Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds.

COVID-19 and heart failure (HF) are common disorders and although they share some similar symptoms, they require different treatments. Accurate diagnosis of these disorders is crucial for disease management, including patient isolation to curb infection spread of COVID-19. In this work, we aim to develop a computer-aided diagnostic system that can accurately differentiate these three classes (normal, COVID-19 and HF) using cough sounds. A novel handcrafted model was used to classify COVID-19 vs. healthy (Case 1), HF vs. healthy (Case 2) and COVID-19 vs. HF vs. healthy (Case 3) automatically using deoxyribonucleic acid (DNA) patterns. The model was developed using the cough sounds collected from 241 COVID-19 patients, 244 HF patients, and 247 healthy subjects using a hand phone. To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals. Our proposed model comprises a graph-based local feature generator (DNA pattern), an iterative maximum relevance minimum redundancy (ImRMR) iterative feature selector, with classification using the k-nearest neighbor classifier. Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively. The developed system is completely automated and economical, and can be utilized to accurately detect COVID-19 versus HF using cough sounds

Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19This work was supported by a 2021 Incheon National University Research Grant. This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A4A4079299)S

An investigation into the mechanisms driving spontaneous cough in a preclinical model of idiopathic pulmonary fibrosis

Idiopathic Pulmonary Fibrosis (IPF) is a terminal lung disease characterised by a progressive deposition of scar tissue within the lung interstitium. The disease is made more troubling for patients by being frequently accompanied by a chronic, sputumless cough, which has a severe impact on their quality of life. The pathophysiology behind cough in IPF remains poorly understood, and as such, it has proven refractory to treatment; the aim of this thesis was to elucidate the mechanisms behind this distressing symptom. We sought to model IPF-associated chronic cough using a bleomycin-driven model of fibrosis in guinea pigs. After having characterised the inflammatory and fibrotic features of this model, we discovered, in a preclinical first, that bleomycin-treated guinea pigs cough spontaneously, much like IPF patients. We believe that this model more closely recapitulates the cough seen in patients than other provoked cough challenge models. In search for the mediators that were driving cough within this preclinical model, we quantified the levels of different biomarkers in bronchoalveolar lavage fluid (BALF) that are reportedly elevated in the IPF lung: extracellular ATP, mast cell tryptase and 8-isoprostane (a biomarker for oxidative stress). We found evidence that all three of these were more highly present in the lungs of bleomycin-treated animals than the vehicle control group. Further to this, we measured these biomarkers in BALF donated by IPF patients at Royal Brompton Hospital and found that 8- isoprostane was significantly more concentrated in IPF BALF than in healthy volunteer BALF. We then utilised a variety of in vitro and in vivo techniques to further investigate how these lung milieus might elicit cough. After showing that H2O2, a reactive oxygen species, was capable of generating oxidative stress in guinea pig vagal ganglia, we further determined that it caused vagus nerve depolarisation through the ion channel TRPA1. We also discovered that activating the PAR2 receptor, a target of mast cell tryptase, also caused airway sensory nerve activation through the ion channel TRPV4; TRPV4 activation has previously been shown to cause cough via the extracellular release of ATP. Finally, we report on the efficacy of GSK2798745, a selective TRPV4 channel antagonist, that is capable of abrogating TRPV4-induced cough. In the near future, we intend to use a variety of pharmacological tools, including this one, in our novel in vivo model of fibrosis-induced spontaneous cough, with the end goal of finding an effective antitussive therapy for IPF patients.Open Acces

Challenges and Advances in Tuberculosis and Mycobacterial Lung Diseases

The Special Issue of the journal Diagnostics entitled “Challenges and Advances in Tuberculosis and Mycobacterial Lung Diseases” encompasses research articles, case presentations, and literature reviews concerning the epidemiology, medical surveillance, as well as diagnostic and therapeutic challenges in tuberculosis (TB) and in the infections caused by non-tuberculous mycobacteria (NTM ). The articles present the current status of knowledge concerning the use of molecular tests and genotyping of M. tuberculosis for the rapid identification of MDR and XDR clones, the advances in the identification of NTM and their differentiation to the species level, as well as clinical studies performed in TB and NTM risk groups. In addition, a summary of current recommendations concerning diagnosis and treatment of tuberculous pericarditis is presented

2022 - The Third Annual Fall Symposium of Student Scholars

The full program book from the Fall 2022 Symposium of Student Scholars, held on November 17, 2022. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1026/thumbnail.jp