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1.
Sci Rep ; 12(1): 7249, 2022 May 04.
Article in English | MEDLINE | ID: covidwho-1821607

ABSTRACT

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.


Subject(s)
Ageusia , COVID-19 , Ageusia/epidemiology , Ageusia/etiology , Anosmia/epidemiology , Anosmia/etiology , COVID-19/complications , COVID-19/epidemiology , Cough/diagnosis , Dyspnea/etiology , Hospitalization , Humans , Middle Aged , Outpatients , Risk Factors , SARS-CoV-2
2.
J Int Med Res ; 50(5): 3000605221096280, 2022 May.
Article in English | MEDLINE | ID: covidwho-1820035

ABSTRACT

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.


Subject(s)
Ageusia , COVID-19 , Olfaction Disorders , Ageusia/diagnosis , Anosmia/diagnosis , COVID-19/complications , COVID-19/diagnosis , Cough/diagnosis , Emergency Service, Hospital , Fever/diagnosis , Humans , Olfaction Disorders/diagnosis , Prospective Studies , SARS-CoV-2 , Saudi Arabia/epidemiology , Taste Disorders/diagnosis
3.
PLoS One ; 16(11): e0260416, 2021.
Article in English | MEDLINE | ID: covidwho-1793553

ABSTRACT

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.


Subject(s)
Lung Diseases/diagnosis , Respiration Disorders/diagnosis , Adolescent , Adult , Asthma/diagnosis , Asthma/epidemiology , Cohort Studies , Cough/diagnosis , Cough/epidemiology , Dyspnea/epidemiology , Female , Forced Expiratory Volume , Humans , Lung Diseases/epidemiology , Lung Neoplasms/diagnosis , Lung Neoplasms/epidemiology , Male , Middle Aged , Norway/epidemiology , Pulmonary Disease, Chronic Obstructive/diagnosis , Pulmonary Disease, Chronic Obstructive/epidemiology , Respiration Disorders/epidemiology , Respiratory Sounds , Risk Factors , Young Adult
4.
Sensors (Basel) ; 22(8)2022 Apr 10.
Article in English | MEDLINE | ID: covidwho-1785900

ABSTRACT

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.


Subject(s)
COVID-19 , Crowdsourcing , COVID-19/diagnosis , COVID-19 Testing , Cough/diagnosis , Humans , Sound
5.
Comput Biol Med ; 145: 105405, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1748110

ABSTRACT

This research aims to analyze the performance of state-of-the-art machine learning techniques for classifying COVID-19 from cough sounds and to identify the model(s) that consistently perform well across different cough datasets. Different performance evaluation metrics (precision, sensitivity, specificity, AUC, accuracy, etc.) make selecting the best performance model difficult. To address this issue, in this paper, we propose an ensemble-based multi-criteria decision making (MCDM) method for selecting top performance machine learning technique(s) for COVID-19 cough classification. We use four cough datasets, namely Cambridge, Coswara, Virufy, and NoCoCoDa to verify the proposed method. At first, our proposed method uses the audio features of cough samples and then applies machine learning (ML) techniques to classify them as COVID-19 or non-COVID-19. Then, we consider a multi-criteria decision-making (MCDM) method that combines ensemble technologies (i.e., soft and hard) to select the best model. In MCDM, we use the technique for order preference by similarity to ideal solution (TOPSIS) for ranking purposes, while entropy is applied to calculate evaluation criteria weights. In addition, we apply the feature reduction process through recursive feature elimination with cross-validation under different estimators. The results of our empirical evaluations show that the proposed method outperforms the state-of-the-art models. We see that when the proposed method is used for analysis using the Extra-Trees classifier, it has achieved promising results (AUC: 0.95, Precision: 1, Recall: 0.97).


Subject(s)
COVID-19 , Algorithms , COVID-19/diagnosis , Cough/diagnosis , Humans , Machine Learning , Sound
6.
IEEE Trans Biomed Circuits Syst ; 16(1): 129-137, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1672882

ABSTRACT

Cough detection has aroused great interest because the assessment of cough frequency may improve diagnosis accuracy for dealing with several diseases, such as chronic obstructive pulmonary disease (COPD) and the recent COVID-19 global pandemic crisis. Here, we propose and experimentally demonstrate a wireless smart face mask based on a passive harmonic tag for real-time cough monitoring and alert. Our results show that the cough events can be successfully monitored through non-contact track of the received signal strength indicator (RSSI) at the harmonic frequency. Owing to the frequency orthogonality between the launched and backscattered radio-frequency (RF) signals, the harmonic tag-based smart mask can well suppress the electromagnetic interferences, such as clutters and crosstalks in noisy environments. We envision that this zero-power and lightweight wireless wearable device may be beneficial for cough monitoring and the public health condition in terms of tracking potential contagious person and virus-transmissive events.


