COVID-19 Detection from Cough Recordings Using Bag-of-Words Classifiers.

Reliable detection of COVID-19 from cough recordings is evaluated using bag-of-words classifiers. The effect of using four distinct feature extraction procedures and four different encoding strategies is evaluated in terms of the Area Under Curve (AUC), accuracy, sensitivity, and F1-score. Additional studies include assessing the effect of both input and output fusion approaches and a comparative analysis against 2D solutions using Convolutional Neural Networks. Extensive experiments conducted on the COUGHVID and COVID-19 Sounds datasets indicate that sparse encoding yields the best performances, showing robustness against various combinations of feature type, encoding strategy, and codebook dimension parameters.

Yardstick for managing cough, part 2: In children.

Nationwide statistics in the United States and Australia reveal that cough of undifferentiated duration is the most common complaint for which patients of all ages seek medical care in the ambulatory setting. Management of chronic cough is one of the most common reasons for new patient visits to respiratory specialists. Because symptomatic cough is such a common problem and so much has been learned about how to diagnose and treat cough of all durations but especially chronic cough, this 2-part yardstick has been written to review in a practical way the evidence-based guidelines most of which have been developed from high-quality systematic reviews on how best to manage cough of all durations in adults, adolescents, and children. Chronic cough in children is often benign and self-limiting. Using established and validated protocols and specific pointers (clues in history, findings on examination) can aid the clinician in identifying causes when present and improve outcomes. In this manuscript, part 2 of the 2-part series, we provide evidence-based, expert opinion recommendations on the management of chronic cough in the pediatric patient (<14 years of age).

Cough Assessment and Management in Pulmonary Rehabilitation- A Canadian Survey.

Pulmonary rehabilitation is a cornerstone intervention for controlling respiratory symptoms in people with chronic respiratory diseases. Chronic cough affects up to 90% of people with chronic respiratory diseases, however, it is currently unknown whether chronic cough is assessed and/or managed in pulmonary rehabilitation. This study aimed to determine if and how chronic cough is assessed and managed in pulmonary rehabilitation. This was a cross-sectional study. Pulmonary rehabilitation programs in Canada were identified via online websites. A representative from each program was invited to complete an online survey including the following topics: program demographics, assessment and management practices, and barriers and facilitators. Of 133 programs contacted, 31 returned a completed survey (23% response rate). Approximately half (52%) of respondents reported enrolling patients with chronic cough. Of those, 45% reported assessing and 62% reported intervening in chronic cough. Inadequate knowledge of assessment and management techniques was commonly identified to be a barrier and increased education was suggested as a possible facilitator. Based on pulmonary rehabilitation programs that responded to our survey, chronic cough is a prevalent symptom; however, it is scarcely assessed and managed. A need for structured education and the use of standardised strategies were reported as facilitators to the assessment and management of chronic cough in pulmonary rehabilitation.

Development and Validation of a Respiratory-Responsive Vocal Biomarker-Based Tool for Generalizable Detection of Respiratory Impairment: Independent Case-Control Studies in Multiple Respiratory Conditions Including Asthma, Chronic Obstructive Pulmonary Disease, and COVID-19.

