Overview of current state of research on the application of artificial intelligence techniques for COVID-19 DOI Creative Commons
Vijay Kumar, Dilbag Singh,

Manjit Kaur

et al.

PeerJ Computer Science, Journal Year: 2021, Volume and Issue: 7, P. e564 - e564

Published: May 26, 2021

Background Until now, there are still a limited number of resources available to predict and diagnose COVID-19 disease. The design novel drug-drug interaction for patients is an open area research. Also, the development rapid testing kits challenging task. Methodology This review focuses on two prime challenges caused by urgent needs effectively address pandemic, i.e., classification tools drug discovery models infected with help artificial intelligence (AI) based techniques such as machine learning deep models. Results In this paper, various AI-based studied evaluated means applying these prediction diagnosis study provides recommendations future research facilitates knowledge collection formation application AI dealing epidemic its consequences. Conclusions can be effective tool tackle COVID-19. These may utilized in four main fields prediction, diagnosis, design, analyzing social implications patients.

Language: Английский

Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review DOI Open Access
Sujan Sarker, Lafifa Jamal,

Syeda Faiza Ahmed

et al.

Robotics and Autonomous Systems, Journal Year: 2021, Volume and Issue: 146, P. 103902 - 103902

Published: Oct. 7, 2021

Language: Английский

Citations

152
Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization DOI
Hang Su, Dong Zhao, Hela Elmannai

et al.

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 146, P. 105618 - 105618

Published: May 18, 2022

Language: Английский

Citations

143
Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives DOI Creative Commons
Shigao Huang, Jie Yang, Simon Fong

et al.

International Journal of Biological Sciences, Journal Year: 2021, Volume and Issue: 17(6), P. 1581 - 1587

Published: Jan. 1, 2021

Artificial intelligence (AI) is being used to aid in various aspects of the COVID-19 crisis, including epidemiology, molecular research and drug development, medical diagnosis treatment, socioeconomics.The association AI can accelerate rapidly diagnose positive patients.To learn dynamics a pandemic with relevance AI, we search literature using different academic databases (PubMed, PubMed Central, Scopus, Google Scholar) preprint servers (bioRxiv, medRxiv, arXiv).In present review, address clinical applications machine learning deep learning, characteristics, electronic records, images (CT, X-ray, ultrasound images, etc.) diagnosis.The current challenges future perspectives provided this review be direct an ideal deployment technology pandemic.

Language: Английский

Citations

118
Densely connected convolutional networks-based COVID-19 screening model DOI Creative Commons
Dilbag Singh, Vijay Kumar, Manjit Kaur

et al.

Applied Intelligence, Journal Year: 2021, Volume and Issue: 51(5), P. 3044 - 3051

Published: Feb. 7, 2021

Language: Английский

Citations

115
Artificial Intelligence Applications in Health Care Practice: Scoping Review DOI Creative Commons
Malvika Sharma, Carl Savage, Monika Nair

et al.

Journal of Medical Internet Research, Journal Year: 2022, Volume and Issue: 24(10), P. e40238 - e40238

Published: Aug. 30, 2022

Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount data collected and available in health care, coupled with advances computational power, has contributed to AI an exponential growth publications. However, development applications does not guarantee their adoption into routine practice. There risk despite resources invested, benefits for patients, staff, society be realized if implementation better understood.The aim this study was explore how care been described researched literature by answering 3 questions: What are characteristics research on practice? types systems described? process discernible?A scoping review conducted MEDLINE (PubMed), Scopus, Web Science, CINAHL, PsycINFO databases identify empirical studies since 2011, addition snowball sampling selected reference lists. Using Rayyan software, we screened titles abstracts full-text articles. Data from included articles were charted summarized.Of 9218 records retrieved, 45 (0.49%) included. cover diverse clinical settings disciplines; most (32/45, 71%) published recently, high-income countries (33/45, 73%), intended providers (25/45, 56%). predominantly particularly pertaining patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. focus establishing effectiveness interventions (16/45, 35%) or related technical aspects (11/45, 24%). Focus specifics processes yet seem priority research, use frameworks guide rare.Our current knowledge derives implementations low approaches common other information systems. To develop specific empirically based framework, further needed more disruptive being implemented unique such building trust, addressing transparency issues, developing explainable interpretable solutions, ethical concerns around privacy protection.

Language: Английский

Citations

113
Computer-Aided Detection and Classification of Monkeypox and Chickenpox Lesion in Human Subjects Using Deep Learning Framework DOI Creative Commons
Dilber Uzun Ozsahin, Mubarak Taiwo Mustapha, Berna Uzun

et al.

