Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence.

Research question: Can smartphones be used to detect individual and population-level changes in cough frequency that correlate with the incidence of coronavirus disease 2019 (COVID-19) and other respiratory infections? Methods: This was a prospective cohort study carried out in Pamplona (Spain) between 2020 and 2021 using artificial intelligence cough detection software. Changes in cough frequency around the time of medical consultation were evaluated using a randomisation routine; significance was tested by comparing the distribution of cough frequencies to that obtained from a model of no difference. The correlation between changes of cough frequency and COVID-19 incidence was studied using an autoregressive moving average analysis, and its strength determined by calculating its autocorrelation function (ACF). Predictors for the regular use of the system were studied using a linear regression. Overall user experience was evaluated using a satisfaction questionnaire and through focused group discussions. Results: We followed-up 616 participants and collected >62 000 coughs. Coughs per hour surged around the time cohort subjects sought medical care (difference +0.77 coughs·h-1; p=0.00001). There was a weak temporal correlation between aggregated coughs and the incidence of COVID-19 in the local population (ACF 0.43). Technical issues affected uptake and regular use of the system. Interpretation: Artificial intelligence systems can detect changes in cough frequency that temporarily correlate with the onset of clinical disease at the individual level. A clearer correlation with population-level COVID-19 incidence, or other respiratory conditions, could be achieved with better penetration and compliance with cough monitoring.

Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study.

INTRODUCTION: Cough is a common symptom of COVID-19 and other respiratory illnesses. However, objectively measuring its frequency and evolution is hindered by the lack of reliable and scalable monitoring systems. This can be overcome by newly developed artificial intelligence models that exploit the portability of smartphones. In the context of the ongoing COVID-19 pandemic, cough detection for respiratory disease syndromic surveillance represents a simple means for early outbreak detection and disease surveillance. In this protocol, we evaluate the ability of population-based digital cough surveillance to predict the incidence of respiratory diseases at population level in Navarra, Spain, while assessing individual determinants of uptake of these platforms. METHODS AND ANALYSIS: Participants in the Cendea de Cizur, Zizur Mayor or attending the local University of Navarra (Pamplona) will be invited to monitor their night-time cough using the smartphone app Hyfe Cough Tracker. Detected coughs will be aggregated in time and space. Incidence of COVID-19 and other diagnosed respiratory diseases within the participants cohort, and the study area and population will be collected from local health facilities and used to carry out an autoregressive moving average analysis on those independent time series. In a mixed-methods design, we will explore barriers and facilitators of continuous digital cough monitoring by evaluating participation patterns and sociodemographic characteristics. Participants will fill an acceptability questionnaire and a subgroup will participate in focus group discussions. ETHICS AND DISSEMINATION: Ethics approval was obtained from the ethics committee of the Centre Hospitalier de l'Université de Montréal, Canada and the Medical Research Ethics Committee of Navarre, Spain. Preliminary findings will be shared with civil and health authorities and reported to individual participants. Results will be submitted for publication in peer-reviewed scientific journals and international conferences. TRIAL REGISTRATION NUMBER: NCT04762693.

Evidencia de la presencia e incremento de niños con diagnóstico de trastornos del espectro autista / Evidence of the presence and increase of children diagnosed with autism spectrum disorders

Introducción: Los Trastornos del Espectro Autista (TEA) son un grupo de complejos trastornos del desarrollo cerebral que se caracterizan por dificultades en la comunicación y la interacción social y por un repertorio de intereses y actividades restringido y repetitivo. Se está asistiendo a un incremento de los pacientes con este diagnóstico. Si bien las causas no están determinadas, se puede atribuir a una mayor concientización sobre el autismo y a la capacidad de detección de los profesionales. Material y Método: Estudio descriptivo. Investigación Cualitativa. Observación. Realizado entre enero y agosto de 2018. M-Chat: Lista de verificación para el autismo en niños pequeños. Criterios diagnósticos DSM V. Signos de alerta. Resultados: A través del trabajo interdisciplinario, de la oportuna observación y aplicación de la herramienta de pesquisa M-Chat destinada a la evaluación de factores de riesgo para autismo, se ha evidenciado la presencia de un número mayor de niños que presentaron signos acordes a un diagnostico dentro del espectro. Conclusión: La detección temprana es una variable determinante de un mejor pronóstico. Debido a la ausencia de marcadores biológicos, el principal medio de detección es la observación. Es por esto que el diagnóstico y la intervención temprana es la mejor respuesta, siendo importante la difusión sobre signos de alerta

Introduction: Autistic Spectrum Disorders (ASD) are a group of complex disorders of brain development characterized by difficulties in communication and social interaction and by a repertoire of interests and restricted and repetitive activities. We are seeing an increase in patients with this diagnosis. Although the causes are not determined, it can be attributed to a greater awareness of autism and the detection capacity of professionals. Material and Method: Descriptive study. Qualitative research. Observation. Made between January and August 2018. M-Chat: Checklist for autism in young children. Diagnostic criteria DSM V. Warning signs. Results: Through the interdisciplinary work, the timely observation and application of the M-Chat research tool aimed at the evaluation of risk factors for autism, the presence of a greater number of children who showed signs according to a diagnostic within the spectrum. Conclusion: Early detection is a determining variable of a better prognosis. Due to the absence of biological markers, the main means of detection is observation. This is why diagnosis and early intervention is the best response, being important the diffusion on warning signs.