AI-based Power Screening Solution for SARS-CoV2 Infection: A Sociodemographic Survey and COVID-19 Cough Detector

Sadhana, S; Pandiarajan, S; Sivaraman, E.; Daniel, D

doi:10.1016/j.procs.2021.10.081

Cited by 9 publications

(7 citation statements)

References 27 publications

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“…Inclusion criteria for healthy people into the study: (1) lack of symptoms of the COVID-19, (2) if the symptoms are present, the physician's diagnosis should not be COVID-19, and (3) None of the close people should have COVID-19 (Alqudaihi et al, 2021 ; Sadhana et al, 2021 ; Santosh et al, 2022 ). During data collection and recording of coughing, healthy people and patients were asked to cough in a safe environment without the presence of other people.…”

Section: Methodsmentioning

confidence: 99%

“…By using the anatomy of the respiratory system, it is possible to measure the amount of changed respiratory infections based on the cough sound (Bai et al, 2020 ; Alqudaihi et al, 2021 ; Mohammed et al, 2021 ). In the past years, studies have been conducted to identify whooping cough (pertussis), chronic obstructive pulmonary disease (COPD), tuberculosis and asthma using an algorithm of audio signals by analyzing cough sounds (Bai et al, 2020 ; Alqudaihi et al, 2021 ; Mohammed et al, 2021 ; Sadhana et al, 2021 ; Santosh et al, 2022 ). Nowadays, some universities in the world, including MIT University in the United States, Cambridge University in England, EPFL University in Switzerland, and Carnegie Mellon University in United States are studying the diagnosis of COVID-19 through cough with the help of artificial intelligence methods (Bai et al, 2020 ; Wang and Wong, 2020 ; Alqudaihi et al, 2021 ; Mohammed et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Coronavirus diagnosis using cough sounds: Artificial intelligence approaches

Nasab

Mirzaei²,

Zali³

et al. 2023

Front. Artif. Intell.

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IntroductionThe Coronavirus disease 2019 (COVID-19) pandemic has caused irreparable damage to the world. In order to prevent the spread of pathogenicity, it is necessary to identify infected people for quarantine and treatment. The use of artificial intelligence and data mining approaches can lead to prevention and reduction of treatment costs. The purpose of this study is to create data mining models in order to diagnose people with the disease of COVID-19 through the sound of coughing.MethodIn this research, Supervised Learning classification algorithms have been used, which include Support Vector Machine (SVM), random forest, and Artificial Neural Networks, that based on the standard “Fully Connected” neural network, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) recurrent neural networks have been established. The data used in this research was from the online site sorfeh.com/sendcough/en, which has data collected during the spread of COVID-19.ResultWith the data we have collected (about 40,000 people) in different networks, we have reached acceptable accuracies.ConclusionThese findings show the reliability of this method for using and developing a tool as a screening and early diagnosis of people with COVID-19. This method can also be used with simple artificial intelligence networks so that acceptable results can be expected. Based on the findings, the average accuracy was 83% and the best model was 95%.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coronavirus diagnosis using cough sounds: Artificial intelligence approaches

Nasab

Mirzaei²,

Zali³

et al. 2023

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…Recently, due to the limitations of real-life consultations caused by the COVID-19 pandemic, such applications have experienced a major increase in popularity. [2][3][4][5][6][7][8][9] Many medical fields, such as radiology, 10 cardiology, 11 oncology, 12 or psychiatry, 13 profit from this development, but the AI revolution itself is deeply rooted in dermatology due to the visual nature of this specialty. 14 As skin lesions can be characterized by specific imaging criteria, machine intelligence is able to aid in categorizing those lesions.…”

Section: Introductionmentioning

confidence: 99%

“…Such applications are often supported or run by artificial intelligence (AI). Recently, due to the limitations of real‐life consultations caused by the COVID‐19 pandemic, such applications have experienced a major increase in popularity 2–9 . Many medical fields, such as radiology, 10 cardiology, 11 oncology, 12 or psychiatry, 13 profit from this development, but the AI revolution itself is deeply rooted in dermatology due to the visual nature of this specialty 14 …”

Section: Introductionmentioning

confidence: 99%

Basic principles of artificial intelligence in dermatology explained using melanoma

