Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

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

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New trend in artificial intelligence-based assistive technology for thoracic imaging

La radiologia medica, Journal Year: 2023, Volume and Issue: 128(10), P. 1236 - 1249

Published: Aug. 28, 2023

Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with similar that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggered by improvements computing power, algorithm development, use big data. In recent years, application development technology medical field intensified internationally. There doubt will be used clinical practice assist diagnostic imaging future. qualitative diagnosis, desirable develop an explainable at least represents basis process. However, must kept mind physician-assistant system, final decision should made physician while understanding limitations AI. The aim this article review from PubMed database particularly focusing on thorax such as lesion detection diagnosis order help radiologists clinicians become familiar thorax.

Language: Английский

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The Impact of Artificial Intelligence on Microbial Diagnosis

Microorganisms, Journal Year: 2024, Volume and Issue: 12(6), P. 1051 - 1051

Published: May 23, 2024

Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed diagnostics with rapid precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, ensure data integrity. This review examines conventional hurdles, stressing the significance standardized procedures processing. It underscores AI’s significant impact, particularly through machine learning (ML), diagnostics. Recent progressions AI, ML methodologies, are explored, showcasing their influence on categorization, comprehension microorganism interactions, augmentation microscopy capabilities. furnishes a comprehensive evaluation utility diagnostics, addressing both advantages challenges. A few case studies including SARS-CoV-2, malaria, mycobacteria serve illustrate potential for swift diagnosis. Utilization convolutional neural networks (CNNs) digital pathology, automated bacterial classification, colony counting further versatility. Additionally, improves antimicrobial susceptibility assessment contributes disease surveillance, outbreak forecasting, real-time monitoring. Despite limitations, integration microbiology presents robust solutions, user-friendly algorithms, training, promising paradigm-shifting advancements healthcare.

Language: Английский

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Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey

Journal of Imaging, Journal Year: 2024, Volume and Issue: 10(8), P. 176 - 176

Published: July 23, 2024

This paper addresses the significant problem of identifying relevant background and contextual literature related to deep learning (DL) as an evolving technology in order provide a comprehensive analysis application DL specific pneumonia detection via chest X-ray (CXR) imaging, which is most common cost-effective imaging technique available worldwide for diagnosis. particular key period associated with COVID-19, 2020–2023, explain, analyze, systematically evaluate limitations approaches determine their relative levels effectiveness. The context applied both aid automated substitute existing expert radiography professionals, who often have limited availability, elaborated detail. rationale undertaken research provided, along justification resources adopted relevance. explanatory text subsequent analyses are intended sufficient detail being addressed, solutions, these, ranging from more general. Indeed, our evaluation agree generally held view that use transformers, specifically, vision transformers (ViTs), promising obtaining further effective results area using CXR images. However, ViTs require extensive address several limitations, specifically following: biased datasets, data code ease model can be explained, systematic methods accurate comparison, notion class imbalance possibility adversarial attacks, latter remains fundamental research.

Language: Английский

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Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging

Diagnostics, Journal Year: 2024, Volume and Issue: 14(13), P. 1456 - 1456

Published: July 8, 2024

The advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development newer models, AI applications are demonstrating improved performance and versatile utility in clinical setting. Thoracic imaging an area profound interest, given prevalence chest significant health implications thoracic diseases. This review aims to highlight promising within imaging. It examines role AI, including its contributions improving diagnostic evaluation interpretation, enhancing workflow, aiding invasive procedures. Next, it further highlights current challenges limitations faced by such as necessity 'big data', ethical legal considerations, bias representation. Lastly, explores potential directions for application

Language: Английский

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Benefits and harms associated with the use of AI-related algorithmic decision-making systems by healthcare professionals: a systematic review

The Lancet Regional Health - Europe, Journal Year: 2024, Volume and Issue: 48, P. 101145 - 101145

Published: Dec. 1, 2024

Language: Английский

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An overview of reviews on digital health interventions during COVID- 19 era: insights and lessons for future pandemics

Archives of Public Health, Journal Year: 2025, Volume and Issue: 83(1)

Published: May 9, 2025

Language: Английский

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Clinical applications of artificial intelligence in identification and management of bacterial infection: Systematic review and meta-analysis

Saudi Journal of Biological Sciences, Journal Year: 2024, Volume and Issue: 31(3), P. 103934 - 103934