Diagnostics, Journal Year: 2023, Volume and Issue: 13(2), P. 292 - 292

Published: Jan. 12, 2023

Monkeypox is a zoonotic viral disease caused by the monkeypox virus. After its recent outbreak, it has become clear that rapid, accurate, and reliable diagnosis may help reduce risk of future outbreak. The presence skin lesions one most prominent symptoms disease. However, this symptom also peculiar to chickenpox. resemblance in human subject disrupt effective and, as result, lead misdiagnosis. Such misdiagnosis can further spread communicable eventually result an As deep learning (DL) algorithms have recently been regarded promising technique medical fields, we attempting integrate well-trained DL algorithm assist early detection classification subjects. This study used two open-sourced digital images for A two-dimensional convolutional neural network (CNN) consisting four layers was applied. Afterward, three MaxPooling were after second, third, fourth layers. Finally, evaluated performance our proposed model with state-of-the-art deep-learning models detection. Our CNN outperformed all test accuracy 99.60%. In addition, weighted average precision, recall, F1 score 99.00% recorded. Subsequently, Alex Net other pre-trained 98.00%. VGGNet VGG16 VGG19 performed least well 80.00%. Due uniqueness image augmentation techniques applied, generalized avoids over-fitting. would be helpful rapid accurate using patients suspected monkeypox.

Language: Английский

Citations

49
A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron DOI
Asifullah Khan, Saddam Hussain Khan,

Mahrukh Saif

et al.

Journal of Experimental & Theoretical Artificial Intelligence, Journal Year: 2023, Volume and Issue: 36(8), P. 1779 - 1821

Published: Jan. 12, 2023

The Coronavirus (COVID-19) outbreak in December 2019 has drastically affected humans worldwide, creating a health crisis that infected millions of lives and devastated the global economy. COVID-19 is ongoing, with emergence many new strains. Deep learning (DL) techniques have proven helpful efficiently analysing delineating infectious regions radiological images. This survey paper draws taxonomy deep for detecting infection radiographic imaging modalities Chest X-Ray, Computer Tomography. DL are broadly categorised into classification, segmentation, multi-stage approaches diagnosis at image region-level analysis. These further classified as pre-trained custom-made Convolutional Neural Network architectures. Furthermore, discussion drawn on datasets, evaluation metrics, commercial platforms provided detection. In end, brief look paid to emerging ideas, gaps existing research, challenges developing diagnostic techniques. provides insight promising areas research likely guide community upcoming development COVID-19. will pave way accelerate designing customised DL-based tools effectively dealing variants challenges.

Language: Английский

Citations

45
Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review DOI Creative Commons
Buket Baddal, Ferdiye Taner, Dilber Uzun Ozsahin

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 14(5), P. 484 - 484

Published: Feb. 23, 2024

Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents foundation for effective prevention control of HAIs, yet conventional surveillance is costly labor intensive. Artificial intelligence (AI) machine learning (ML) have potential to support development HAI algorithms understanding risk factors, improvement patient stratification as well prediction timely detection infections. AI-supported systems so far been explored clinical laboratory testing imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery prediction-based decision tools terms HAIs. This review aims provide comprehensive summary current literature on AI applications field HAIs discuss future potentials this emerging technology infection practice. Following PRISMA guidelines, study examined articles databases including PubMed Scopus until November 2023, which were screened based inclusion exclusion criteria, resulting 162 included articles. By elucidating advancements field, we aim highlight report related issues shortcomings directions.

Language: Английский

Citations

17
Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review DOI Creative Commons
Hossein Mohammad‐Rahimi,

Mohadeseh Nadimi,

Azadeh Ghalyanchi‐Langeroudi

et al.

Frontiers in Cardiovascular Medicine, Journal Year: 2021, Volume and Issue: 8

Published: March 25, 2021

Coronavirus disease, first detected in late 2019 (COVID-19), has spread fast throughout the world, leading to high mortality. This condition can be diagnosed using RT-PCR technique on nasopharyngeal and throat swabs with sensitivity values ranging from 30 70%. However, chest CT scans X-ray images have been reported of 98 69%, respectively. The application machine learning methods facilitated accurate diagnosis COVID-19. In this study, we reviewed studies which used deep for COVID-19 compared their performance. accuracy these ranged 76% more than 99%, indicating applicability clinical

Language: Английский

Citations

90
COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion DOI Creative Commons
Muhammad Attique Khan, Majed Alhaisoni, Usman Tariq

et al.

Sensors, Journal Year: 2021, Volume and Issue: 21(21), P. 7286 - 7286

Published: Nov. 2, 2021

In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), so on, that can be analyzed by artificial intelligence methods for early diagnosis diseases. Recently, the outbreak COVID-19 disease caused many deaths. Computer vision researchers support doctors employing deep learning techniques on images to diagnose patients. Various were proposed case classification. A new automated technique using parallel fusion optimization models. The starts with contrast enhancement combination top-hat Wiener filters. Two pre-trained models (AlexNet VGG16) are employed fine-tuned according target classes (COVID-19 healthy). Features extracted fused approach—parallel positive correlation. Optimal features selected entropy-controlled firefly method. classified machine classifiers multiclass vector (MC-SVM). Experiments carried out Radiopaedia database achieved an accuracy 98%. Moreover, detailed analysis conducted shows improved performance scheme.

Language: Английский

Citations

78