Биология ва тиббиёт муаммолари 2025, №4.1 (164)

Maqola mavzusi

ЭХОКАРДИОГРАФИК ТАСВИРЛАР АСОСИДА ЮРАКНИНГ MRT ТАСВИРЛАРИНИ СУНЪИЙ ИНТЕЛЛЕКТ ОРҚАЛИ РЕКОНСТРУКЦИЯ ҚИЛИШ ЁРДАМИДА ЮРАК КАСАЛЛИКЛАРИНИНГ ЭРТА ДИАГНОСТИКАСИНИ ТАКОМИЛЛАШТИРИШ (9-13)

Mualliflar

Абдуллаев Иброҳимжон Ниғматилла ўғли, Насимов Рашид Ҳамид ўғли, Жиянбаев Отабек Эшдавлатович

Muassasa

1 - Тиббиет ходимларининг касбий малакасини ошириш маркази, Ўзбекистон Республикаси, Тошкент ш.; 2 - Тошкент давлат иқтисодиёт университети, Ўзбекистон Республикаси, Тошкент ш.

Annotatsiya

Мазкур мақолада юрак касалликларини эрта аниқлаш мақсадида эхокардиографик (ECHO) тасвирлардан юракнинг магнит-резонанс томографияси (MRT) тасвирларини сунъий интеллект ёндошуви асосида реконструк-ция қилиш технологияси ёритилган. Тадқиқотда ECHO тасвирларининг диагностик аҳамияти таҳлил қилиниб, уларнинг асосида чуқур ўрганишга асосланган GAN (Generative Adversarial Network) моделлар ёрдамида MRT форматдаги тасвирларни яратиш имкониятлари кўриб чиқилган. Ушбу ёндошув юрак мушакларининг анатомик ва функционал хусусиятларини чуқурроқ таҳлил қилишга имконият беради ҳамда MRT ускунаси мавжуд бўлмаган шароитларда муқобил диагностика усули сифатида фойдаланиш имкониятини очади. Тадқиқот натижалари юрак патологияларини эрта аниқлаш ва профилактик чораларни ўз вақтида кўришда сунъий интеллектдан самарали фойдаланиш имкониятларини намоён этади

Kalit so'zlar

Юрак касалликлари, ECHO, MRT, сунъий интеллект, GAN, реконструкция, эрта диагностика.

Adabiyotlar

1. Ouyang D, He B, Ghorbani A, et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature, 2020; 580(7802): 252–256. 2. Zhang J, Gajjala S, Agrawal P, et al. Fully Automated Echocardiogram Interpretation in Clinical Practice. Circulation, 2018; 138(16): 1623–1635. 3. Chartsias A, Joyce T, Dharmakumar R, et al. Adversarial image synthesis for unpaired multi-modal cardiac data. Med Image Anal, 2019; 54: 1–13. 4. Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Advances in neural information processing systems, 2014; 27. 5. Wolterink JM, Dinkla AM, Savenije MH, et al. Deep MR to CT synthesis using unpaired data. IEEE transactions on medical imaging, 2017; 36(11): 2276–2285. 6. Mirametov A.B., Abdullayev I.N., Nazirov R.M., Tashev B.J. Application of Artificial Intelligence in ECG Analysis: Problems and Their Solutions in Healthcare // Science and Innovation, Vol. 3, Issue 3. – March 2024. – P. 110–115. 7. Abdullayev I.N., Shakarov F.Q., Umarova D.A. Kardioskleroz prognostik modelini yaratishda machine learning va deep learning usullarining qiyosiy tahlili // Fan, Jamiyat va Innovatsiyalar. – 2025. – T. 2. – №19. – B. 22–24. 8. Jiyanbayev O.E., Abdullayev I.N. Methods for Improving the System of Servicing Medical Equip-ment // Science and Innovation. – 2025. – Vol. 4. – Issue 2. – P. 83–85. 9. Abdullayev I.N., Karabayeva L.X., Yusupova N.S. Miokard kardiosklerozi tashxisida sun’iy neyron tarmoqlarining qo‘llanilishi: zamonaviy yondashuvlar // ScienceResearch.com. – 2025. – B. 103–105. 10. Jiyanbayev O.E., Abdullayev I.N. Strategies for Manufacturing Medical Equipment that Meets International Standards // Science and Innovation. – 2025. – Vol. 4. – Issue 2. – P. 78–80. 11. Abdullayev I.N., Tashev B.J., Mirametov A.B. Sun’iy intellekt yordamida yurak kasalliklarini prognozlash modellari ishonchliligi // Fan, Jamiyat va Innovatsiyalar. – 2025. – T. 2. – №19. – B. 16–17. 12. Abdullayev I.N., Yusupova N.S., Tashev B.J. Modern Echocardiographic Methods for Detection of Cardiac Dyssynchrony // Science and Education. – 2025. – Vol. 6. – Issue 2. – P. 75–77. 13. Jiyanbayev O.E., Abdullayev I.N. Effective Resource Management in Medical Facilities Through Artificial Intelligence // International Journal of Medical Sciences and Clinical Research. – 2024. – Vol. 4. – Issue 7. – P. 39–43. 14. Magrupov T.M., Nazirov R.M., Abdullayev I.N. Formation of a Database of Lung Disease Sound Signals // Science and Innovation. – 2024. – Vol. 3. – Issue 9. – P. 90–92. 15. Abdullayev I.N., Yunusxo‘jayeva M.Z., Elmurotova D.B. Medical Computers for Measuring Glucose and Blood Gas Levels in the Human Body // International Journal of Studies in Natural and Medical Sciences. – 2023. – Vol. 2. – Issue 5. – P. 121–123. 16. Nematov Sh.Q., Kamolova Y.M., Abdullayev I.N. Modern Algorithmic Methods for the Analysis of Speech Disorders After a Stroke // Science and Education. – 2023. – Vol. 4. – Issue 6. – P. 452–453. 17. Dar SU, Yurt M, Karacan L, et al. Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE transactions on medical imaging, 2019; 38(10): 2375–2388. 18. Sudre CH, Li W, Vercauteren T, et al. Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 2017: 240–248. 19. Jiyanbayev , O., & Abdullayev , I. (2025). Tibbiyot muassasalarida tibbiy jihozlarni profilaktik texnik xizmat ko‘rsatish tizimini takomillashtirish. Universal Xalqaro Ilmiy Jurnal, 2(4.5), 430–432. Retrieved from https://inlibrary.uz/index.php/universaljurnal/article/view/111647 20. Isensee F, Jaeger PF, Kohl SAA, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 2021; 18: 203–211. 21. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015: 234–241. 22. Bai W, Sinclair M, Tarroni G, et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. Journal of Cardiovascular Magnetic Resonance, 2018; 20(1): 65.