Subject(s)
COVID-19 , Wearable Electronic Devices , COVID-19/diagnosis , Cough/diagnosis , Humans , Masks , Monitoring, Physiologic
7.
Comput Biol Med ; 141: 105153, 2022 02.
Article in English | MEDLINE | ID: covidwho-1588034

ABSTRACT

We present an experimental investigation into the effectiveness of transfer learning and bottleneck feature extraction in detecting COVID-19 from audio recordings of cough, breath and speech. This type of screening is non-contact, does not require specialist medical expertise or laboratory facilities and can be deployed on inexpensive consumer hardware such as a smartphone. We use datasets that contain cough, sneeze, speech and other noises, but do not contain COVID-19 labels, to pre-train three deep neural networks: a CNN, an LSTM and a Resnet50. These pre-trained networks are subsequently either fine-tuned using smaller datasets of coughing with COVID-19 labels in the process of transfer learning, or are used as bottleneck feature extractors. Results show that a Resnet50 classifier trained by this transfer learning process delivers optimal or near-optimal performance across all datasets achieving areas under the receiver operating characteristic (ROC AUC) of 0.98, 0.94 and 0.92 respectively for all three sound classes: coughs, breaths and speech. This indicates that coughs carry the strongest COVID-19 signature, followed by breath and speech. Our results also show that applying transfer learning and extracting bottleneck features using the larger datasets without COVID-19 labels led not only to improved performance, but also to a marked reduction in the standard deviation of the classifier AUCs measured over the outer folds during nested cross-validation, indicating better generalisation. We conclude that deep transfer learning and bottleneck feature extraction can improve COVID-19 cough, breath and speech audio classification, yielding automatic COVID-19 detection with a better and more consistent overall performance.


Subject(s)
COVID-19 , Cough/diagnosis , Humans , Machine Learning , SARS-CoV-2 , Speech
8.
PLoS One ; 16(3): e0247773, 2021.
Article in English | MEDLINE | ID: covidwho-1575465

ABSTRACT

BACKGROUND: The coronavirus infectious disease 19 (COVID-19) pandemic has resulted in significant morbidities, severe acute respiratory failures and subsequently emergency departments' (EDs) overcrowding in a context of insufficient laboratory testing capacities. The development of decision support tools for real-time clinical diagnosis of COVID-19 is of prime importance to assist patients' triage and allocate resources for patients at risk. METHODS AND PRINCIPAL FINDINGS: From March 2 to June 15, 2020, clinical patterns of COVID-19 suspected patients at admission to the EDs of Liège University Hospital, consisting in the recording of eleven symptoms (i.e. dyspnoea, chest pain, rhinorrhoea, sore throat, dry cough, wet cough, diarrhoea, headache, myalgia, fever and anosmia) plus age and gender, were investigated during the first COVID-19 pandemic wave. Indeed, 573 SARS-CoV-2 cases confirmed by qRT-PCR before mid-June 2020, and 1579 suspected cases that were subsequently determined to be qRT-PCR negative for the detection of SARS-CoV-2 were enrolled in this study. Using multivariate binary logistic regression, two most relevant symptoms of COVID-19 were identified in addition of the age of the patient, i.e. fever (odds ratio [OR] = 3.66; 95% CI: 2.97-4.50), dry cough (OR = 1.71; 95% CI: 1.39-2.12), and patients older than 56.5 y (OR = 2.07; 95% CI: 1.67-2.58). Two additional symptoms (chest pain and sore throat) appeared significantly less associated to the confirmed COVID-19 cases with the same OR = 0.73 (95% CI: 0.56-0.94). An overall pondered (by OR) score (OPS) was calculated using all significant predictors. A receiver operating characteristic (ROC) curve was generated and the area under the ROC curve was 0.71 (95% CI: 0.68-0.73) rendering the use of the OPS to discriminate COVID-19 confirmed and unconfirmed patients. The main predictors were confirmed using both sensitivity analysis and classification tree analysis. Interestingly, a significant negative correlation was observed between the OPS and the cycle threshold (Ct values) of the qRT-PCR. CONCLUSION AND MAIN SIGNIFICANCE: The proposed approach allows for the use of an interactive and adaptive clinical decision support tool. Using the clinical algorithm developed, a web-based user-interface was created to help nurses and clinicians from EDs with the triage of patients during the second COVID-19 wave.