Hartmann,

Passauer,

Hartmann

et al. 2024

J Deutsche Derma Gesell

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SummaryThe use of artificial intelligence (AI) continues to establish itself in the most diverse areas of medicine at an increasingly fast pace. Nevertheless, many healthcare professionals lack the basic technical understanding of how this technology works, which severely limits its application in clinical settings and research. Thus, we would like to discuss the functioning and classification of AI using melanoma as an example in this review to build an understanding of the technology behind AI. For this purpose, elaborate illustrations are used that quickly reveal the technology involved. Previous reviews tend to focus on the potential applications of AI, thereby missing the opportunity to develop a deeper understanding of the subject matter that is so important for clinical application. Malignant melanoma has become a significant burden for healthcare systems. If discovered early, a better prognosis can be expected, which is why skin cancer screening has become increasingly popular and is supported by health insurance. The number of experts remains finite, reducing their availability and leading to longer waiting times. Therefore, innovative ideas need to be implemented to provide the necessary care. Thus, machine learning offers the ability to recognize melanomas from images at a level comparable to experienced dermatologists under optimized conditions.

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“…Solche Anwendungen werden häufig durch künstliche Intelligenz (KI) unterstützt oder gesteuert. In jüngster Zeit haben solche Anwendungen aufgrund der durch die COVID‐19‐Pandemie verursachten Einschränkungen bei realen Konsultationen stark an Popularität gewonnen 2–9 . Viele medizinische Bereiche wie die Radiologie, 10 die Kardiologie, 11 die Onkologie, 12 oder die Psychiatrie 13 profitieren von dieser Entwicklung, aber die KI‐Revolution selbst ist aufgrund der visuellen Natur dieses Fachgebiets tief in der Dermatologie verwurzelt 14 …”

Section: Introductionunclassified

Grundprinzipien der künstlichen Intelligenz in der Dermatologie erklärt am Beispiel des Melanoms

Hartmann,

Passauer,

Hartmann

et al. 2024

J Deutsche Derma Gesell

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ZusammenfassungDer Einsatz von künstlicher Intelligenz (KI) setzt sich in den verschiedensten Bereichen der Medizin immer schneller durch. Dennoch fehlt vielen medizinischen Kollegen das technische Grundverständnis für die Funktionsweise dieser Technologie, was ihre Anwendung in Klinik und Forschung stark einschränkt. Daher möchten wir in dieser Übersichtsarbeit die Funktionsweise und Klassifizierung der KI am Beispiel des Melanoms erörtern, um ein Verständnis für die Technologie hinter der KI zu schaffen. Dazu werden ausführliche Illustrationen verwendet, die die Technologie schnell erklären. Bisherige Übersichten konzentrieren sich eher auf die potenziellen Anwendungen der KI und verpassen die Gelegenheit, ein tieferes Verständnis für die Materie herauszuarbeiten, das für die klinische Anwendung so wichtig ist. Das maligne Melanom ist zu einer erheblichen Belastung für die Gesundheitssysteme geworden. Bei frühzeitiger Entdeckung ist eine bessere Prognose zu erwarten, weshalb das Hautkrebs‐Screening immer populärer und von den Krankenkassen unterstützt wird. Die Zahl der Fachärzte ist jedoch begrenzt, was ihre Verfügbarkeit einschränkt und zu längeren Wartezeiten führt. Daher müssen innovative Ideen umgesetzt werden, um die notwendige Versorgung zu gewährleisten. Das maschinelle Lernen bietet die Möglichkeit, Melanome auf Bildern zu erkennen, und zwar auf einem Niveau, das mit dem von erfahrenen Dermatologen – unter optimierten Bedingungen – vergleichbar ist.

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AI-based Power Screening Solution for SARS-CoV2 Infection: A Sociodemographic Survey and COVID-19 Cough Detector

Cited by 9 publications

References 27 publications

Coronavirus diagnosis using cough sounds: Artificial intelligence approaches

Coronavirus diagnosis using cough sounds: Artificial intelligence approaches

Basic principles of artificial intelligence in dermatology explained using melanoma

Grundprinzipien der künstlichen Intelligenz in der Dermatologie erklärt am Beispiel des Melanoms

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