Koul Apeksha, Bawa Rajesh K, Kumar Yogesh
Department of Computer Science and Engineering, Punjabi University, Patiala, Punjab India.
Department of Computer Science, Punjabi University, Patiala, Punjab India.
Arch Comput Methods Eng. 2023;30(2):831-864. doi: 10.1007/s11831-022-09818-4. Epub 2022 Sep 28.
Airway disease is a major healthcare issue that causes at least 3 million fatalities every year. It is also considered one of the foremost causes of death all around the globe by 2030. Numerous studies have been undertaken to demonstrate the latest advances in artificial intelligence algorithms to assist in identifying and classifying these diseases. This comprehensive review aims to summarise the state-of-the-art machine and deep learning-based systems for detecting airway disorders, envisage the trends of the recent work in this domain, and analyze the difficulties and potential future paths. This systematic literature review includes the study of one hundred fifty-five articles on airway diseases such as cystic fibrosis, emphysema, lung cancer, Mesothelioma, covid-19, pneumoconiosis, asthma, pulmonary edema, tuberculosis, pulmonary embolism as well as highlights the automated learning techniques to predict them. The study concludes with a discussion and challenges about expanding the efficiency and machine and deep learning-assisted airway disease detection applications.
气道疾病是一个重大的医疗保健问题,每年至少导致300万人死亡。到2030年,它还被认为是全球主要的死亡原因之一。已经进行了大量研究来展示人工智能算法在协助识别和分类这些疾病方面的最新进展。这篇综述旨在总结用于检测气道疾病的基于机器学习和深度学习的先进系统,展望该领域近期工作的趋势,并分析困难和潜在的未来发展方向。这篇系统的文献综述包括对155篇关于气道疾病的文章的研究,如囊性纤维化、肺气肿、肺癌、间皮瘤、新冠肺炎、尘肺病、哮喘、肺水肿、肺结核、肺栓塞等,同时还突出了用于预测这些疾病的自动学习技术。该研究最后讨论了提高效率以及机器学习和深度学习辅助气道疾病检测应用方面的挑战。