Subject(s)
COVID-19 Testing , COVID-19/diagnosis , Decision Support Systems, Clinical , Adult , Aged , Cough/diagnosis , Dyspnea/diagnosis , Female , Fever/diagnosis , Headache/diagnosis , Hospitals , Humans , Male , Middle Aged , Pharyngitis/diagnosis , SARS-CoV-2/isolation & purification
9.
PLoS One ; 16(12): e0249980, 2021.
Article in English | MEDLINE | ID: covidwho-1571978

ABSTRACT

PURPOSE: To evaluate the diagnostic value of symptoms used by daycares and schools to screen children and adolescents for SARS-CoV-2 infection, we analyzed data from a primary care setting. METHODS: This cohort study included all patients ≤17 years old who were evaluated at Providence Community Health Centers (PCHC; Providence, U.S.), for COVID-19 symptoms and/or exposure, and received SARS-CoV-2 polymerase chain reaction (PCR) testing between March-June 2020. Participants were identified from PCHC electronic medical records. For three age groups- 0-4, 5-11, and 12-17 years-we estimated the sensitivity, specificity, and area under the receiver operating curve (AUC) of individual symptoms and three symptom combinations: a case definition published by the Rhode Island Department of Health (RIDOH), and two novel combinations generated by different statistical approaches to maximize sensitivity, specificity, and AUC. We evaluated symptom combinations both with and without consideration of COVID-19 exposure. Myalgia, headache, sore throat, abdominal pain, nausea, anosmia, and ageusia were not assessed in 0-4 year-olds due to the lower reliability of these symptoms in this group. RESULTS: Of 555 participants, 217 (39.1%) were SARS-CoV-2-infected. Fever was more common among 0-4 years-olds (p = 0.002); older children more frequently reported fatigue (p = 0.02). In children ≥5 years old, anosmia or ageusia had 94-98% specificity. In all ages, exposure history most accurately predicted infection. With respect to individual symptoms, cough most accurately predicted infection in <5 year-olds (AUC 0.69) and 12-17 year-olds (AUC 0.62), while headache was most accurate in 5-11 year-olds (AUC 0.62). In combination with exposure history, the novel symptom combinations generated statistically to maximize test characteristics had sensitivity >95% but specificity <30%. No symptom or symptom combination had AUC ≥0.70. CONCLUSIONS: Anosmia or ageusia in children ≥5 years old should raise providers' index of suspicion for COVID-19. However, our overall findings underscore the limited diagnostic value of symptoms.


Subject(s)
Ageusia/diagnosis , COVID-19/diagnosis , Cough/diagnosis , Headache/diagnosis , Myalgia/diagnosis , Pharyngitis/diagnosis , Adolescent , Age Distribution , Area Under Curve , Child , Child, Preschool , Cohort Studies , Community Health Centers , Diagnostic Tests, Routine , Electronic Health Records , Humans , Infant , Infant, Newborn , Primary Health Care
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2021: 2353-2357, 2021 11.
Article in English | MEDLINE | ID: covidwho-1566225

ABSTRACT

Since the COVID-19 pandemic began, research has shown promises in building COVID-19 screening tools using cough recordings as a convenient and inexpensive alternative to current testing techniques. In this paper, we present a novel and fully automated algorithm framework for cough extraction and COVID-19 detection using a combination of signal processing and machine learning techniques. It involves extracting cough episodes from audios of a diverse real-world noisy conditions and then screening for the COVID-19 infection based on the cough characteristics. The proposed algorithm was developed and evaluated using self-recorded cough audios collected from COVID-19 patients monitored by Biovitals® Sentinel remote patient management platform and publicly available datasets of various sound recordings. The proposed algorithm achieves a duration Area Under Receiver Operating Characteristic curve (AUROC) of 98.6% in the cough extraction task and a mean cross-validation AUROC of 98.1% in the COVID-19 classification task. These results demonstrate high accuracy and robustness of the proposed algorithm as a fast and easily accessible COVID-19 screening tool and its potential to be used for other cough analysis applications.


