The outbreak of coronavirus disease-19 (COVID-19) is a public health emergency of international concern. Telehealth is an effective option to fight the outbreak of COVID-19. The aim of this systematic review was to identify the role of telehealth services in preventing, diagnosing, treating, and controlling diseases during COVID-19 outbreak. This systematic review was conducted through searching five databases including PubMed, Scopus, Embase, Web of Science, and Science Direct. Inclusion criteria included studies clearly defining any use of telehealth services in all aspects of health care during COVID-19 outbreak, published from December 31, 2019, written in English language and published in peer reviewed journals. Two reviewers independently assessed search results, extracted data, and assessed the quality of the included studies. Quality assessment was based on the Critical Appraisal Skills Program (CASP) checklist. Narrative synthesis was undertaken to summarize and report the findings. Eight studies met the inclusion out of the 142 search results. Currently, healthcare providers and patients who are self-isolating, telehealth is certainly appropriate in minimizing the risk of COVID-19 transmission. This solution has the potential to prevent any sort of direct physical contact, provide continuous care to the community, and finally reduce morbidity and mortality in COVID-19 outbreak. The use of telehealth improves the provision of health services. Therefore, telehealth should be an important tool in caring services while keeping patients and health providers safe during COVID-19 outbreak.

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The role of telehealth during COVID-19 outbreak: a systematic review based on current evidence.

Confronting the pandemic of COVID-19, is nowadays one of the most prominent challenges of the human species. A key factor in slowing down the virus propagation is the rapid diagnosis and isolation of infected patients. The standard method for COVID-19 identification, the Reverse transcription polymerase chain reaction method, is time-consuming and in short supply due to the pandemic. Thus, researchers have been looking for alternative screening methods and deep learning applied to chest X-rays of patients has been showing promising results. Despite their success, the computational cost of these methods remains high, which imposes difficulties to their accessibility and availability. Thus, the main goal of this work is to propose an accurate yet efficient method in terms of memory and processing time for the problem of COVID-19 screening in chest X-rays. Methods: To achieve the defined objective we exploit and extend the EfficientNet family of deep artificial neural networks which are known for their high accuracy and low footprints in other applications. We also exploit the underlying taxonomy of the problem with a hierarchical classifier. A dataset of 13,569 X-ray images divided into healthy, non-COVID-19 pneumonia, and COVID-19 patients is used to train the proposed approaches and other 5 competing architectures. Finally, 231 images of the three classes were used to assess the quality of the methods. Results: The results show that the proposed approach was able to produce a high-quality model, with an overall accuracy of 93.9%, COVID-19, sensitivity of 96.8% and positive prediction of 100%, while having from 5 to 30 times fewer parameters than other than the other tested architectures. Larger and more heterogeneous databases are still needed for validation before claiming that deep learning can assist physicians in the task of detecting COVID-19 in X-ray images.

/pdf/towards-an-effective-and-efficient-deep-learning-model-for-1soy8rqots.pdf

Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images

Objectives:Identify and summarize the available literature on the acceleration in the use of telemedicine in the midst of the COVID-19 pandemic, with an aim to provide justification and guidance fo...

https://journals.sagepub.com/doi/pdf/10.1177/2150132720980612

Implementation and Usefulness of Telemedicine During the COVID-19 Pandemic: A Scoping Review.

Telehealth utilization during the Covid-19 pandemic: A systematic review.

Confronting the pandemic of COVID-19 is nowadays one of the most prominent challenges of the human species. A key factor in slowing down the virus propagation is the rapid diagnosis and isolation of infected patients. The standard method for COVID-19 identification, the Reverse transcription polymerase chain reaction method, is time-consuming and in short supply due to the pandemic. Thus, researchers have been looking for alternative screening methods, and deep learning applied to chest X-rays of patients has been showing promising results. Despite their success, the computational cost of these methods remains high, which imposes difficulties to their accessibility and availability. Thus, the main goal of this work is to propose an accurate yet efficient method in terms of memory and processing time for the problem of COVID-19 screening in chest X-rays. To achieve the defined objective, we propose a new family of models based on the EfficientNet family of deep artificial neural networks which are known for their high accuracy and low footprints. We also exploit the underlying taxonomy of the problem with a hierarchical classifier. A dataset of 13,569 X-ray images divided into healthy, non-COVID-19 pneumonia, and COVID-19 patients is used to train the proposed approaches and other 5 competing architectures. We also propose a cross-dataset evaluation with a second dataset to evaluate the method generalization power. The results show that the proposed approach was able to produce a high-quality model, with an overall accuracy of 93.9%, COVID-19 sensitivity of 96.8%, and positive prediction of 100% while having from 5 to 30 times fewer parameters than the other tested architectures. Larger and more heterogeneous databases are still needed for validation before claiming that deep learning can assist physicians in the task of detecting COVID-19 in X-ray images, since the cross-dataset evaluation shows that even state-of-the-art models suffer from a lack of generalization power. We believe the reported figures represent state-of-the-art results, both in terms of efficiency and effectiveness, for the COVIDx database, a database of 13,800 X-ray images, 183 of which are from patients affected by COVID-19. The current proposal is a promising candidate for embedding in medical equipment or even physicians’ mobile phones.

/pdf/towards-an-effective-and-efficient-deep-learning-model-for-4l91moo4na.pdf

Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images

Radiologic management of COVID-19: Preliminary experience of the iranian society of radiology COVID-19 consultant group (ISRCC)

Novel Screening and Triage Strategy in Iran During Deadly Coronavirus Disease 2019 (COVID-19) Epidemic: Value of Humanitarian Teleconsultation Service.

Farahnaz Falahati

Papers

Radiologic management of COVID-19: Preliminary experience of the iranian society of radiology COVID-19 consultant group (ISRCC)

Novel Screening and Triage Strategy in Iran During Deadly Coronavirus Disease 2019 (COVID-19) Epidemic: Value of Humanitarian Teleconsultation Service.