully connected layers, was designed to classify the processed clips into four categories: normal, crackles, wheezes, and combined crackles and wheezes. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to the training data. Hyperparameters were optimized using grid search with k−fold cross-validation. The model achieved an overall accuracy of 0.95, outperforming state-of-the-art methods. Particularly, the normal and crackles categories attained the highest F1-scores of 0.97 and 0.95, respectively. The model’s robustness was further validated through 5−fold and 10−fold cross-validation experiments. This research highlighted an essential aspect of diagnosing lung sounds through artificial intelligence and utilized the 1D-CNN to classify lung sounds accurately. The proposed advancement of technology shall enable medical care practitioners to diagnose lung disorders in an improved manner, leading to better patient care. Full article
(This article belongs to the Special Issue AI-Based Automated Recognition and Detection in Healthcare)
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14 pages, 464 KiB  
Article
Empowering Healthcare: TinyML for Precise Lung Disease Classification
by Youssef Abadade, Nabil Benamar, Miloud Bagaa and Habiba Chaoui
Future Internet 2024, 16(11), 391; https://doi.org/10.3390/fi16110391 - 25 Oct 2024
Cited by 4 | Viewed by 3589
Abstract
Respiratory diseases such as asthma pose significant global health challenges, necessitating efficient and accessible diagnostic methods. The traditional stethoscope is widely used as a non-invasive and patient-friendly tool for diagnosing respiratory conditions through lung auscultation. However, it has limitations, such as a lack [...] Read more.
Respiratory diseases such as asthma pose significant global health challenges, necessitating efficient and accessible diagnostic methods. The traditional stethoscope is widely used as a non-invasive and patient-friendly tool for diagnosing respiratory conditions through lung auscultation. However, it has limitations, such as a lack of recording functionality, dependence on the expertise and judgment of physicians, and the absence of noise-filtering capabilities. To overcome these limitations, digital stethoscopes have been developed to digitize and record lung sounds. Recently, there has been growing interest in the automated analysis of lung sounds using Deep Learning (DL). Nevertheless, the execution of large DL models in the cloud often leads to latency, dependency on internet connectivity, and potential privacy issues due to the transmission of sensitive health data. To address these challenges, we developed Tiny Machine Learning (TinyML) models for the real-time detection of respiratory conditions by using lung sound recordings, deployable on low-power, cost-effective devices like digital stethoscopes. We trained three machine learning models—a custom CNN, an Edge Impulse CNN, and a custom LSTM—on a publicly available lung sound dataset. Our data preprocessing included bandpass filtering and feature extraction through Mel-Frequency Cepstral Coefficients (MFCCs). We applied quantization techniques to ensure model efficiency. The custom CNN model achieved the highest performance, with 96% accuracy and 97% precision, recall, and F1-scores, while maintaining moderate resource usage. These findings highlight the potential of TinyML to provide accessible, reliable, and real-time diagnostic tools, particularly in remote and underserved areas, demonstrating the transformative impact of integrating advanced AI algorithms into portable medical devices. This advancement facilitates the prospect of automated respiratory health screening using lung sounds. Full article
(This article belongs to the Special Issue Edge Intelligence: Edge Computing for 5G and the Internet of Things)
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12 pages, 15147 KiB  
Article
Design and Analysis of a Contact Piezo Microphone for Recording Tracheal Breathing Sounds
by Walid Ashraf and Zahra Moussavi
Sensors 2024, 24(17), 5511; https://doi.org/10.3390/s24175511 - 26 Aug 2024
Cited by 1 | Viewed by 2426
Abstract
Analysis of tracheal breathing sounds (TBS) is a significant area of study in medical diagnostics and monitoring for respiratory diseases and obstructive sleep apnea (OSA). Recorded at the suprasternal notch, TBS can provide detailed insights into the respiratory system’s functioning and health. This [...] Read more.