Published: Jan. 18, 2024

Pneumonia is declared a global emergency public health crisis in children less than five age and the geriatric population. Recent advancements deep learning models could be utilized effectively for timely early diagnosis of pneumonia immune-compromised patients to avoid complications. This systematic review meta-analysis PRISMA guidelines selection ten articles included this study. The literature search was done through electronic databases including PubMed, Scopus, Google Scholar from 1st January 2016 till 1 July 2023. Overall studies total 126,610 images 1706 meta-analysis. At 95% confidence interval, pooled sensitivity 0.90 (0.85–0.94) I2 statistics 90.20 (88.56 – 91.92). specificity models' diagnostic accuracy 0.89 (0.86–––0.92) 92.72 (91.50 94.83). showed low heterogeneity across highlighting consistent reliable estimates, instilling these findings researchers healthcare practitioners. study highlighted recent single or combination with high accuracy, sensitivity, ensure use bacterial identification differentiate other viral, fungal adults chest x-rays radiographs.

Language: Английский

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Validated machine learning tools to distinguish immune checkpoint inhibitor, radiotherapy, COVID-19 and other infective pneumonitis

Radiotherapy and Oncology, Journal Year: 2024, Volume and Issue: 195, P. 110266 - 110266

Published: April 5, 2024

BackgroundPneumonitis is a well-described, potentially disabling, or fatal adverse effect associated with both immune checkpoint inhibitors (ICI) and thoracic radiotherapy. Accurate differentiation between inhibitor pneumonitis (CIP) radiation (RP), infective (IP) crucial for swift, appropriate, tailored management to achieve optimal patient outcomes. However, correct diagnosis often challenging, owing overlapping clinical presentations radiological patterns.MethodsIn this multi-centre study of 455 patients, we used machine learning radiomic features extracted from chest CT imaging develop validate five models distinguish CIP RP COVID-19, non-COVID-19 pneumonitis, each other. Model performance was compared that two radiologists.ResultsModels COVID-19 IP out-performed radiologists (test set AUCs 0.92 vs 0.8 0.8; 0.68 0.43 0.4; 0.71 0.55 0.63 respectively). Models were not superior but demonstrated modest performance, test 0.81 respectively. The model performed less well on patients prior exposure ICI radiotherapy (AUC 0.54), though the also had difficulty distinguishing cohort values 0.6 0.6).ConclusionOur results demonstrate potential utility such tools as second concurrent reader support oncologists, radiologists, physicians in cases diagnostic uncertainty. Further research required

Language: Английский

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Artificial neural network based prediction of the lung tissue involvement as an independent in‐hospital mortality and mechanical ventilation risk factor in COVID‐19

Journal of Medical Virology, Journal Year: 2023, Volume and Issue: 95(5)

Published: May 1, 2023

During COVID-19 pandemic, artificial neural network (ANN) systems have been providing aid for clinical decisions. However, to achieve optimal results, these models should link multiple data points simple models. This study aimed model the in-hospital mortality and mechanical ventilation risk using a two step approach combining variables ANN-analyzed lung inflammation data.A set of 4317 hospitalized patients, including 266 patients requiring ventilation, was analyzed. Demographic (including length hospital stay mortality) chest computed tomography (CT) were collected. Lung involvement analyzed trained ANN. The combined then unadjusted multivariate Cox proportional hazards models.Overall associated with ANN-assigned percentage (hazard ratio [HR]: 5.72, 95% confidence interval [CI]: 4.4-7.43, p < 0.001 >50% tissue affected by pneumonia), age category (HR: 5.34, CI: 3.32-8.59 cases >80 years, 0.001), procalcitonin 2.1, 1.59-2.76, 0.001, C-reactive protein level (CRP) 2.11, 1.25-3.56, = 0.004), glomerular filtration rate (eGFR) 1.82, 1.37-2.42, 0.001) troponin 2.14, 1.69-2.72, 0.001). Furthermore, is also ANN-based 13.2, 8.65-20.4, involvement), age, 1.91, 1.14-3.2, 0.14, eGFR 1.2-2.74, 0.004) variables, diabetes 2.5, 1.91-3.27, cardiovascular cerebrovascular disease 3.16, 2.38-4.2, chronic pulmonary 2.31, 1.44-3.7, 0.001).ANN-based strongest predictor unfavorable outcomes in represents valuable support tool

Language: Английский

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