Conformal coronary calcification volume estimation with conditional coverage via histogram clustering

TL;DR

This paper introduces a cluster-based conformal prediction method for estimating coronary calcium scores in CT scans, providing calibrated intervals that improve triage accuracy without retraining, aiding early clinical decision-making.

Contribution

It presents a novel cluster-based conformal prediction framework that calibrates predictive intervals for coronary calcium scoring models without retraining, enhancing reliability and clinical utility.

Findings

Achieved calibrated coverage with various segmentation models.

Improved triage metrics over conventional conformal prediction.

Provided meaningful confidence intervals for clinical risk assessment.

Abstract

Incidental detection and quantification of coronary calcium in CT scans could lead to the early introduction of lifesaving clinical interventions. However, over-reporting could negatively affect patient wellbeing and unnecessarily burden the medical system. Therefore, careful considerations should be taken when automatically reporting coronary calcium scores. A cluster-based conditional conformal prediction framework is proposed to provide score intervals with calibrated coverage from trained segmentation networks without retraining. The proposed method was tuned and used to calibrate predictive intervals for 3D UNet models (deterministic, MCDropout and deep ensemble) reaching similar coverage with better triage metrics compared to conventional conformal prediction. Meaningful predictive intervals of calcium scores could help triage patients according to the confidence of their risk category prediction.