Artificial Intelligence for the Diagnosis of Transthyretin Amyloid Cardiomyopathy: A Systematic Review of Machine Learning Application
Eyad Jamileh, Ahmed T. Elmewafy, Muhammad Abdullah Qadeer, Zuhaib Zulfiqar, Kiran Kang, Kaveh Hosseini, Ibrahim Antoun · 2026
Read Artificial Intelligence for the Diagnosis of Transthyretin Amyloid Cardiomyopathy: A Systematic Review of Machine Learning Application by Eyad Jamileh, Ahmed T. Elmewafy, Muhammad Abdullah Qadeer, Zuhaib Zulfiqar, Kiran Kang, Kaveh Hosseini, Ibrahim Antoun (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13586670.txt.
Description
Seven reviewers gathered the evidence on artificial intelligence tools for spotting a stiff-heart disease called ATTR-CM, in a review published in 2026. The disease, caused by protein deposits in the heart, is often missed until late. New drugs that slow it make early detection worth more than before. The team searched five databases for studies that used machine learning on routine heart data for this task.
Twenty-eight reports covering more than one hundred twenty thousand entries entered the review. Accuracy varied widely by data type, with heart tracing models scoring lowest and nuclear imaging models often scoring highest. Many studies tested their tools only on their own rich data sets, with few events and tuned cutoffs. The authors warn that headline scores do not equal clinic-ready performance. They call for forward-looking tests across many centers before such tools enter daily care.
Catalog record
| CREATOR | Eyad Jamileh, Ahmed T. Elmewafy, Muhammad Abdullah Qadeer, Zuhaib Zulfiqar, Kiran Kang, Kaveh Hosseini, Ibrahim Antoun |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 62 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |