AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review

Deng, Hanqiu; Li, Xingyu

doi:10.3389/frai.2021.612914

Cited by 6 publications

(2 citation statements)

References 94 publications

(122 reference statements)

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“…The limitation of this study was that it did not use image preprocessing and data enhancement techniques, and the use of pre-trained networks or data enhancement may improve accuracy. Since direct transmission between datasets from different fields may lead to poor performance, researchers have developed various strategies to mitigate the impact of domain differences on transmission performance [52].…”

Section: Development Of New DL and Ai Algorithmsmentioning

confidence: 99%

A Promising and Challenging Approach: Radiologists’ Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19

et al. 2021

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Chest X-rays (CXR) and computed tomography (CT) are the main medical imaging modalities used against the increased worldwide spread of the 2019 coronavirus disease (COVID-19) epidemic. Machine learning (ML) and artificial intelligence (AI) technology, based on medical imaging fully extracting and utilizing the hidden information in massive medical imaging data, have been used in COVID-19 research of disease diagnosis and classification, treatment decision-making, efficacy evaluation, and prognosis prediction. This review article describes the extensive research of medical image-based ML and AI methods in preventing and controlling COVID-19, and summarizes their characteristics, differences, and significance in terms of application direction, image collection, and algorithm improvement, from the perspective of radiologists. The limitations and challenges faced by these systems and technologies, such as generalization and robustness, are discussed to indicate future research directions.

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Section: Development Of New DL and Ai Algorithmsmentioning

confidence: 99%

A Promising and Challenging Approach: Radiologists’ Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19

et al. 2021

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“…Indeed, the segmentation of HRCT images, which means the manual delineation and quantification of the pathological lung regions from the imaging data, was revealed to be a challenging and time-consuming task, not only for this reason, but also due to the high number of cases to report, the magnitude of the imaging data, and the similarity of COVID-19 patterns with other types of pneumonia [8]. A modern solution to this challenge is the integration of automated segmentation using Artificial Intelligence (AI), specifically methods based on Deep Learning (DL) [4,9] and Convolutional Neural Networks (CNNs) [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Enhancing COVID-19 CT Image Segmentation: A Comparative Study of Attention and Recurrence in UNet Models

Buongiorno,

Del Corso,

Germanese

et al. 2023

J. Imaging

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Imaging plays a key role in the clinical management of Coronavirus disease 2019 (COVID-19) as the imaging findings reflect the pathological process in the lungs. The visual analysis of High-Resolution Computed Tomography of the chest allows for the differentiation of parenchymal abnormalities of COVID-19, which are crucial to be detected and quantified in order to obtain an accurate disease stratification and prognosis. However, visual assessment and quantification represent a time-consuming task for radiologists. In this regard, tools for semi-automatic segmentation, such as those based on Convolutional Neural Networks, can facilitate the detection of pathological lesions by delineating their contour. In this work, we compared four state-of-the-art Convolutional Neural Networks based on the encoder–decoder paradigm for the binary segmentation of COVID-19 infections after training and testing them on 90 HRCT volumetric scans of patients diagnosed with COVID-19 collected from the database of the Pisa University Hospital. More precisely, we started from a basic model, the well-known UNet, then we added an attention mechanism to obtain an Attention-UNet, and finally we employed a recurrence paradigm to create a Recurrent–Residual UNet (R2-UNet). In the latter case, we also added attention gates to the decoding path of an R2-UNet, thus designing an R2-Attention UNet so as to make the feature representation and accumulation more effective. We compared them to gain understanding of both the cognitive mechanism that can lead a neural model to the best performance for this task and the good compromise between the amount of data, time, and computational resources required. We set up a five-fold cross-validation and assessed the strengths and limitations of these models by evaluating the performances in terms of Dice score, Precision, and Recall defined both on 2D images and on the entire 3D volume. From the results of the analysis, it can be concluded that Attention-UNet outperforms the other models by achieving the best performance of 81.93%, in terms of 2D Dice score, on the test set. Additionally, we conducted statistical analysis to assess the performance differences among the models. Our findings suggest that integrating the recurrence mechanism within the UNet architecture leads to a decline in the model’s effectiveness for our particular application.

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Artificial intelligence for chest imaging against COVID-19: an insight into image segmentation methods

Buongiorno

Germanese

Colligiani

et al. 2023

Artificial Intelligence in Healthcare and COVID-19

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AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review

Cited by 6 publications

References 94 publications

A Promising and Challenging Approach: Radiologists’ Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19

A Promising and Challenging Approach: Radiologists’ Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19

Enhancing COVID-19 CT Image Segmentation: A Comparative Study of Attention and Recurrence in UNet Models

Artificial intelligence for chest imaging against COVID-19: an insight into image segmentation methods

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