Analysis of tracheal breathing sounds (TBS) is a significant area of study in medical diagnostics and monitoring for respiratory diseases and obstructive sleep apnea (OSA). Recorded at the suprasternal notch, TBS can provide detailed insights into the respiratory system’s functioning and health. This method has been particularly useful for non-invasive assessments and is used in various clinical settings, such as OSA, asthma, respiratory infectious diseases, lung function, and detection during either wakefulness or sleep. One of the challenges and limitations of TBS recording is the background noise, including speech sound, movement, and even non-tracheal breathing sounds propagating in the air. The breathing sounds captured from the nose or mouth can interfere with the tracheal breathing sounds, making it difficult to isolate the sounds necessary for accurate diagnostics. In this study, two surface microphones are proposed to accurately record TBS acquired solely from the trachea. The frequency response of each microphone is compared with a reference microphone. Additionally, this study evaluates the tracheal and lung breathing sounds of six participants recorded using the proposed microphones versus a commercial omnidirectional microphone, both in environments with and without background white noise. The proposed microphones demonstrated reduced susceptibility to background noise particularly in the frequency ranges (1800–2199) Hz and (2200–2599) Hz with maximum deviation of 2 dB and 2.1 dB, respectively, compared to 9 dB observed in the commercial microphone. The findings of this study have potential implications for improving the accuracy and reliability of respiratory diagnostics in clinical practice. Full article
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19 pages, 3746 KiB  
Article
An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning
by Brian Sang, Haoran Wen, Gregory Junek, Wendy Neveu, Lorenzo Di Francesco and Farrokh Ayazi
Biosensors 2024, 14(3), 118; https://doi.org/10.3390/bios14030118 - 22 Feb 2024
Cited by 7 | Viewed by 5015
Abstract
Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, [...] Read more.
Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, accurate respiratory data to patients. With this goal as our aim, we used a low-profile accelerometer-based wearable system that utilizes deep learning to objectively detect wheezing along with respiration rate using a single sensor. The miniature patch consists of a sensitive wideband MEMS accelerometer and low-noise CMOS interface electronics on a small board, which was then placed on nine conventional lung auscultation sites on the patient’s chest walls to capture the pulmonary-induced vibrations (PIVs). A deep learning model was developed and compared with a deterministic time–frequency method to objectively detect wheezing in the PIV signals using data captured from 52 diverse patients with respiratory diseases. The wearable accelerometer patch, paired with the deep learning model, demonstrated high fidelity in capturing and detecting respiratory wheezes and patterns across diverse and pertinent settings. It achieved accuracy, sensitivity, and specificity of 95%, 96%, and 93%, respectively, with an AUC of 0.99 on the test set—outperforming the deterministic time–frequency approach. Furthermore, the accelerometer patch outperforms the digital stethoscopes in sound analysis while offering immunity to ambient sounds, which not only enhances data quality and performance for computational wheeze detection by a significant margin but also provides a robust sensor solution that can quantify respiration patterns simultaneously. Full article
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23 pages, 1282 KiB  
Article
Classification of Adventitious Sounds Combining Cochleogram and Vision Transformers
by Loredana Daria Mang, Francisco David González Martínez, Damian Martinez Muñoz, Sebastián García Galán and Raquel Cortina
Sensors 2024, 24(2), 682; https://doi.org/10.3390/s24020682 - 21 Jan 2024
Cited by 8 | Viewed by 3191
Abstract
Early identification of respiratory irregularities is critical for improving lung health and reducing global mortality rates. The analysis of respiratory sounds plays a significant role in characterizing the respiratory system’s condition and identifying abnormalities. The main contribution of this study is to investigate [...] Read more.
