Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387032904> ?p ?o ?g. }
- W4387032904 abstract "Abstract Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine. The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education. On this basis, machine learning, particularly deep learning, enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes. This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence (AI) in this field. We focus on each component of deep learning-based lung sound analysis systems, including the task categories, public datasets, denoising methods, and, most importantly, existing deep learning methods, i.e., the state-of-the-art approaches to convert lung sounds into two-dimensional (2D) spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds. Additionally, this review highlights current challenges in this field, including the variety of devices, noise sensitivity, and poor interpretability of deep models. To address the poor reproducibility and variety of deep learning in this field, this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension: https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis ." @default.
- W4387032904 created "2023-09-27" @default.
- W4387032904 creator A5011404538 @default.
- W4387032904 creator A5018393711 @default.
- W4387032904 creator A5021983179 @default.
- W4387032904 creator A5026011614 @default.
- W4387032904 creator A5041568200 @default.
- W4387032904 creator A5048992699 @default.
- W4387032904 date "2023-09-26" @default.
- W4387032904 modified "2023-10-14" @default.
- W4387032904 title "Deep learning-based lung sound analysis for intelligent stethoscope" @default.
- W4387032904 cites W1513801117 @default.
- W4387032904 cites W1654136511 @default.
- W4387032904 cites W1849256066 @default.
- W4387032904 cites W1981293822 @default.
- W4387032904 cites W1984488358 @default.
- W4387032904 cites W1985184135 @default.
- W4387032904 cites W1988292012 @default.
- W4387032904 cites W1990755053 @default.
- W4387032904 cites W1991194449 @default.
- W4387032904 cites W1999768600 @default.
- W4387032904 cites W2011970079 @default.
- W4387032904 cites W2016016168 @default.
- W4387032904 cites W2017041124 @default.
- W4387032904 cites W2017714438 @default.
- W4387032904 cites W2017939241 @default.
- W4387032904 cites W2021218356 @default.
- W4387032904 cites W2021429427 @default.
- W4387032904 cites W2028197266 @default.
- W4387032904 cites W2029309119 @default.
- W4387032904 cites W2036299575 @default.
- W4387032904 cites W2038711987 @default.
- W4387032904 cites W2040211323 @default.
- W4387032904 cites W2042137579 @default.
- W4387032904 cites W2043392634 @default.
- W4387032904 cites W2070146600 @default.
- W4387032904 cites W2071797156 @default.
- W4387032904 cites W2076866543 @default.
- W4387032904 cites W2086113636 @default.
- W4387032904 cites W2093857251 @default.
- W4387032904 cites W2093867321 @default.
- W4387032904 cites W2095366724 @default.
- W4387032904 cites W2103179912 @default.
- W4387032904 cites W2111580864 @default.
- W4387032904 cites W2123958678 @default.
- W4387032904 cites W2124075749 @default.
- W4387032904 cites W2126051824 @default.
- W4387032904 cites W2130683606 @default.
- W4387032904 cites W2133758724 @default.
- W4387032904 cites W2135326113 @default.
- W4387032904 cites W2136892584 @default.
- W4387032904 cites W2147979895 @default.
- W4387032904 cites W2151124172 @default.
- W4387032904 cites W2156242782 @default.
- W4387032904 cites W2159602727 @default.
- W4387032904 cites W2170748564 @default.
- W4387032904 cites W2187643710 @default.
- W4387032904 cites W2192412620 @default.
- W4387032904 cites W2232610944 @default.
- W4387032904 cites W2315864625 @default.
- W4387032904 cites W2327109843 @default.
- W4387032904 cites W2399296970 @default.
- W4387032904 cites W2466330452 @default.
- W4387032904 cites W2526223273 @default.
- W4387032904 cites W2538750502 @default.
- W4387032904 cites W2547512825 @default.
- W4387032904 cites W2593116425 @default.
- W4387032904 cites W2593768305 @default.
- W4387032904 cites W2606912347 @default.
- W4387032904 cites W2615070281 @default.
- W4387032904 cites W2619772334 @default.
- W4387032904 cites W2702739796 @default.
- W4387032904 cites W2754136846 @default.
- W4387032904 cites W2755308984 @default.
- W4387032904 cites W2770437304 @default.
- W4387032904 cites W2773526096 @default.
- W4387032904 cites W2792466658 @default.
- W4387032904 cites W2801920224 @default.
- W4387032904 cites W2806006687 @default.
- W4387032904 cites W2807611011 @default.
- W4387032904 cites W2889291648 @default.
- W4387032904 cites W2891232543 @default.
- W4387032904 cites W2895590231 @default.
- W4387032904 cites W2899245535 @default.
- W4387032904 cites W2899360556 @default.
- W4387032904 cites W2907579173 @default.
- W4387032904 cites W2912223386 @default.
- W4387032904 cites W2920797638 @default.
- W4387032904 cites W2933912245 @default.
- W4387032904 cites W2954103020 @default.
- W4387032904 cites W2962845248 @default.
- W4387032904 cites W2962858109 @default.
- W4387032904 cites W2964439653 @default.
- W4387032904 cites W2964770898 @default.
- W4387032904 cites W2966600143 @default.
- W4387032904 cites W2969103690 @default.
- W4387032904 cites W2992420332 @default.
- W4387032904 cites W3002644008 @default.
- W4387032904 cites W3004806247 @default.
- W4387032904 cites W3004824173 @default.