CHUQUR O‘QITISH YORDAMIDA EXOKARDIOGRAMMA TASVIRLARINI TASNIFLASH

Mehriddin Raximov; Nigora Djurayeva

Authors

Raximov Mehriddin Fazliddinovich Muhammad al-Xorazmiy nomidagi TATU
Djurayeva Nigora Soibjon qizi Muhammad al-Xorazmiy nomidagi TATU

Keywords:

exokardiogramma, mashinali o‘qitish, chuqur o‘qitish, sun’iy intellekt (AI), konvolyutsion neyron tarmoqlari, tasvir, ma’lumotlar bazasi

Abstract

Exokardiografiya zamonaviy kardiologiyada muhim ahamiyatga ega. Biroq, inson tomonidan talqin qilinishi aniq va standartlashtirilgan yuqori samarali tahlilni cheklaydi, bu esa exokardiografiyaning aniq tibbiyot uchun klinik va ilmiy salohiyatini to‘liq ro‘yobga chiqarishiga to‘sqinlik qiladi. Ushbu tadqiqotda biz chuqur o‘qitish texnologiyasining exokardiografik ko‘rish turlarini aniqlashda qo‘llanishini ko‘rsatdik — bunda model 15 ta asosiy transtorasik exokardiogramma (UTT) ko‘rishlarini mutaxassis darajasida aniqlay oldi.

References

Ma, L., Wang, Z., Fan, J., Hu, S.: Interpretation of report on cardiovascular health and diseases in china 2022. Chinese General Practice 26(32), 3975 (2023) https://doi.org/10.12114/j.issn.1007-9572.2023.0408

Amirata Ghorbani, et al. “Deep learning interpretation of echocardiograms”, npj Digital Medicine (2020), pp.1-14.

Nasimov, R., Muminov, B., Mirzahalilov, S., & Nasimova, N. “Algorithm of Automatic Differentiation of Myocardial Infarction from Cardiomyopathy based on Electrocardiogram.” 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT), pp. 1-5.

Luo C., Li X., Wang L., He J., Li D. and Zhou J.How Does the Data set Affect CNN-based Image Classification Performance // 5th International Conference on Systems and Informatics, - 2018, -P. 361-366

Suzuki K. Artificial neural networks – architectures and applications // InTech Publisher, Rijeka, Croatia, - 2013, - 264 p.

Haya A.Convolutional Neural Network Application in Biomedical Signals // Journal of Computer Science and Information Technology, - 2018, - vol. 6, no.2, -P. 45

Kim J.,Shin H. Simple and Robust Realtime QRS Detection Algorithm Based on Spatiotemporal Characteristic of the QRS Complex // PLOS ONE researchjournal, - 2016, - vol.13, no 3, - P.1-13

Wilkins E, Wilson L, Wickramasinghe K, Bhatnagar P, Leal J, LuengoFernandez R, et al. European Cardiovascular Disease Statistics 2017. Brussels: European Heart Network (2017).

Shengfeng Liu, Yi Wang, Xin Yang, Baiying Lei, Li Liu, Shawn Xiang Li, Dong Ni, Tianfu Wang, “Deep Learning in Medical Ultrasound Analysis: A Review”, Engineering 5 (2019) 261–275

Kenya Kusunose, “Steps to use artifcial intelligence in echocardiography”, Journal of Echocardiography (2021) 19:21–27

Zeynettin Akkus *, Yousof H. Aly, Itzhak Z. Attia, Francisco Lopez-Jimenez, Adelaide M. Arruda-Olson, Patricia A. Pellikka, Sorin V. Pislaru, Garvan C. Kane, Paul A. Friedman and Jae K. Oh, “Review Artificial Intelligence (AI)-Empowered Echocardiography Interpretation: A State-of-the-Art Review”, Journal of Clinical Medicine, 2021, 10, 1391.