Detection of cardiac amyloidosis using machine learning on routine echocardiographic measurements
Rachel Si-Wen Chang, I-min Chiu, Phillip Tacon, Michael Abiragi, Louie Cao, Gloria Hong, Jonathan Le, James Zou, Chathuri Daluwatte, Piero Ricchiuto, David Ouyang · 2024
Read Detection of cardiac amyloidosis using machine learning on routine echocardiographic measurements by Rachel Si-Wen Chang, I-min Chiu, Phillip Tacon, Michael Abiragi, Louie Cao, Gloria Hong, Jonathan Le, James Zou, Chathuri Daluwatte, Piero Ricchiuto, David Ouyang (2024) 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-11667434.txt.
Description
Rachel Si-Wen Chang and co-authors report a 2024 study of a learning model that spots a heart disease from routine ultrasound measures. The disease, cardiac amyloidosis, stiffens the heart and is often missed until late. The team trained a forest model on measures taken from standard heart scans.
Thousands of scans from 636 patients at one center fed the work, split by patient into train and test sets. On held-out patients the model split disease from look-alike cases well. Wall and flow measures drove the calls most, and the team notes some routine measures were often missing from reports.
Catalog record
| CREATOR | Rachel Si-Wen Chang, I-min Chiu, Phillip Tacon, Michael Abiragi, Louie Cao, Gloria Hong, Jonathan Le, James Zou, Chathuri Daluwatte, Piero Ricchiuto, David Ouyang |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 25 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |