Diagnostic Test Accuracy of Deep Learning Detection of COVID-19: A Systematic Review and Meta-Analysis

Komolafe, Temitope Emmanuel; Cao, Yuzhu; Nguchu, Benedictor Alexander; Monkam, Patrice; Olaniyi, Ebenezer O.; Sun, Haotian; Zheng, Jian; Yang, Xiaodong

doi:10.1016/j.acra.2021.08.008

Cited by 16 publications

(5 citation statements)

References 47 publications

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“…Therefore, the impact of these 4 aspects on the specificity of DL prediction models needs to be further investigated. In DL model networks, Komolafe et al [ 53 ] found a difference between ResNet and other network models in detecting COVID-19, whereas our study found no significant difference in sensitivity or specificity between ResNet and other network architectures in diagnosing and predicting the severity of COVID-19 ( Table 2 ). This result suggests that, unlike in disease detection, changing the network architecture alone may have little significant impact on DL performance and that factors such as subgroups of sources, training methods, input parameters, and images need to be taken into account.…”

Section: Discussioncontrasting

confidence: 76%

“…After evaluating sensitivity, specificity, and LR together [ 53 ], we found that DL achieved higher sensitivity and specificity in assessing the severity of COVID-19 compared to using CT [ 54 ] or neutrophil-lymphocyte ratio (NLR) alone [ 55 ]. However, DL models for longitudinal prediction of disease severity failed to exclude and confirm patients.…”

Section: Discussionmentioning

confidence: 99%

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Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis

Wang¹,

Liu²,

Tang³

et al. 2023

J Med Internet Res

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Background Deep learning (DL) prediction models hold great promise in the triage of COVID-19. Objective We aimed to evaluate the diagnostic test accuracy of DL prediction models for assessing and predicting the severity of COVID-19. Methods We searched PubMed, Scopus, LitCovid, Embase, Ovid, and the Cochrane Library for studies published from December 1, 2019, to April 30, 2022. Studies that used DL prediction models to assess or predict COVID-19 severity were included, while those without diagnostic test accuracy analysis or severity dichotomies were excluded. QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2), PROBAST (Prediction Model Risk of Bias Assessment Tool), and funnel plots were used to estimate the bias and applicability. Results A total of 12 retrospective studies involving 2006 patients reported the cross-sectionally assessed value of DL on COVID-19 severity. The pooled sensitivity and area under the curve were 0.92 (95% CI 0.89-0.94; I2=0.00%) and 0.95 (95% CI 0.92-0.96), respectively. A total of 13 retrospective studies involving 3951 patients reported the longitudinal predictive value of DL for disease severity. The pooled sensitivity and area under the curve were 0.76 (95% CI 0.74-0.79; I2=0.00%) and 0.80 (95% CI 0.76-0.83), respectively. Conclusions DL prediction models can help clinicians identify potentially severe cases for early triage. However, high-quality research is lacking. Trial Registration PROSPERO CRD42022329252; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD 42022329252

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Section: Discussioncontrasting

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis

Wang¹,

Liu²,

Tang³

et al. 2023

J Med Internet Res

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“…An analysis of 70 PROSPERO protocols was conducted as part of this systematic review, with five being centered on COVID-19. Three studies demonstrated the effectiveness of AI algorithms in tasks such as COVID-19 detection from highresolution CT images [22], patient triaging based on medical imaging [23], and conducting systematic reviews [24]. However, these studies also highlighted considerable limitations related to data heterogeneity and the limited quantity of available data for AI training, indicating a requirement for enhanced future research [23].…”

