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Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective.

ABSTRACT: SARS-CoV-2 has infected over ∼165 million people worldwide causing Acute Respiratory Distress Syndrome (ARDS) and has killed ∼3.4 million people. Artificial Intelligence (AI) has shown to benefit in the biomedical image such as X-ray/Computed Tomography in diagnosis of ARDS, but there are limited AI-based systematic reviews (aiSR). The purpose of this study is to understand the Risk-of-Bias (RoB) in a non-randomized AI trial for handling ARDS using novel AtheroPoint-AI-Bias (AP(ai)Bias). Our hypothesis for acceptance of a study to be in low RoB must have a mean score of 80% in a study. Using the PRISMA model, 42 best AI studies were analyzed to understand the RoB. Using the AP(ai)Bias paradigm, the top 19 studies were then chosen using the raw-cutoff of 1.9. This was obtained using the intersection of the cumulative plot of "mean score vs. study" and score distribution. Finally, these studies were benchmarked against ROBINS-I and PROBAST paradigm. Our observation showed that AP(ai)Bias, ROBINS-I, and PROBAST had only 32%, 16%, and 26% studies, respectively in low-moderate RoB (cutoff>2.5), however none of them met the RoB hypothesis. Further, the aiSR analysis recommends six primary and six secondary recommendations for the non-randomized AI for ARDS. The primary recommendations for improvement in AI-based ARDS design inclusive of (i) comorbidity, (ii) inter-and intra-observer variability studies, (iii) large data size, (iv) clinical validation, (v) granularity of COVID-19 risk, and (vi) cross-modality scientific validation. The AI is an important component for diagnosis of ARDS and the recommendations must be followed to lower the RoB.

SUBMITTER: Suri JS

PROVIDER: S-EPMC8843049 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective.

Suri Jasjit S JS Agarwal Sushant S Gupta Suneet S Puvvula Anudeep A Viskovic Klaudija K Suri Neha N Alizad Azra A El-Baz Ayman A Saba Luca L Fatemi Mostafa M Naidu D Subbaram DS

IEEE journal of biomedical and health informatics 20211105 11

SARS-CoV-2 has infected over ∼165 million people worldwide causing Acute Respiratory Distress Syndrome (ARDS) and has killed ∼3.4 million people. Artificial Intelligence (AI) has shown to benefit in the biomedical image such as X-ray/Computed Tomography in diagnosis of ARDS, but there are limited AI-based systematic reviews (aiSR). The purpose of this study is to understand the Risk-of-Bias (RoB) in a non-randomized AI trial for handling ARDS using novel AtheroPoint-AI-Bias (AP(ai)Bias). Our hyp ...[more]

PMID: 34379599

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Project description:BackgroundAcute respiratory distress syndrome (ARDS) is a serious threat to human life. Hence, early and accurate diagnosis and treatment are crucial for patient survival. This meta-analysis evaluates the accuracy of artificial intelligence in the early diagnosis of ARDS and provides guidance for future research and applications.MethodsA search on PubMed, Embase, Cochrane, Web of Science, CNKI, Wanfang, Chinese Biomedical Literature (CBM), and VIP databases was systematically conducted, from their establishment to November 2023, to obtain eligible studies for the analysis and evaluation of the predictive effect of AI on ARDS. The retrieved literature was screened according to inclusion and exclusion criteria, the quality of the included studies was assessed using QUADAS-2, and the included studies were statistically analyzed.ResultsAmong the 2, 996 studies, 33 were included in this meta-analysis, which showed that the pooled sensitivity of AI in predicting ARDS was 0.81 (0.76-0.85), the pooled specificity was 0.88 (0.84-0.91), and the area under the receiver operating characteristic curve (AUC) was 0.91 (0.88-0.93). The analyzed studies included 28 models, with a pooled sensitivity of 0.79 (0.76-0.82), a pooled specificity of 0.85 (0.83-0.88), and an AUC of 0.89 (0.86-0.91). In the subgroup analysis, the pooled AUC of the AI models ANN, CNN, LR, RF, SVM, and XGB were 0.86 (0.83-0.89), 0.91 (0.88-0.93), 0.86 (0.83-0.89), and 0.89 (0.86-0.91), 0.90 (0.87-0.92), 0.93 (0.90-0.95), respectively. In an additional subgroup analysis, we evaluated the predictive performance of the AI models trained using different predictors. This meta-analysis was registered in PROSPERO (CRD42023491546).ConclusionAI has good sensitivity and specificity for predicting ARDS, indicating a high clinical application value. Algorithmic models such as CNN, SVM, and XGB have improved prediction performance. The subgroup analysis revealed that the model trained using images combined with other predictors had the best predictive performance.

Dataset Information

Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective.

Publications

Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective.

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