Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19

IEEE Reviews in Biomedical Engineering, Journal Year: 2020, Volume and Issue: 14, P. 4 - 15

Published: April 16, 2020

The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in global fight against COVID-19, whereas recently emerging artificial intelligence (AI) technologies further strengthen power tools help medical specialists. We hereby review rapid responses community (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly automate scanning procedure also reshape workflow with minimal contact to patients, providing best protection technicians. Also, AI improve work efficiency accurate delineation infections CT images, facilitating subsequent quantification. Moreover, computer-aided platforms radiologists make clinical decisions, i.e., for diagnosis, tracking, prognosis. In this paper, we thus cover entire pipeline analysis techniques involved including acquisition, segmentation, follow-up. particularly focus on integration CT, both which are widely used frontline hospitals, order depict latest progress radiology fighting

Language: Английский

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Social physics

Physics Reports, Journal Year: 2022, Volume and Issue: 948, P. 1 - 148

Published: Jan. 11, 2022

Language: Английский

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Unleashing the convergence amid digitalization and sustainability towards pursuing the Sustainable Development Goals (SDGs): A holistic review

Journal of Cleaner Production, Journal Year: 2020, Volume and Issue: 280, P. 122204 - 122204

Published: Sept. 18, 2020

Language: Английский

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Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost

Computers Environment and Urban Systems, Journal Year: 2022, Volume and Issue: 96, P. 101845 - 101845

Published: June 18, 2022

Machine learning and artificial intelligence (ML/AI), previously considered black box approaches, are becoming more interpretable, as a result of the recent advances in eXplainable AI (XAI). In particular, local interpretation methods such SHAP (SHapley Additive exPlanations) offer opportunity to flexibly model, interpret visualise complex geographical phenomena processes. this paper, we use XGBoost (eXtreme Gradient Boosting) an example demonstrate how extract spatial effects from machine models. We conduct simulation experiments that compare SHAP-explained Spatial Lag Model (SLM) Multi-scale Geographically Weighted Regression (MGWR) at parameter level. Results show estimates similar those SLM MGWR An empirical Chicago ride-hailing modelling is presented utility with real datasets. Examples evidence paper suggest locally interpreted models good alternatives statistical perform better when non-spatial (e.g. non-linearities, interactions) co-exist unknown.

Language: Английский

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Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images

IEEE Access, Journal Year: 2020, Volume and Issue: 8, P. 118869 - 118883

Published: Jan. 1, 2020

An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with risk fatality 4.03% and highest 13.04% Algeria 12.67% Italy (as 8th April 2020). The onset serious illness may result death as consequence substantial alveolar damage progressive respiratory failure. laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), golden standard for clinical diagnosis, tests produce false negatives. Moreover, under situation, shortage RT-PCR testing resources delay following decision treatment. Under such circumstances, chest CT imaging become valuable tool both diagnosis prognosis patients. In this study, we propose weakly supervised deep learning strategy detecting classifying infection from images. proposed method minimise requirements manual labelling images but still able to obtain accurate detection distinguish non-COVID-19 cases. Based on promising results obtained qualitatively quantitatively, envisage wide deployment our developed technique large-scale studies.

Language: Английский

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Artificial intelligence vs COVID-19: limitations, constraints and pitfalls

AI & Society, Journal Year: 2020, Volume and Issue: 35(3), P. 761 - 765

Published: April 28, 2020

This paper provides an early evaluation of Artificial Intelligence (AI) against COVID-19. The main areas where AI can contribute to the fight COVID-19 are discussed. It is concluded that has not yet been impactful Its use hampered by a lack data, and too much data. Overcoming these constraints will require careful balance between data privacy public health, rigorous human-AI interaction. unlikely be addressed in time help during present pandemic. In meantime, extensive gathering diagnostic on who infectious essential save lives, train AI, limit economic damages.

Language: Английский

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A review of modern technologies for tackling COVID-19 pandemic

Diabetes & Metabolic Syndrome Clinical Research & Reviews, Journal Year: 2020, Volume and Issue: 14(4), P. 569 - 573

Published: May 7, 2020

Language: Английский

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Machine learning based approaches for detecting COVID-19 using clinical text data

International Journal of Information Technology, Journal Year: 2020, Volume and Issue: 12(3), P. 731 - 739

Published: June 30, 2020

Technology advancements have a rapid effect on every field of life, be it medical or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing data. COVID-19 affected more than 100 countries matter no time. People all over world are vulnerable to consequences future. It is imperative develop control system that will detect coronavirus. One solution current havoc can diagnosis disease with help various AI tools. In this paper, we classified textual clinical reports into four classes using classical and ensemble machine learning algorithms. Feature engineering was performed techniques like Term frequency/inverse document frequency (TF/IDF), Bag words (BOW) report length. These features were supplied traditional classifiers. Logistic regression Multinomial Naïve Bayes showed better ML algorithms having 96.2% testing accuracy. future recurrent neural network used for

Language: Английский

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Deep Learning applications for COVID-19

Journal Of Big Data, Journal Year: 2021, Volume and Issue: 8(1)

Published: Jan. 11, 2021

This survey explores how Deep Learning has battled the COVID-19 pandemic and provides directions for future research on COVID-19. We cover applications in Natural Language Processing, Computer Vision, Life Sciences, Epidemiology. describe each of these vary with availability big data learning tasks are constructed. begin by evaluating current state conclude key limitations applications. These include Interpretability, Generalization Metrics, from Limited Labeled Data, Data Privacy. Processing mining Information Retrieval Question Answering, as well Misinformation Detection, Public Sentiment Analysis. Vision Medical Image Analysis, Ambient Intelligence, Vision-based Robotics. Within our looks at can be applied to Precision Diagnostics, Protein Structure Prediction, Drug Repurposing. additionally been utilized Spread Forecasting Our literature review found many examples systems fight hope that this will help accelerate use research.

Language: Английский

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Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art

SN Computer Science, Journal Year: 2020, Volume and Issue: 1(4)

Published: June 11, 2020

COVID-19 is a pandemic that has affected over 170 countries around the world. The number of infected and deceased patients been increasing at an alarming rate in almost all nations. Forecasting techniques can be inculcated thereby assisting designing better strategies taking productive decisions. These assess situations past enabling predictions about situation to occur future. might help prepare against possible threats consequences. play very important role yielding accurate predictions. This study categorizes forecasting into two types, namely, stochastic theory mathematical models data science/machine learning techniques. Data collected from various platforms also vital forecasting. In this study, categories datasets have discussed, i.e., big accessed World Health Organization/National databases social media communication. done based on parameters such as impact environmental factors, incubation period, quarantine, age, gender many more. used for are extensively studied work. However, come with their own set challenges (technical generic). discusses these provides recommendations people who currently fighting global pandemic.

Language: Английский

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