Subject(s)
COVID-19 , Cough/diagnosis , Humans , Monitoring, Physiologic , Pandemics , Research Design , SARS-CoV-2
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2021: 2252-2257, 2021 11.
Article in English | MEDLINE | ID: covidwho-1566196

ABSTRACT

Cough is one of the most common symptoms of COVID-19. It is easily recorded using a smartphone for further analysis. This makes it a great way to track and possibly identify patients with COVID. In this paper, we present a deep learning-based algorithm to identify whether a patient's audio recording contains a cough for subsequent COVID screening. More generally, cough identification is valuable for the remote monitoring and tracking of infections and chronic conditions. Our algorithm is validated on our novel dataset in which COVID-19 patients were instructed to volunteer natural coughs. The validation dataset consists of real patient cough and no cough audio. It was supplemented by files without cough from publicly available datasets that had cough-like sounds including: throat clearing, snoring, etc. Our algorithm had an area under receiver operating characteristic curve statistic of 0.977 on a validation set when making a cough/no cough determination. The specificity and sensitivity of the model on a reserved test set, at a threshold set by the validation set, was 0.845 and 0.976. This algorithm serves as a fundamental step in a larger cascading process to monitor, extract, and analyze COVID-19 patient coughs to detect the patient's health status, symptoms, and potential for deterioration.


Subject(s)
COVID-19 , Cough , Algorithms , Cough/diagnosis , Humans , Records , SARS-CoV-2
12.
Sensors (Basel) ; 21(23)2021 Dec 02.
Article in English | MEDLINE | ID: covidwho-1561072

ABSTRACT

Spirometer measurements can reflect cough strength but might not be routinely available for patients with severe neurological or medical conditions. A digital device that can record and help track abnormal cough sound changes serially in a noninvasive but reliable manner would be beneficial for monitoring such individuals. This report includes two cases of respiratory distress whose cough changes were monitored via assessments performed using recordings made with a digital device. The cough sounds were recorded using an iPad (Apple, Cupertino, CA, USA) through an embedded microphone. Cough sounds were recorded at the bedside, with no additional special equipment. The two patients were able to complete the recordings with no complications. The maximum root mean square values obtained from the cough sounds were significantly reduced when both cases were diagnosed with aspiration pneumonia. In contrast, higher values became apparent when the patients demonstrated a less severe status. Based on an analysis of our two cases, the patients' cough sounds recorded with a commercial digital device show promise as potential digital biomarkers that may reflect aspiration risk related to attenuated cough force. Serial monitoring aided the decision making to resume oral feeding. Future studies should further explore the clinical utility of this technique.


Subject(s)
Cough , Sound , Biomarkers , Cough/diagnosis , Diagnostic Tests, Routine , Humans , Spirometry
13.
14.
Sensors (Basel) ; 21(21)2021 Oct 23.
Article in English | MEDLINE | ID: covidwho-1512558

ABSTRACT

Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.


Subject(s)
Artificial Intelligence , Pneumonia , Adult , Algorithms , Cough/diagnosis , Humans , Pneumonia/diagnosis , Reproducibility of Results
16.
Comput Biol Med ; 138: 104944, 2021 11.
Article in English | MEDLINE | ID: covidwho-1466249

ABSTRACT

COVID-19 heavily affects breathing and voice and causes symptoms that make patients' voices distinctive, creating recognizable audio signatures. Initial studies have already suggested the potential of using voice as a screening solution. In this article we present a dataset of voice, cough and breathing audio recordings collected from individuals infected by SARS-CoV-2 virus, as well as non-infected subjects via large scale crowdsourced campaign. We describe preliminary results for detection of COVID-19 from cough patterns using standard acoustic features sets, wavelet scattering features and deep audio embeddings extracted from low-level feature representations (VGGish and OpenL3). Our models achieve accuracy of 88.52%, sensitivity of 88.75% and specificity of 90.87%, confirming the applicability of audio signatures to identify COVID-19 symptoms. We furthermore provide an in-depth analysis of the most informative acoustic features and try to elucidate the mechanisms that alter the acoustic characteristics of coughs of people with COVID-19.


