Consistent performance between medical experts and non-expert readers in forced-choice lesion-detection tasks with PET images.

ABSTRACT:

Background

Labeled data are used to train, validate, and test deep learning model observers (DLMOs) as well as linear model observers, such as channelized Hotelling observers (CHOs), for image quality assessment in many imaging modalities, including PET imaging. Ideally, these annotations would come from clinical readers, but these readers are often difficult to access, and it is unclear whether clinical experience is necessary in simple detection tasks involving a known signal profile at a specified location in an image using patient data for backgrounds.

Purpose

We evaluate the potential for using non-medical observers in simple detection tasks to train DLMOs and CHOs. To this end, we compare nuclear medicine physicians to medically naïve readers using a two-alternative fo

SUBMITTER: Abbey CK 

PROVIDER: S-EPMC12546698 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature