COVIR: A virtual rendering of a novel NN architecture O-Net for COVID-19 Ct-scan automatic lung lesions segmentation

Amara, Kahina; Aouf, Ali; Kennouche, Hoceine; Djekoune, Oualid; Zenati, Nadia; Kerdjidj, Oussama; Ferguene, Farid

doi:10.1016/j.cag.2022.03.003

Cited by 18 publications

(3 citation statements)

References 23 publications

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“…This section entails providing an in-depth explanation of the state-of-the-art research utilizing CT scan images and deep learning for COVID detection. At a broader level, these research studies can be classified into two types (a) COVID Detection ( Bougourzi, Contino et al, 2021 , Lassau et al, 2021 , Li et al, 2021 , Song et al, 2021 , Ye et al, 2022 ) (b) COVID Segmentation ( Amara et al, 2022 , Bose et al, 2022 , Elharrouss et al, 2022 , Fan et al, 2020 , Hu et al, 2022 , Stefano and Comelli, 2021 ). The research studies falling into the aforementioned categories are explained in the subsequent paragraph.…”

Section: Related Workmentioning

confidence: 99%

EffViT-COVID: A dual-path network for COVID-19 percentage estimation

Chauhan

Bedi²

2023

Expert Systems with Applications

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Section: Related Workmentioning

confidence: 99%

EffViT-COVID: A dual-path network for COVID-19 percentage estimation

Chauhan

Bedi²

2023

Expert Systems with Applications

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“…In recent studies focused on COVID-19 diagnosis, researchers have explored the potential of VR and AR immersive technologies [78][79][80]. These investigations have sought to integrate CT imaging with VR technology to create an automated system capable of navigating 3D visualizations.…”

Section: Ai Can Improve Teleguided Pocus Monitoring Of Covid-19 Patientsmentioning

confidence: 99%

Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound

Sultan,

Haertter,

Al-Hasani

et al. 2023

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With the 2019 coronavirus disease (COVID-19) pandemic, there is an increasing demand for remote monitoring technologies to reduce patient and provider exposure. One field that has an increasing potential is teleguided ultrasound, where telemedicine and point-of-care ultrasound (POCUS) merge to create this new scope. Teleguided POCUS can minimize staff exposure while preserving patient safety and oversight during bedside procedures. In this paper, we propose the use of teleguided POCUS supported by AI technologies for the remote monitoring of COVID-19 patients by non-experienced personnel including self-monitoring by the patients themselves. Our hypothesis is that AI technologies can facilitate the remote monitoring of COVID-19 patients through the utilization of POCUS devices, even when operated by individuals without formal medical training. In pursuit of this goal, we performed a pilot analysis to evaluate the performance of users with different clinical backgrounds using a computer-based system for COVID-19 detection using lung ultrasound. The purpose of the analysis was to emphasize the potential of the proposed AI technology for improving diagnostic performance, especially for users with less experience.

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“…When writing this paper, no works addressed the 3D virtual visualisation of segmented COVID-19 lung lesions using Deep Learning approach for Augmented reality rendering to the best of our knowledge. We recently addressed the virtual rendering of COVID-19 lung lesions segmentation in [18] Recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. In this paper, we propose a COVID-19 lung lesions segmentation using U-Net.…”

Section: Introductionmentioning

confidence: 99%

Augmented Reality for COVID-19 Aid Diagnosis: Ct-Scan segmentation based Deep Learning

Amara

Aouf

Kerdjidj

et al. 2022

2022 7th International Conference on Image and Signal Processing and Their Applications (ISPA)

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The virus new variants of Coronavirus disease 2019 (COVID-19) continue to appear, making the situation more challenging and threatening. The COVID-19 pandemic has profoundly affected health systems and medical centres worldwide. The primary clinical tools used in diagnosing patients presenting with respiratory distress and suspected COVID-19 symptoms are radiology examinations. Recently emerging artificial intelligence (AI) technologies further strengthen the power of imaging tools and help medical specialists. This paper presents an Augmented Reality (AR) tool for COVID-19 aid diagnosis, including Computerised Tomography Ct-scans segmentation based Deep Learning, 3D reconstruction, and AR visualisation. Segmentation is a critical step in AI-based COVID-19 image processing and analysis; we use the popular segmentation networks, including classic U-Net. Quantitative and qualitative evaluation showed reasonable performance of U-Net for lung and COVID-19 lesions segmentation. The AR-COVID-19 aid diagnosis system could be used for medical education professional training and as a support visualisation and reading tool for radiologist.

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COVIR: A virtual rendering of a novel NN architecture O-Net for COVID-19 Ct-scan automatic lung lesions segmentation

Cited by 18 publications

References 23 publications

EffViT-COVID: A dual-path network for COVID-19 percentage estimation

EffViT-COVID: A dual-path network for COVID-19 percentage estimation

Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound

Augmented Reality for COVID-19 Aid Diagnosis: Ct-Scan segmentation based Deep Learning

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