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.

中文翻译：

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人工智能在 COVID-19 肺部患者急性呼吸窘迫综合征中的系统评价：生物医学成像视角


SARS-CoV-2 已感染全球约 1.65 亿人，导致急性呼吸窘迫综合征 (ARDS)，并已导致约 340 万人死亡。人工智能 (AI) 已显示出在生物医学图像（如 X 射线/计算机断层扫描）诊断 ARDS 方面的优势，但基于人工智能的系统评价 (aiSR) 有限。本研究的目的是了解使用新型 AtheroPoint-AI-Bias (AP(ai)Bias) 处理 ARDS 的非随机 AI 试验中的偏倚风险 (RoB)。我们假设接受低 RoB 的研究的平均分数必须为 80%。使用 PRISMA 模型，对 42 项最佳人工智能研究进行了分析，以了解 RoB。使用 AP(ai)Bias 范式，然后使用原始截止值 1.9 选择前 19 项研究。这是使用“平均分数与研究”的累积图和分数分布的交集获得的。最后，这些研究以 ROBINS-I 和 PROBAST 范式为基准。我们的观察显示，AP(ai)Bias、ROBINS-I 和 PROBAST 在中低 RoB 中的研究分别只有 32%、16% 和 26%（cutoff>2.5），但没有一个达到 RoB假设。此外，aiSR 分析为 ARDS 的非随机 AI 提出了六项主要建议和六项次要建议。改进基于人工智能的 ARDS 设计的主要建议包括 (i) 合并症、(ii) 观察者间和观察者内部变异性研究、(iii) 大数据量、(iv) 临床验证、(v) COVID-19 的粒度19 风险，以及 (vi) 跨模式科学验证。 AI 是诊断 ARDS 的重要组成部分，必须遵循建议以降低 RoB。