BACKGROUND: Vocal biomarker-based machine learning approaches have shown promising results in the detection of various health conditions, including respiratory diseases, such as asthma. OBJECTIVE: This study aimed to determine whether a respiratory-responsive vocal biomarker (RRVB) model platform initially trained on an asthma and healthy volunteer (HV) data set can differentiate patients with active COVID-19 infection from asymptomatic HVs by assessing its sensitivity, specificity, and odds ratio (OR). METHODS: A logistic regression model using a weighted sum of voice acoustic features was previously trained and validated on a data set of approximately 1700 patients with a confirmed asthma diagnosis and a similar number of healthy controls. The same model has shown generalizability to patients with chronic obstructive pulmonary disease, interstitial lung disease, and cough. In this study, 497 participants (female: n=268, 53.9%; <65 years old: n=467, 94%; Marathi speakers: n=253, 50.9%; English speakers: n=223, 44.9%; Spanish speakers: n=25, 5%) were enrolled across 4 clinical sites in the United States and India and provided voice samples and symptom reports on their personal smartphones. The participants included patients who are symptomatic COVID-19 positive and negative as well as asymptomatic HVs. The RRVB model performance was assessed by comparing it with the clinical diagnosis of COVID-19 confirmed by reverse transcriptase-polymerase chain reaction. RESULTS: The ability of the RRVB model to differentiate patients with respiratory conditions from healthy controls was previously demonstrated on validation data in asthma, chronic obstructive pulmonary disease, interstitial lung disease, and cough, with ORs of 4.3, 9.1, 3.1, and 3.9, respectively. The same RRVB model in this study in COVID-19 performed with a sensitivity of 73.2%, specificity of 62.9%, and OR of 4.64 (P<.001). Patients who experienced respiratory symptoms were detected more frequently than those who did not experience respiratory symptoms and completely asymptomatic patients (sensitivity: 78.4% vs 67.4% vs 68%, respectively). CONCLUSIONS: The RRVB model has shown good generalizability across respiratory conditions, geographies, and languages. Results using data set of patients with COVID-19 demonstrate its meaningful potential to serve as a prescreening tool for identifying individuals at risk for COVID-19 infection in combination with temperature and symptom reports. Although not a COVID-19 test, these results suggest that the RRVB model can encourage targeted testing. Moreover, the generalizability of this model for detecting respiratory symptoms across different linguistic and geographic contexts suggests a potential path for the development and validation of voice-based tools for broader disease surveillance and monitoring applications in the future.

[Expert consensus on diagnosis and treatment of COVID-19 infection related cough in children].

An epidemic outbreak of the corona virus disease 2019(COVID-19) Omicron variant occurred in most regions of China. Children are susceptible to COVID-19 and the vast majority of them suffer from upper respiratory tract infection. Cough is one of the most common symptoms. COVID-19 infection related cough includes acute cough, persistent cough and chronic cough, and children with original chronic cough or chronic lung disease can also induce or aggravate symptom of cough after infection, which has a great impact on children's physical and mental health. The treatment for COVID-19 infection related cough vary with the etiology. Improper treatment would delay the patient's condition and increase adverse drug reaction. Currently, there is no guideline or consensus on the diagnosis and treatment of COVID-19 infection related cough in children in China, therefore this consensus is drafted. Referring to the latest international research and the diagnostic and therapeutic strategy for COVID-19 infection (Tenth Edition For Trial Implementation), and combining with clinical diagnosis and treatment experience,the consensus elaborates the pathogenesis and etiology of COVID-19 infection related cough, the use of cough relievers and expectorants, as well as the key points of diagnosis and treatment of different etiological factors. It is expected to provide specific and feasible guidance scheme for pediatricians, general practitioners and clinical pharmacists.

MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds.

Coronavirus has an impact on millions of lives and has been added to the important pandemics that continue to affect with its variants. Since it is transmitted through the respiratory tract, it has had significant effects on public health and social relations. Isolating people who are COVID positive can minimize the transmission, therefore several exams are proposed to detect the virus such as reverse transcription-polymerase chain reaction (RT-PCR), chest X-Ray, and computed tomography (CT). However, these methods suffer from either a low detection rate or high radiation dosage, along with being expensive. In this study, deep neural network-based model capable of detecting coronavirus from only coughing sound, which is fast, remotely operable and has no harmful side effects, has been proposed. The proposed multi-branch model takes M el Frequency Cepstral Coefficients (MFCC), S pectrogram, and C hromagram as inputs and is abbreviated as MSCCov19Net. The system is trained on publicly available crowdsourced datasets, and tested on two unseen (used only for testing) clinical and non-clinical datasets. Experimental outcomes represent that the proposed system outperforms the 6 popular deep learning architectures on four datasets by representing a better generalization ability. The proposed system has reached an accuracy of 61.5 % in Virufy and 90.4 % in NoCoCoDa for unseen test datasets.

Reliability of crowdsourced data and patient-reported outcome measures in cough-based COVID-19 screening.

Mass community testing is a critical means for monitoring the spread of the COVID-19 pandemic. Polymerase chain reaction (PCR) is the gold standard for detecting the causative coronavirus 2 (SARS-CoV-2) but the test is invasive, test centers may not be readily available, and the wait for laboratory results can take several days. Various machine learning based alternatives to PCR screening for SARS-CoV-2 have been proposed, including cough sound analysis. Cough classification models appear to be a robust means to predict infective status, but collecting reliable PCR confirmed data for their development is challenging and recent work using unverified crowdsourced data is seen as a viable alternative. In this study, we report experiments that assess cough classification models trained (i) using data from PCR-confirmed COVID subjects and (ii) using data of individuals self-reporting their infective status. We compare performance using PCR-confirmed data. Models trained on PCR-confirmed data perform better than those trained on patient-reported data. Models using PCR-confirmed data also exploit more stable predictive features and converge faster. Crowd-sourced cough data is less reliable than PCR-confirmed data for developing predictive models for COVID-19, and raises concerns about the utility of patient reported outcome data in developing other clinical predictive models when better gold-standard data are available.