Early identification of respiratory irregularities is critical for improving lung health and reducing global mortality rates. The analysis of respiratory sounds plays a significant role in characterizing the respiratory system’s condition and identifying abnormalities. The main contribution of this study is to investigate the performance when the input data, represented by cochleogram, is used to feed the Vision Transformer (ViT) architecture, since this input–classifier combination is the first time it has been applied to adventitious sound classification to our knowledge. Although ViT has shown promising results in audio classification tasks by applying self-attention to spectrogram patches, we extend this approach by applying the cochleogram, which captures specific spectro-temporal features of adventitious sounds. The proposed methodology is evaluated on the ICBHI dataset. We compare the classification performance of ViT with other state-of-the-art CNN approaches using spectrogram, Mel frequency cepstral coefficients, constant-Q transform, and cochleogram as input data. Our results confirm the superior classification performance combining cochleogram and ViT, highlighting the potential of ViT for reliable respiratory sound classification. This study contributes to the ongoing efforts in developing automatic intelligent techniques with the aim to significantly augment the speed and effectiveness of respiratory disease detection, thereby addressing a critical need in the medical field. Full article
(This article belongs to the Special Issue Advanced Machine Intelligence for Biomedical Signal Processing)
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16 pages, 2703 KiB  
Article
Automatic Robust Crackle Detection and Localization Approach Using AR-Based Spectral Estimation and Support Vector Machine
by Loredana Daria Mang, Julio José Carabias-Orti, Francisco Jesús Canadas-Quesada, Juan de la Torre-Cruz, Antonio Muñoz-Montoro, Pablo Revuelta-Sanz and Eilas Fernandez Combarro
Appl. Sci. 2023, 13(19), 10683; https://doi.org/10.3390/app131910683 - 26 Sep 2023
Cited by 2 | Viewed by 1569
Abstract
Auscultation primarily relies upon the acoustic expertise of individual doctors in identifying, through the use of a stethoscope, the presence of abnormal sounds such as crackles because the recognition of these sound patterns has critical importance in the context of early detection and [...] Read more.
Auscultation primarily relies upon the acoustic expertise of individual doctors in identifying, through the use of a stethoscope, the presence of abnormal sounds such as crackles because the recognition of these sound patterns has critical importance in the context of early detection and diagnosis of respiratory pathologies. In this paper, we propose a novel method combining autoregressive (AR)-based spectral features and a support vector machine (SVM) classifier to detect the presence of crackle events and their temporal location within the input signal. A preprocessing stage is performed to discard information out of the band of interest and define the segments for short-time signal analysis. The AR parameters are estimated for each segment to be classified by means of support vector machine (SVM) classifier into crackles and normal lung sounds using a set of synthetic crackle waveforms that have been modeled to train the classifier. A dataset composed of simulated and real coarse and fine crackles sound signals was created with several signal-to-noise (SNR) ratios to evaluate the robustness of the proposed method. Each simulated and real signal was mixed with noise that shows the same spectral energy distribution as typically found in breath noise from a healthy subject. This study makes a significant contribution by achieving competitive results. The proposed method yields values ranging from 80% in the lowest signal-to-noise ratio scenario to a perfect 100% in the highest signal-to-noise ratio scenario. Notably, these results surpass those of other methods presented by a margin of at least 15%. The combination of an autoregressive (AR) model with a support vector machine (SVM) classifier offers an effective solution for detecting the presented events. This approach exhibits enhanced robustness against variations in the signal-to-noise ratio that the input signals may encounter. Full article
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12 pages, 7402 KiB  
Article
Real-Time Simulation of Wave Phenomena in Lung Ultrasound Imaging
by Kamil Szostek, Julia Lasek and Adam Piórkowski
Appl. Sci. 2023, 13(17), 9805; https://doi.org/10.3390/app13179805 - 30 Aug 2023
Viewed by 2049
Abstract
Medical simulations have proven to be highly valuable in the education of healthcare professionals. This significance was particularly evident during the COVID-19 pandemic, where simulators provided a safe and effective means of training healthcare practitioners in the principles of lung ultrasonography without exposing [...] Read more.
