Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective.
IEEE J Biomed Health Inform
; 25(11): 4128-4139, 2021 11.
Article
in English
| MEDLINE | ID: covidwho-1352392
ABSTRACT
SARS-CoV-2 has infected over â¼165 million people worldwide causing Acute Respiratory Distress Syndrome (ARDS) and has killed â¼3.4 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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Respiratory Distress Syndrome
/
COVID-19
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
/
Randomized controlled trials
/
Reviews
/
Systematic review/Meta Analysis
Limits:
Humans
Language:
English
Journal:
IEEE J Biomed Health Inform
Year:
2021
Document Type:
Article
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