We used GPT4.1 to extract a binary macro-vascular-disease label from more than 7000 Dutch CT Heart reports
in the University Medical Center Utrecht.
In parallel we developed a heuristics pipeline based on CADRADS>2, MESA>65%, CACS>400
and a stenosis/occlusion>=50%, together with regular expressions regarding the macro-vascular disease level.
This model is trained on the labels extracted with GPT-4.1, these labels were consistent with the heuristics-based labeling pipeline, with a correlation score of ahout
about 0.83(Pearson).
In 10-fold cross-validation the model scored f1,precision and recall of about 93%, the model uploaded here was 1 fold from a 40-fold split, and obtained 90% (f1,prec,rec).
Note: this scoring was obtained with the default 0.5 proba threshold.