Medical simulations have proven to be highly valuable in the education of healthcare professionals. This significance was particularly evident during the COVID-19 pandemic, where simulators provided a safe and effective means of training healthcare practitioners in the principles of lung ultrasonography without exposing them to the risk of infection. This further emphasizes another important advantage of medical simulation in the field of medical education. This paper presents the principles of ultrasound simulation in the context of inflammatory lung conditions. The propagation of sound waves in this environment is discussed, with a specific focus on key diagnostic artifacts in lung imaging. The simulated medium was modeled by assigning appropriate acoustic characteristics to the tissue components present in the simulated study. A simulation engine was developed, taking into consideration the requirements of easy accessibility through a web browser and high-performance simulation through GPU-based computing. The obtained images were compared with real-world examples. An analysis of simulation parameter selection was conducted to achieve real-time simulations while maintaining excellent visual quality. The research findings demonstrate the feasibility of real-time, high-quality visualization in ultrasound simulation, providing valuable insights for the development of educational tools and diagnostic training in the field of medical imaging. Full article
(This article belongs to the Special Issue Image Processing and Computer Vision for Biomedical Applications)
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17 pages, 7543 KiB  
Article
Mannose-Functionalized Isoniazid-Loaded Nanostructured Lipid Carriers for Pulmonary Delivery: In Vitro Prospects and In Vivo Therapeutic Efficacy Assessment
by Shaveta Ahalwat, Dinesh Chandra Bhatt, Surbhi Rohilla, Vikas Jogpal, Kirti Sharma, Tarun Virmani, Girish Kumar, Abdulsalam Alhalmi, Ali S. Alqahtani, Omar M. Noman and Marwan Almoiliqy
Pharmaceuticals 2023, 16(8), 1108; https://doi.org/10.3390/ph16081108 - 4 Aug 2023
Cited by 21 | Viewed by 2767
Abstract
Resistance to isoniazid (INH) is common and increases the possibility of acquiring multidrug-resistant tuberculosis. For this study, isoniazid-loaded nanostructured lipid carriers (INH-NLCs) were developed and effectively functionalized with mannose (Man) to enhance the residence time of the drug within the lungs via specific [...] Read more.
Resistance to isoniazid (INH) is common and increases the possibility of acquiring multidrug-resistant tuberculosis. For this study, isoniazid-loaded nanostructured lipid carriers (INH-NLCs) were developed and effectively functionalized with mannose (Man) to enhance the residence time of the drug within the lungs via specific delivery and increase the therapeutic efficacy of the formulation. The mannose-functionalized isoniazid-loaded nanostructured lipid carrier (Man-INH-NLC) formulation was evaluated with respect to various formulation parameters, namely, encapsulation efficiency (EE), drug loading (DL), average particle size (PS), zeta potential (ZP), polydispersity index (PDI), in vitro drug release (DR), and release kinetics. The in vitro inhalation behavior of the developed formulation after nebulization was investigated using an Andersen cascade impactor via the estimation of the mass median aerosolized diameter (MMAD) and geometric aerodynamic diameter (GAD) and subsequently found to be suitable for effective lung delivery. An in vivo pharmacokinetic study was carried out in a guinea pig animal model, and it was demonstrated that Man-INH-NLC has a longer residence time in the lungs with improved pharmacokinetics when compared with unfunctionalized INH-NLC, indicating the enhanced therapeutic efficacy of the Man-INH-NLC formulation. Histopathological analysis led us to determine that the extent of tissue damage was more severe in the case of the pure drug solution of isoniazid compared to the Man-INH-NLC formulation after nebulization. Thus, the nebulization of Man-INH-NLC was found to be safe, forming a sound basis for enhancing the therapeutic efficacy of the drug for improved management in the treatment of pulmonary tuberculosis. Full article
(This article belongs to the Special Issue Current Insights on Lipid-Based Nanosystems 2023)
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