Sathyakumar Kaviya, Munoz Michael, Singh Jaikaran, Hussain Nowair, Babu Benson A
Family Medicine, University of Florida College of Medicine, Gainesville, USA.
Pediatrics, Monmouth Medical Center, Long Branch, USA.
Cureus. 2020 Aug 25;12(8):e10017. doi: 10.7759/cureus.10017.
Lung cancer is the number one cause of cancer-related deaths in the United States as well as worldwide. Radiologists and physicians experience heavy daily workloads, thus are at high risk for burn-out. To alleviate this burden, this narrative literature review compares the performance of four different artificial intelligence (AI) models in lung nodule cancer detection, as well as their performance to physicians/radiologists reading accuracy. A total of 648 articles were selected by two experienced physicians with over 10 years of experience in the fields of pulmonary critical care, and hospital medicine. The data bases used to search and select the articles are PubMed/MEDLINE, EMBASE, Cochrane library, Google Scholar, Web of science, IEEEXplore, and DBLP. The articles selected range from the years between 2008 and 2019. Four out of 648 articles were selected using the following inclusion criteria: 1) 18-65 years old, 2) CT chest scans, 2) lung nodule, 3) lung cancer, 3) deep learning, 4) ensemble and 5) classic methods. The exclusion criteria used in this narrative review include: 1) age greater than 65 years old, 2) positron emission tomography (PET) hybrid scans, 3) chest X-ray (CXR) and 4) genomics. The model performance outcomes metrics are measured and evaluated in sensitivity, specificity, accuracy, receiver operator characteristic (ROC) curve, and the area under the curve (AUC). This hybrid deep-learning model is a state-of-the-art architecture, with high-performance accuracy and low false-positive results. Future studies, comparing each model accuracy at depth is key. Automated physician-assist systems as this model in this review article help preserve a quality doctor-patient relationship.
肺癌是美国乃至全球癌症相关死亡的首要原因。放射科医生和内科医生日常工作量繁重，因此面临着很高的职业倦怠风险。为了减轻这一负担，本文献综述比较了四种不同人工智能（AI）模型在肺结节癌症检测中的表现，以及它们与医生/放射科医生读片准确性的对比。两位在肺部重症监护和医院医学领域拥有超过10年经验的资深医生共筛选出648篇文章。用于搜索和筛选文章的数据库有PubMed/MEDLINE、EMBASE、Cochrane图书馆、谷歌学术、科学网、IEEE Xplore和DBLP。所选文章的时间跨度为2008年至2019年。根据以下纳入标准从648篇文章中筛选出4篇：1）年龄在18至65岁之间；2）胸部CT扫描；2）肺结节；3）肺癌；3）深度学习；4）集成方法；5）经典方法。本叙述性综述使用的排除标准包括：1）年龄大于65岁；2）正电子发射断层扫描（PET）混合扫描；3）胸部X光（CXR）；4）基因组学。模型性能结果指标通过灵敏度、特异性、准确性、受试者操作特征（ROC）曲线和曲线下面积（AUC）进行测量和评估。这种混合深度学习模型是一种先进的架构，具有高性能准确性和低假阳性结果。未来的研究，深入比较每个模型的准确性是关键。本文综述中的这种模型作为自动化医生辅助系统有助于维持良好的医患关系。