Guidelines for Triage of COVID-19 Patients Presenting With Multisystemic Symptoms.

BACKGROUND AND OBJECTIVES: This article describes how multisystemic symptoms, both respiratory and nonrespiratory, can be used to differentiate coronavirus disease-2019 (COVID-19) from other diseases at the point of patient triage in the community. The article also shows how combinations of symptoms could be used to predict the probability of a patient having COVID-19. METHODS: We first used a scoping literature review to identify symptoms of COVID-19 reported during the first year of the global pandemic. We then surveyed individuals with reported symptoms and recent reverse transcription polymerase chain reaction (RT-PCR) test results to assess the accuracy of diagnosing COVID-19 from reported symptoms. The scoping literature review, which included 81 scientific articles published by February 2021, identified 7 respiratory, 9 neurological, 4 gastrointestinal, 4 inflammatory, and 5 general symptoms associated with COVID-19 diagnosis. The likelihood ratio associated with each symptom was estimated from sensitivity and specificity of symptoms reported in the literature. A total of 483 individuals were then surveyed to validate the accuracy of predicting COVID-19 diagnosis based on patient symptoms using the likelihood ratios calculated from the literature review. Survey results were weighted to reflect age, gender, and race of the US population. The accuracy of predicting COVID-19 diagnosis from patient-reported symptoms was assessed using area under the receiver operating curve (AROC). RESULTS: In the community, cough, sore throat, runny nose, dyspnea, and hypoxia, by themselves, were not good predictors of COVID-19 diagnosis. A combination of cough and fever was also a poor predictor of COVID-19 diagnosis (AROC = 0.56). The accuracy of diagnosing COVID-19 based on symptoms was highest when individuals presented with symptoms from different body systems (AROC of 0.74-0.81); the lowest accuracy was when individuals presented with only respiratory symptoms (AROC = 0.48). CONCLUSIONS: There are no simple rules that clinicians can use to diagnose COVID-19 in the community when diagnostic tests are unavailable or untimely. However, triage of patients to appropriate care and treatment can be improved by reviewing the combinations of certain types of symptoms across body systems.

Literature Highlights.

Literature Highlights is a digest of notable papers recently published in the leading respiratory journals, allowing our readers to stay up-to-date with research advances. This month we include coverage on use of monoclonal antibodies for prevention of COVID-19, acoustic epidemiology and cough assessment; immunotherapeutic interventions for viral and bacterial infections; the potential for harmful use of dexamethasone in COVID-19 patients; diagnostic accuracy of a new finger stick blood test for TB; Clinical standards for pulmonary TB.

UFRC: A Unified Framework for Reliable COVID-19 Detection on Crowdsourced Cough Audio.

We suggested a unified system with core components of data augmentation, ImageNet-pretrained ResNet-50, cost-sensitive loss, deep ensemble learning, and uncertainty estimation to quickly and consistently detect COVID-19 using acoustic evidence. To increase the model's capacity to identify a minority class, data augmentation and cost-sensitive loss are incorporated (infected samples). In the COVID-19 detection challenge, ImageNet-pretrained ResNet-50 has been found to be effective. The unified framework also integrates deep ensemble learning and uncertainty estimation to integrate predictions from various base classifiers for generalisation and reliability. We ran a series of tests using the DiCOVA2021 challenge dataset to assess the efficacy of our proposed method, and the results show that our method has an AUC-ROC of 85.43 percent, making it a promising method for COVID-19 detection. The unified framework also demonstrates that audio may be used to quickly diagnose different respiratory disorders.

CoughLIME: Sonified Explanations for the Predictions of COVID-19 Cough Classifiers.

