Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review.

ABSTRACT:

Purpose

To compare the diagnostic performance of standalone deep learning (DL) algorithms and human experts in lung cancer detection on chest computed tomography (CT) scans.

Materials and methods

This study searched for studies on PubMed, Embase, and Web of Science from their inception until November 2023. We focused on adult lung cancer patients and compared the efficacy of DL algorithms and expert radiologists in disease diagnosis on CT scans. Quality assessment was performed using QUADAS-2, QUADAS-C, and CLAIM. Bivariate random-effects and subgroup analyses were performed for tasks (malignancy classification vs invasiveness classification), imaging modalities (CT vs low-dose CT [LDCT] vs high-resolution CT), study region, software used, and publication year.

Results

SUBMITTER: Wang TW 

PROVIDER: S-EPMC11519296 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature