Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts. Many machine learning-based approaches have been developed for the prognosis and diagnosis of COVID-19 from medical images and this Analysis identifies over 2,200 relevant published papers and preprints in this area. After initial screening, 62 studies are analysed and the authors find they all have methodological flaws standing in the way of clinical utility. The authors have several recommendations to address these issues.

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Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation.

COVID-19 Detection in CT Images with Deep Learning: A Voting-based Scheme and Cross-Datasets Analysis

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming, demanding, and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methodologies, deep learning (DL) networks have gained much popularity compared to traditional machine learning (ML) methods. Unlike ML techniques, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and automated segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL techniques is presented. Lastly, the challenges faced in the automated detection of COVID-19 using DL techniques and directions for future research are discussed.

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

The World Health Organization (WHO) declared novel coronavirus 2019 (COVID-19), an infectious epidemic caused by SARS-CoV-2, as Pandemic in March 2020. It has affected more than 40 million people in 216 countries. Almost in all the affected countries, the number of infected and deceased patients has been enhancing at a distressing rate. As the early prediction can reduce the spread of the virus, it is highly desirable to have intelligent prediction and diagnosis tools. The inculcation of efficient forecasting and prediction models may assist the government in implementing better design strategies to prevent the spread of virus. In this paper, a state-of-the-art analysis of the ongoing machine learning (ML) and deep learning (DL) methods in the diagnosis and prediction of COVID-19 has been done. Moreover, a comparative analysis on the impact of machine learning and other competitive approaches like mathematical and statistical models on COVID-19 problem has been conducted. In this study, some factors such as type of methods(machine learning, deep learning, statistical & mathematical) and the impact of COVID research on the nature of data used for the forecasting and prediction of pandemic using computing approaches has been presented. Finally some important research directions for further research on COVID-19 are highlighted which may facilitate the researchers and technocrats to develop competent intelligent models for the prediction and forecasting of COVID-19 real time data.

Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review.

The fast spreading of the novel coronavirus COVID-19 has aroused worldwide interest and concern, and caused more than one million and a half confirmed cases to date. To combat this spread, medical imaging such as computed tomography (CT) images can be used for diagnostic. An automatic detection tools is necessary for helping screening COVID-19 pneumonia using chest CT imaging. In this work, we propose a multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. Our motivation is to leverage useful information contained in multiple related tasks to help improve both segmentation and classification performances. Our architecture is composed by an encoder and two decoders for reconstruction and segmentation, and a multi-layer perceptron for classification. The proposed model is evaluated and compared with other image segmentation and classification techniques using a dataset of 1044 patients including 449 patients with COVID-19, 100 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.78 for the segmentation and an area under the ROC curve higher than 93% for the classification.

https://www.medrxiv.org/content/medrxiv/early/2020/04/21/2020.04.16.20064709.full.pdf

Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation

Multi-Task Deep Learning Based CT Imaging Analysis for Covid-19: Classification and Segmentation

Radiomics is now widely used to improve the prediction of treatment response and patient prognosis in oncology. In this paper, we propose an end-to-end prediction model based on a 3-D convolutional neural network (CNN), called 3-D RPET-NET, that extracts 3-D image features through four layers. Our model was evaluated for its ability to predict the response to radio-chemotherapy in 97 patients with esophageal cancer from positron emission tomography (PET) images. The accuracy of the model was compared to five other methods proposed in the literature for PET images, based on 2-D CNN and random forest algorithms. The role of the volume of interest on the accuracy of 3-D RPET-NET was also evaluated using isotropic margins of 1–4 cm around the tumor volume. After segmentation of the lesion using a fixed threshold value of 40% of the maximum standard uptake value, the best accuracy of 3-D RPET-NET reached 72% and outperformed the other methods tested. We also showed that using an isotropic margin of 2 cm around the tumor volume improved the performances of 3-D RPET-NET to reach an accuracy of 75%.

3-D RPET-NET: Development of a 3-D PET Imaging Convolutional Neural Network for Radiomics Analysis and Outcome Prediction

Artificial intelligence (AI) techniques have significant potential to enable effective, robust, and automated image phenotyping including the identification of subtle patterns. AI-based detection searches the image space to find the regions of interest based on patterns and features. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features. The extraction of minable information from images gives way to the field of "radiomics" and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics analysis has the potential to be used as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring. This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks. We also discuss needed efforts to enable the translation of AI techniques to routine clinical workflows, and potential improvements and complementary techniques such as the use of natural language processing on electronic health records and neuro-symbolic AI techniques.

AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging:: Towards Radiophenomics.

324 Objectives: The aim was i) to develop and validate three convolutional neural network architectures (CNN) for predicting response to cancer treatment in FDG PET imaging, and ii) to compare their performances with three random forest classifiers. Methods: We have developed an end-to-end 3D convolutional neural network (3D-CNN). We have also evaluated 2 others CNN architectures from the literature (Ypsilantis et al. PloS, 2015). The first one, called 1S-CNN, corresponds to an architecture where the input of the CNN corresponds to one slice. The process is repeated on each slice belonging to the tumor. Then, the model is evaluated by a majority vote. The second one, called 3S-CNN takes a triplet of adjacent slices as input. The CNN architectures were evaluated on a retrospective study database of initial FDG PET images of 97 patients with an esophageal cancer treated by chemo-radiotherapy. 56 patients responded 3 months after treatment. FDG positive tissues were segmented using a fixed threshold value of 40% of SUVmax. Images were spatially normalized with isotropic voxels (2x2x2 mm3) followed by an absolute intensity resampling (0.5 SUV/bin). For each CNN, the search for the best architecture was achieved using a validation procedure, by tuning hyper parameters, such as the number of layers, the number of feature maps and the size of filters among others. For comparison, a radiomic analysis was also conducted from 17 uncorrelated features (Spearman’s rank correlation coefficient Results: The performances of the CNN architectures outperformed those found with the RF classifiers. The best results were found with 3D-CNN and 3S-CNN with comparable performances: Acc=0.72±0.08, AUC=0.70±0.04, Se=0.79±0.17 and Sp=0.62±0.21 (3D-CNN). 1S-CNN, seems to have lower performances (Acc=0.67±0.06, AUC=0.67±0.06), but the 1S-CNN ROC curve was not statistically significantly different from 3D-CNN (p=0.53) and 3S-CNN (p=0.48) ROC curves. From the RF classifiers, the best results were found with the GARF algorithm (Acc=0.68±0.08, Se=0.79±0.10, Sp=0.41±0.11, AUC=0.59±0.06). GARF ROC curve was not statistically significantly different from 1S-CNN (p=0.10) and 3S-CNN (p=0.058) ROC curves, while the 3D-CNN ROC curve gave better statistically significant results compared to GARF ROC curve (p=0.028). Conclusion: CNN architectures give very promising results for predicting cancer treatment response on initial PET tumor images. These results need to be confirmed with different type of cancers.

Amine Amyar

Papers

Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation

Multi-Task Deep Learning Based CT Imaging Analysis for Covid-19: Classification and Segmentation

3-D RPET-NET: Development of a 3-D PET Imaging Convolutional Neural Network for Radiomics Analysis and Outcome Prediction

AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging:: Towards Radiophenomics.

Radiomics-net: Convolutional Neural Networks on FDG PET Images for predicting cancer treatment response