Since the emergence of the COVID-19 pandemic, various methods to detect the illness from cough and speech audio data have been proposed. While many of them deliver promising results, they lack transparency in the form of expla-nations which is crucial for establishing trust in the classifiers. We propose CoughLIME which extends LIME to explanations for audio data, specifically tailored towards cough data. We show that CoughLIME is capable of generating faithful sonified explanations for COVID-19 detection. To quantify the performance of the explanations generated for the CIdeR model, we adopt pixel flipping to audio and introduce a novel metric to assess the performance of the XAI classifier. CoughLIME achieves a ΔAUC of 19.48 % generating explanations for CIdeR's predictions.

A Cough-based deep learning framework for detecting COVID-19.

This paper presents a deep learning framework for detecting COVID-19 positive subjects from their cough sounds. In particular, the proposed approach comprises two main steps. In the first step, we generate a feature representing the cough sound by combining an embedding extracted from a pre-trained model and handcrafted features extracted from draw audio recording, referred to as the front-end feature extraction. Then, the combined features are fed into different back-end classification models for detecting COVID-19 positive subjects in the second step. Our experiments on the Track-2 dataset of the Second 2021 DiCOVA Challenge achieved the second top ranking with an AUC score of 81.21 and the top F1 score of 53.21 on a Blind Test set, improving the challenge baseline by 8.43% and 23.4% respectively and showing deployability, robustness and competitiveness with the state-of-the-art systems.

Cough hypersensitivity and chronic cough.

Chronic cough is globally prevalent across all age groups. This disorder is challenging to treat because many pulmonary and extrapulmonary conditions can present with chronic cough, and cough can also be present without any identifiable underlying cause or be refractory to therapies that improve associated conditions. Most patients with chronic cough have cough hypersensitivity, which is characterized by increased neural responsivity to a range of stimuli that affect the airways and lungs, and other tissues innervated by common nerve supplies. Cough hypersensitivity presents as excessive coughing often in response to relatively innocuous stimuli, causing significant psychophysical morbidity and affecting patients' quality of life. Understanding of the mechanisms that contribute to cough hypersensitivity and excessive coughing in different patient populations and across the lifespan is advancing and has contributed to the development of new therapies for chronic cough in adults. Owing to differences in the pathology, the organs involved and individual patient factors, treatment of chronic cough is progressing towards a personalized approach, and, in the future, novel ways to endotype patients with cough may prove valuable in management.

Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation.

BACKGROUND: Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection and detect the infection, given the current audio sample, but do not monitor disease progression in COVID-19. Limited exploration has been put forward to continuously monitor COVID-19 progression, especially recovery, through longitudinal audio data. Tracking disease progression characteristics and patterns of recovery could bring insights and lead to more timely treatment or treatment adjustment, as well as better resource management in health care systems. OBJECTIVE: The primary objective of this study is to explore the potential of longitudinal audio samples over time for COVID-19 progression prediction and, especially, recovery trend prediction using sequential deep learning techniques. METHODS: Crowdsourced respiratory audio data, including breathing, cough, and voice samples, from 212 individuals over 5-385 days were analyzed, alongside their self-reported COVID-19 test results. We developed and validated a deep learning-enabled tracking tool using gated recurrent units (GRUs) to detect COVID-19 progression by exploring the audio dynamics of the individuals' historical audio biomarkers. The investigation comprised 2 parts: (1) COVID-19 detection in terms of positive and negative (healthy) tests using sequential audio signals, which was primarily assessed in terms of the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with 95% CIs, and (2) longitudinal disease progression prediction over time in terms of probability of positive tests, which was evaluated using the correlation between the predicted probability trajectory and self-reported labels. RESULTS: We first explored the benefits of capturing longitudinal dynamics of audio biomarkers for COVID-19 detection. The strong performance, yielding an AUROC of 0.79, a sensitivity of 0.75, and a specificity of 0.71 supported the effectiveness of the approach compared to methods that do not leverage longitudinal dynamics. We further examined the predicted disease progression trajectory, which displayed high consistency with longitudinal test results with a correlation of 0.75 in the test cohort and 0.86 in a subset of the test cohort with 12 (57.1%) of 21 COVID-19-positive participants who reported disease recovery. Our findings suggest that monitoring COVID-19 evolution via longitudinal audio data has potential in the tracking of individuals' disease progression and recovery. CONCLUSIONS: An audio-based COVID-19 progression monitoring system was developed using deep learning techniques, with strong performance showing high consistency between the predicted trajectory and the test results over time, especially for recovery trend predictions. This has good potential in the postpeak and postpandemic era that can help guide medical treatment and optimize hospital resource allocations. The changes in longitudinal audio samples, referred to as audio dynamics, are associated with COVID-19 progression; thus, modeling the audio dynamics can potentially capture the underlying disease progression process and further aid COVID-19 progression prediction. This framework provides a flexible, affordable, and timely tool for COVID-19 tracking, and more importantly, it also provides a proof of concept of how telemonitoring could be applicable to respiratory diseases monitoring, in general.

