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Abstract
Abstract: Research carried out in the field of digital processing algorithms of artificial intelligence and biomedical signals sets the stage for a radical transformation of modern medicine and the health system. Biomedicine signaling is a complex set of data that reflects the various physiological processes of the human body. By properly analyzing these signals and working them efficiently, early disease detection, diagnostic quality enhancement, and individual treatment strategies can be developed. Artificial intelligence technologies are creating new opportunities in the analysis of these complex signals.
References
1. Aminizadeh S., Heidari A., Dehghan M., Toumaj S., Rezaei M., Navimipour N.J., Stroppa F., Unal M. Opportunities and Challenges of Artificial Intelligence and Distributed Systems to Improve the Quality of Healthcare Service. Artif. Intell. Med. 2024;149:102779. doi: 10.1016/j.artmed.2024.102779. [DOI] [PubMed] [Google Scholar]
2.Ahmadi A., Ganji N.R. AI-Driven Medical Innovations: Transforming Healthcare through Data Intelligence. Int. J. BioLife Sci. 2023;2:132–142. doi: 10.22034/IJBLS.2023.185475. [DOI] [Google Scholar]
3.Ieracitano C., Zhang X. Editorial Topical Collection: “Biomedical Imaging and Data Analytics for Disease Diagnosis and Treatment”. Bioengineering. 2024;11:726. doi: 10.3390/bioengineering11070726. [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Lian S., Luo Z. Cutting-Edge Machine Learning in Biomedical Image Analysis: Editorial for Bioengineering Special Issue: “Recent Advance of Machine Learning in Biomedical Image Analysis”. Bioengineering. 2024;11:1106. doi: 10.3390/bioengineering11111106. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Kaviri S.M., Vinjamuri R. Integrating Electroencephalography Source Localization and Residual Convolutional Neural Network for Advanced Stroke Rehabilitation. Bioengineering. 2024;11:967. doi: 10.3390/bioengineering11100967. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Li X., Sakevych M., Atkinson G., Metsis V. BioDiffusion: A Versatile Diffusion Model for Biomedical Signal Synthesis. Bioengineering. 2024;11:299. doi: 10.3390/bioengineering11040299. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Cheng C.H., Yuen Z., Chen S., Wong K.L., Chin J.W., Chan T.T., So R.H.Y. Contactless Blood Oxygen Saturation Estimation from Facial Videos Using Deep Learning. Bioengineering. 2024;11:251. doi: 10.3390/bioengineering11030251. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Hwang C.S., Kim Y.H., Hyun J.K., Kim J.H., Lee S.R., Kim C.M., Nam J.W., Kim E.Y. Evaluation of the Photoplethysmogram-Based Deep Learning Model for Continuous Respiratory Rate Estimation in Surgical Intensive Care Unit. Bioengineering. 2023;10:1222. doi: 10.3390/bioengineering10101222. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Park J.E., Kim D.Y., Park J.W., Jung Y.J., Lee K.S., Park J.H., Sheen S.S., Park K.J., Sunwoo M.H., Chung W.Y. Development of a Machine Learning Model for Predicting Weaning Outcomes Based Solely on Continuous Ventilator Parameters during Spontaneous Breathing Trials. Bioengineering. 2023;10:1163. doi: 10.3390/bioengineering10101163. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Zhao Q., Liu F., Song Y., Fan X., Wang Y., Yao Y., Mao Q., Zhao Z. Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model. Bioengineering. 2023;10:1024. doi: 10.3390/bioengineering10091024. [DOI] [PMC free article] [PubMed] [Google Scholar]