Cardiology Diagnosis Automation Zhurek AI
| dc.contributor.advisor | Fazli, Siamac | |
| dc.contributor.advisor | Abilgazym, Aibek | |
| dc.contributor.author | Abdimalinov, Ablay | |
| dc.contributor.author | Aimuratova, Amina | |
| dc.contributor.author | Muratkhanova, Zhazelya | |
| dc.contributor.author | Kenbayeva, Ariana | |
| dc.contributor.author | Amanova, Kymbat | |
| dc.date.accessioned | 2026-06-09T05:38:16Z | |
| dc.date.issued | 2026-04-20 | |
| dc.description.abstract | The leading cause of deaths in Kazakhstan is Cardiovascular disease(CDV). Some revisions were made in international heart-failure guidelines since 2016, but new diagnostic standards are adopted slowly in primary care, especially outside main cities [1]. This gap is caused by a noticeable shortage of specialists capable of interpreting echocardiographic (echo) studies. Based on this, the team identified three key objectives: 1. Lessen the reliance on specialists by automating echocardiographic analysis. 2. Lower the amount of misinterpretation of important cardiac measurements through AI-driven report generation. 3. Ensure clinical transparency through explainable and verifiable AI outputs that clinicians can trust. To address these objectives the team built Zhurek AI, an AI-powered web platform for echocardiography analysis. The platform creates structured clinical reports covering ejection fraction, valvular evaluation, wall motion, and pericardial findings. It is powered by EchoPrime [2], a multi-video view-informed vision-language model trained on 12.1 million echocardiographic clips, which achieves a mean AUC of 0.85–0.92 and exceeds human cardiologists on several tasks. | |
| dc.identifier.citation | Abdimalinov, A., Aimuratova, A., Muratkhanova, Z., Kenbayeva, & A., Amanova, K.(2026). Cardiology diagnosis automation: Zhurek AI [Unpublished senior project]. Nazarbayev University School of Engineering and Digital Sciences | |
| dc.identifier.uri | https://nur.nu.edu.kz/handle/123456789/18905 | |
| dc.language.iso | en_US | |
| dc.publisher | Nazarbayev University School of Engineering and Digital Sciences | |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | |
| dc.subject | Cardiovascular Disease | |
| dc.subject | AI | |
| dc.subject | Grad-CAM | |
| dc.title | Cardiology Diagnosis Automation Zhurek AI | |
| dc.type | Bachelor's Capstone project |