Course of disease and risk factors for hospitalization in outpatients with a SARS-CoV-2 infection.

We analyzed symptoms and comorbidities as predictors of hospitalization in 710 outpatients in North-East Germany with PCR-confirmed SARS-CoV-2 infection. During the first 3 days of infection, commonly reported symptoms were fatigue (71.8%), arthralgia/myalgia (56.8%), headache (55.1%), and dry cough (51.8%). Loss of smell (anosmia), loss of taste (ageusia), dyspnea, and productive cough were reported with an onset of 4 days. Anosmia or ageusia were reported by only 18% of the participants at day one, but up to 49% between days 7 and 9. Not all participants who reported ageusia also reported anosmia. Individuals suffering from ageusia without anosmia were at highest risk of hospitalization (OR 6.8, 95% CI 2.5-18.1). They also experienced more commonly dyspnea and nausea (OR of 3.0, 2.9, respectively) suggesting pathophysiological connections between these symptoms. Other symptoms significantly associated with increased risk of hospitalization were dyspnea, vomiting, and fever. Among basic parameters and comorbidities, age > 60 years, COPD, prior stroke, diabetes, kidney and cardiac diseases were also associated with increased risk of hospitalization. In conclusion, due to the delayed onset, ageusia and anosmia may be of limited use in differential diagnosis of SARS-CoV-2. However, differentiation between ageusia and anosmia may be useful for evaluating risk for hospitalization.

Towards Multimodal Equipment to Help in the Diagnosis of COVID-19 Using Machine Learning Algorithms.

COVID-19 occurs due to infection through respiratory droplets containing the SARS-CoV-2 virus, which are released when someone sneezes, coughs, or talks. The gold-standard exam to detect the virus is Real-Time Polymerase Chain Reaction (RT-PCR); however, this is an expensive test and may require up to 3 days after infection for a reliable result, and if there is high demand, the labs could be overwhelmed, which can cause significant delays in providing results. Biomedical data (oxygen saturation level-SpO2, body temperature, heart rate, and cough) are acquired from individuals and are used to help infer infection by COVID-19, using machine learning algorithms. The goal of this study is to introduce the Integrated Portable Medical Assistant (IPMA), which is a multimodal piece of equipment that can collect biomedical data, such as oxygen saturation level, body temperature, heart rate, and cough sound, and helps infer the diagnosis of COVID-19 through machine learning algorithms. The IPMA has the capacity to store the biomedical data for continuous studies and can be used to infer other respiratory diseases. Quadratic kernel-free non-linear Support Vector Machine (QSVM) and Decision Tree (DT) were applied on three datasets with data of cough, speech, body temperature, heart rate, and SpO2, obtaining an Accuracy rate (ACC) and Area Under the Curve (AUC) of approximately up to 88.0% and 0.85, respectively, as well as an ACC up to 99% and AUC = 0.94, respectively, for COVID-19 infection inference. When applied to the data acquired with the IMPA, these algorithms achieved 100% accuracy. Regarding the easiness of using the equipment, 36 volunteers reported that the IPMA has a high usability, according to results from two metrics used for evaluation: System Usability Scale (SUS) and Post Study System Usability Questionnaire (PSSUQ), with scores of 85.5 and 1.41, respectively. In light of the worldwide needs for smart equipment to help fight the COVID-19 pandemic, this new equipment may help with the screening of COVID-19 through data collected from biomedical signals and cough sounds, as well as the use of machine learning algorithms.

A 31-Year-Old Man With Fever, Atypical Chest Pain, and Mediastinal Mass.

CASE PRESENTATION: A 31-year-old Asian male never-smoker living in the upper Midwest with a past medical history of congenital bilateral hearing loss sought treatment with a 1-week history of fever, fatigue, right-sided pleuritic chest pain, shortness of breath, productive cough with mild intermittent hemoptysis, night sweats, and unintentional 10-lb weight loss over 4 weeks. He was adopted from South Korea as an infant, and thus the family history was unknown. He worked in the heating, ventilation, and air conditioning business, performing installations and repairs. There was no known exposure to animals, caves, rivers, lakes, or wooded areas. He travelled to South Korea and New Hampshire approximately 9 months previously. He did not take any medication.