Subject(s)
COVID-19 , Voice , Cough/diagnosis , Humans , Respiration , SARS-CoV-2
18.
PLoS Med ; 18(9): e1003777, 2021 09.
Article in English | MEDLINE | ID: covidwho-1440982

ABSTRACT

BACKGROUND: Rapid detection, isolation, and contact tracing of community COVID-19 cases are essential measures to limit the community spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to identify a parsimonious set of symptoms that jointly predict COVID-19 and investigated whether predictive symptoms differ between the B.1.1.7 (Alpha) lineage (predominating as of April 2021 in the US, UK, and elsewhere) and wild type. METHODS AND FINDINGS: We obtained throat and nose swabs with valid SARS-CoV-2 PCR test results from 1,147,370 volunteers aged 5 years and above (6,450 positive cases) in the REal-time Assessment of Community Transmission-1 (REACT-1) study. This study involved repeated community-based random surveys of prevalence in England (study rounds 2 to 8, June 2020 to January 2021, response rates 22%-27%). Participants were asked about symptoms occurring in the week prior to testing. Viral genome sequencing was carried out for PCR-positive samples with N-gene cycle threshold value < 34 (N = 1,079) in round 8 (January 2021). In univariate analysis, all 26 surveyed symptoms were associated with PCR positivity compared with non-symptomatic people. Stability selection (1,000 penalized logistic regression models with 50% subsampling) among people reporting at least 1 symptom identified 7 symptoms as jointly and positively predictive of PCR positivity in rounds 2-7 (June to December 2020): loss or change of sense of smell, loss or change of sense of taste, fever, new persistent cough, chills, appetite loss, and muscle aches. The resulting model (rounds 2-7) predicted PCR positivity in round 8 with area under the curve (AUC) of 0.77. The same 7 symptoms were selected as jointly predictive of B.1.1.7 infection in round 8, although when comparing B.1.1.7 with wild type, new persistent cough and sore throat were more predictive of B.1.1.7 infection while loss or change of sense of smell was more predictive of the wild type. The main limitations of our study are (i) potential participation bias despite random sampling of named individuals from the National Health Service register and weighting designed to achieve a representative sample of the population of England and (ii) the necessary reliance on self-reported symptoms, which may be prone to recall bias and may therefore lead to biased estimates of symptom prevalence in England. CONCLUSIONS: Where testing capacity is limited, it is important to use tests in the most efficient way possible. We identified a set of 7 symptoms that, when considered together, maximize detection of COVID-19 in the community, including infection with the B.1.1.7 lineage.


Subject(s)
COVID-19/complications , COVID-19/diagnosis , Models, Biological , Ageusia/diagnosis , Ageusia/etiology , Ageusia/virology , Anosmia/diagnosis , Anosmia/etiology , Anosmia/virology , Appetite , Area Under Curve , COVID-19/virology , Chills/diagnosis , Chills/etiology , Chills/virology , Communicable Disease Control , Cough/diagnosis , Cough/etiology , Cough/virology , England , False Positive Reactions , Female , Fever/diagnosis , Fever/etiology , Fever/virology , Humans , Male , Mass Screening , Myalgia/diagnosis , Myalgia/etiology , Myalgia/virology , Pharyngitis/diagnosis , Pharyngitis/etiology , Pharyngitis/virology , Polymerase Chain Reaction , SARS-CoV-2/genetics , State Medicine
19.
Sci Rep ; 11(1): 19149, 2021 09 27.
Article in English | MEDLINE | ID: covidwho-1440482

ABSTRACT

Recently deep learning has attained a breakthrough in model accuracy for the classification of images due mainly to convolutional neural networks. In the present study, we attempted to investigate the presence of subclinical voice feature alteration in COVID-19 patients after the recent resolution of disease using deep learning. The study was a prospective study of 76 post COVID-19 patients and 40 healthy individuals. The diagnoses of post COVID-19 patients were based on more than the eighth week after onset of symptoms. Voice samples of an 'ah' sound, coughing sound and a polysyllabic sentence were collected and preprocessed to log-mel spectrogram. Transfer learning using the VGG19 pre-trained convolutional neural network was performed with all voice samples. The performance of the model using the polysyllabic sentence yielded the highest classification performance of all models. The coughing sound produced the lowest classification performance while the ability of the monosyllabic 'ah' sound to predict the recent COVID-19 fell between the other two vocalizations. The model using the polysyllabic sentence achieved 85% accuracy, 89% sensitivity, and 77% specificity. In conclusion, deep learning is able to detect the subtle change in voice features of COVID-19 patients after recent resolution of the disease.


Subject(s)
COVID-19/diagnosis , Cough/diagnosis , Deep Learning , Neural Networks, Computer , Sound , Voice/physiology , Adult , COVID-19/physiopathology , COVID-19/virology , Cough/physiopathology , Female , Humans , Logistic Models , Male , Multivariate Analysis , Prospective Studies , SARS-CoV-2/physiology , Sensitivity and Specificity , Voice Disorders/diagnosis , Voice Disorders/physiopathology
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