ARTIFICIAL INTELLIGENCE-ASSISTED FUNCTIONAL ASSESSMENT OF BORDERLINE CORONARY STENOSES IN MOBILE ANGIOGRAPHY: INTEGRATION WITH PERSONALIZED CARDIAC REHABILITATION
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2026
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| _version_ | 1866901919578980352 |
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| author | Fayzievich, Azam Saidov, Maksud Sabirov, Djakhongir |
| author_facet | Fayzievich, Azam Saidov, Maksud Sabirov, Djakhongir |
| contents | <table class="MsoTableGrid"> <tbody> <tr> <td> <p class="17"><em>Background: Borderline coronary stenoses (40–70%) represent a major clinical challenge in interventional cardiology, particularly in mobile angiography settings where invasive functional assessment tools such as Fractional Flow Reserve (FFR) and instant wave-free ratio (iFR) are largely unavailable. Artificial intelligence (AI)-based approaches offer a potential solution for real-time functional triage, yet their applicability in resource-limited mobile environments and integration with downstream cardiac rehabilitation remain unexplored.</em><em> </em></p> </td> </tr> </tbody> </table> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19511626 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | ARTIFICIAL INTELLIGENCE-ASSISTED FUNCTIONAL ASSESSMENT OF BORDERLINE CORONARY STENOSES IN MOBILE ANGIOGRAPHY: INTEGRATION WITH PERSONALIZED CARDIAC REHABILITATION Fayzievich, Azam Saidov, Maksud Sabirov, Djakhongir <table class="MsoTableGrid"> <tbody> <tr> <td> <p class="17"><em>Background: Borderline coronary stenoses (40–70%) represent a major clinical challenge in interventional cardiology, particularly in mobile angiography settings where invasive functional assessment tools such as Fractional Flow Reserve (FFR) and instant wave-free ratio (iFR) are largely unavailable. Artificial intelligence (AI)-based approaches offer a potential solution for real-time functional triage, yet their applicability in resource-limited mobile environments and integration with downstream cardiac rehabilitation remain unexplored.</em><em> </em></p> </td> </tr> </tbody> </table> |
| title | ARTIFICIAL INTELLIGENCE-ASSISTED FUNCTIONAL ASSESSMENT OF BORDERLINE CORONARY STENOSES IN MOBILE ANGIOGRAPHY: INTEGRATION WITH PERSONALIZED CARDIAC REHABILITATION |
| url | https://doi.org/10.5281/zenodo.19511626 |