Testing for anosmia and ageusia in patients presenting to the emergency department with suspected coronavirus disease 2019 in Saudi Arabia.

OBJECTIVE: This study investigated the role of objective olfactory dysfunction (OD) and gustatory dysfunction (GD) testing among patients with suspected coronavirus disease 2019 (COVID-19) who presented with respiratory symptoms. METHODS: A prospective, blinded, observational study was conducted in the emergency units of two tertiary hospitals. Participants were asked to identify scents in the pocket smell test (PST) and flavors in four different solutions in the gustatory dysfunction test (GDT). We assessed the level of agreement between objective findings and self-reported symptoms. We evaluated the diagnostic accuracy of chemosensory dysfunction for diagnosing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. RESULTS: Of 250 participants, 74 (29.6%) were SARS-CoV-2-positive. There was slight agreement between self-reported symptoms and objective findings (kappa = 0.13 and 0.10 for OD and GD, respectively). OD assessed by the PST was independently associated with COVID-19 (adjusted odds ratio = 1.89, 95% confidence interval, 1.04-3.46). This association was stronger when OD was combined with objective GD, cough, and fever (adjusted odds ratio = 7.33, 95% confidence interval, 1.17-45.84). CONCLUSIONS: Neither the PST nor GDT alone are useful screening tools for COVID-19. However, a diagnostic scale based on objective OD, GD, fever, and cough may help triage patients with suspected COVID-19.

Respiratory symptoms and respiratory deaths: A multi-cohort study with 45 years observation time.

This study determined the association between respiratory symptoms and death from respiratory causes over a period of 45 years. In four cohorts of random samples of Norwegian populations with 103,881 participants, 43,731 persons had died per 31 December 2016. In total, 5,949 (14%) had died from respiratory diseases; 2,442 (41%) from lung cancer, 1,717 (29%) chronic obstructive pulmonary disease (COPD), 1,348 (23%) pneumonia, 119 (2%) asthma, 147 (2%) interstitial lung disease and 176 (3%) other pulmonary diseases. Compared with persons without respiratory symptoms the multivariable adjusted hazard ratio (HR) for lung cancer deaths increased with score of breathlessness on effort and cough and phlegm, being 2.6 (95% CI 2.1-3.2) for breathlessness score 3 and 2.1 (95% CI 1.7-2.5) for cough and phlegm score 5. The HR of COPD death was 6.4 (95% CI 5.4-7.7) for breathlessness score 3 and 3.0 (2.4-3.6) for cough and phlegm score 5. Attacks of breathlessness and wheeze score 2 had a HR of 1.6 (1.4-1.9) for COPD death. The risk of pneumonia deaths increased also with higher breathlessness on effort score, but not with higher cough and phlegm score, except for score 2 with HR 1.5 (1.2-1.8). In this study with >2.4 million person-years at risk, a positive association was observed between scores of respiratory symptoms and deaths due to COPD and lung cancer. Respiratory symptoms are thus important risk factors, which should be followed thoroughly by health care practitioners for the benefit of public health.

Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review.

Cough is a very common symptom and the most frequent reason for seeking medical advice. Optimized care goes inevitably through an adapted recording of this symptom and automatic processing. This study provides an updated exhaustive quantitative review of the field of cough sound acquisition, automatic detection in longer audio sequences and automatic classification of the nature or disease. Related studies were analyzed and metrics extracted and processed to create a quantitative characterization of the state-of-the-art and trends. A list of objective criteria was established to select a subset of the most complete detection studies in the perspective of deployment in clinical practice. One hundred and forty-four studies were short-listed, and a picture of the state-of-the-art technology is drawn. The trend shows an increasing number of classification studies, an increase of the dataset size, in part from crowdsourcing, a rapid increase of COVID-19 studies, the prevalence of smartphones and wearable sensors for the acquisition, and a rapid expansion of deep learning. Finally, a subset of 12 detection studies is identified as the most complete ones. An unequaled quantitative overview is presented. The field shows a remarkable dynamic, boosted by the research on COVID-19 diagnosis, and a perfect adaptation to mobile health.