A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.
A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.
复制标题
使用人工智能对感染 COVID-19 的肺部急性呼吸窘迫综合征的特征进行叙述性回顾。
DOI：
10.1016/j.compbiomed.2021.104210
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发表时间：
2021-03
影响因子：
7.7
通讯作者：
Naidu S
中科院分区：
文献类型：
--
作者：
Suri JS;Agarwal S;Gupta SK;Puvvula A;Biswas M;Saba L;Bit A;Tandel GS;Agarwal M;Patrick A;Faa G;Singh IM;Oberleitner R;Turk M;Chadha PS;Johri AM;Miguel Sanches J;Khanna NN;Viskovic K;Mavrogeni S;Laird JR;Pareek G;Miner M;Sobel DW;Balestrieri A;Sfikakis PP;Tsoulfas G;Protogerou A;Misra DP;Agarwal V;Kitas GD;Ahluwalia P;Teji J;Al-Maini M;Dhanjil SK;Sockalingam M;Saxena A;Nicolaides A;Sharma A;Rathore V;Ajuluchukwu JNA;Fatemi M;Alizad A;Viswanathan V;Krishnan PK;Naidu S
截至 2020 年 12 月 21 日，COVID-19 已感染全球 7740 万人，并造成 170 万人死亡。COVID-19 导致的主要原因是急性呼吸窘迫综合征 (ARDS)。根据世界卫生组织 (WHO) 的数据，年满 60 岁或患有主要目标合并症的人感染 SARS-CoV-2 的风险最高。在当前持续的大流行期间，医学成像为诊断提供了一种非侵入性、非接触式且相对更安全的替代工具。人工智能 (AI) 科学家正在开发多种多种成像模式的智能计算机辅助诊断 (CAD) 工具，即肺部计算机断层扫描 (CT)、胸部 X 光检查和肺部超声检查。这些人工智能工具通过以下方式帮助肺部和重症监护临床医生：(a) 更快地检测病毒的存在，(b) 对肺炎类型进行分类，以及 (c) 测量 COVID-19 感染患者的病毒损伤的严重程度。因此，充分了解快速、成功、及时的肺部扫描分析的要求至关重要。本叙述性回顾首先介绍了 COVID-19 场景中肺部的病理布局，然后理解并解释了 ARDS 框架中的共病统计分布。这篇评论的新颖之处在于按照思想流派（SoT）对人工智能模型进行分类的方法，并根据技术及其特征的分离进行展示。该研究还讨论了 AI 模型的识别及其从非 ARDS 肺部（COVID-19 之前）到 ARDS 肺部（COVID-19 之后）的扩展。此外，它还介绍了 COVID-19 框架中医学成像模式的人工智能工作流程注意事项。最后，将讨论临床人工智能设计考虑因素。我们的结论是，通过将合并症视为一个独立因素，可以改进当前现有人工智能模型的设计。此外，ARDS 后处理临床系统必须涉及 (i) AI 模型的临床验证和验证，(ii) 可靠性和稳定性标准，(iii) 易于适应，以及 (iv) 对 AI 系统在肺部、重症监护和放射环境中的使用进行概括评估。
COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.
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影响因子：
20.1
作者：
Ambale-Venkatesh B;Yang X;Wu CO;Liu K;Hundley WG;McClelland R;Gomes AS;Folsom AR;Shea S;Guallar E;Bluemke DA;Lima JAC
通讯作者：
Lima JAC
影响因子：
4.6
作者：
Aresta, Guilherme;Jacobs, Colin;Campilho, Aurelio
通讯作者：
Campilho, Aurelio
影响因子：
6.1
作者：
Biswas, Mainak;Kuppili, Venkatanareshbabu;Suri, Jasjit S.
通讯作者：
Suri, Jasjit S.
DOI：
10.1007/s42399-020-00569-6
发表时间：
2020
期刊：
SN comprehensive clinical medicine
影响因子：
--
作者：
Al-Wahaibi K;Al-Wahshi Y;Mohamed Elfadil O
通讯作者：
Mohamed Elfadil O
影响因子：
3.8
作者：
Acharya, U. Rajendra;Sree, S. Vinitha;Suri, Jasjit S.
通讯作者：
Suri, Jasjit S.