Section: Covid-19mentioning

confidence: 99%

Artificial Intelligence in Biomedicine: Systematic Review

Gabashvili¹

2023

Preprint

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Artificial Intelligence (AI) is a rapidly progressing technology with its applications expanding exponentially over the past decade. While initial breakthroughs predominantly focused on deep learning and computer vision, recent advancements have facilitated a shift towards natural language processing and beyond. This includes generative models, like ChatGPT, capable of understanding the 'grammar' of software code, analog signals, and molecular structures. This research undertakes a comprehensive examination of AI trends within the biomedical domain, including the impact of ChatGPT. We explore scientific literature, clinical trials, and FDA-approval data, utilizing a thematic synthesis approach and bibliometric mapping of keywords to examine numerous subsets from over a hundred thousand unique records found in prominent public repositories up to mid- July 2023. Our analysis reveals a higher prevalence of general health-related publications compared to more specialized papers using or evaluating ChatGPT. However, the growth in specialized papers suggests a convergence with the trend observed for other AI tools. Our findings also imply a greater prevalence of publications using ChatGPT across multiple medical specialties compared to other AI tools, indicating its rising influence in complex fields requiring interdisciplinary collaboration. Leading topics in AI literature include radiology, ethics, drug discovery, COVID-19, robotics, brain research, stroke, and laparoscopy, indicating a shift from laboratory to emergency medicine and deep-learning-based image processing. Publications involving ChatGPT predominantly address current themes such as COVID-19, practical applications, interdisciplinary collaboration, and risk mitigation. Radiology retains dominance across all stages of biomedical R&D, spanning preprints, peer-reviewed papers, clinical trials, patents, and FDA approvals. Meanwhile, surgery-focused papers appear more frequently within ChatGPT preprints and case reports. Traditionally less represented areas, such as Pediatrics, Otolaryngology, and Internal Medicine, are starting to realize the benefits of ChatGPT, hinting at its potential to spark innovation within new medical sectors. AI application in geriatrics is notably underrepresented in publications. However, ongoing clinical trials are already exploring the use of ChatGPT for managing age-related conditions. The higher frequency of general health-related publications compared to specialized papers employing or evaluating ChatGPT showcases its broad applicability across multiple fields. AI, particularly ChatGPT, possesses significant potential to reshape the future of medicine. With millions of papers published annually across various disciplines, efficiently navigating the information deluge to pinpoint valuable studies has become increasingly challenging. Consequently, AI methods, gaining in popularity, are poised to redefine the future of scientific publishing and its educational reach. Despite challenges like quality of training data and ethical concerns, prevalent in preceding AI tools, the wider applicability of ChatGPT across diverse fields is manifest. This review employed the PRISMA tool and numerous overlapping data sources to minimize bias risks.

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“…Besides diagnosis ML-based applications involve early disease prediction, treatment, outcome prediction and prognosis evaluation, [13] personalized medicine, behaviour variation, drug discovery, manufacturing, clinical trial research, radiology and radiotherapy, smart electronic health records, and epidemic outbreak prediction [14]. In the light of the novel Sars-COV-2 pandemic, AI was successfully used for infection disease surveillance [15].…”

Section: In the Medical Fieldmentioning

confidence: 99%

Expending the power of artificial intelligence in preclinical research: an overview

Diaconu

Cojocaru

Gardikiotis

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Artificial intelligence (AI) is described as the joint set of data entry, able to receive inputs, interpret and learn from such feedbacks, and display related and flexible independent actions that help the entity reach a specific aim over a period of time. By extending its health-care applications continuously, the ultimate AI target is to use machine simulation of human intelligence processes such as learning, reasoning, and self-correction, to mimic human behaviour. AI is extensively used in diverse sectors of medicine, including clinical trials, drug discovery and development, understanding of target-disease associations, disease prediction, imaging, and precision medicine. In this review, we firstly describe the limitations and challenges of the AI tools and techniques utilized in medicine, followed by current uses and applications of AI in the translational field, highlighting the cardio-renal preclinical models with potential to contribute to future clinical research.

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Diagnostic Test Accuracy of Deep Learning Detection of COVID-19: A Systematic Review and Meta-Analysis

Cited by 16 publications

References 47 publications

Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis

Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis

Artificial Intelligence in Biomedicine: Systematic Review

Expending the power of artificial intelligence in preclinical research: an overview

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