Clinical value of deep learning image reconstruction in chest computed tomography (CT) imaging: a systematic review

AIM Computed tomography (CT) plays a central role in thoracic imaging, but maintaining diagnostic image quality at reduced doses remains a challenge. Filtered back projection (FBP) produces high noise, and iterative reconstruction (IR) reduces noise but alters image texture at low dose. Deep learning image reconstruction (DLIR) suppresses noise while preserving detail, yet its diagnostic performance in chest CT remains unclear. This review aimed to evaluate the clinical diagnostic value of DLIR in chest CT imaging. MATERIALS AND METHODS A systematic review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (International Prospective Register of Systematic Reviews [PROSPERO] registered). The following databases were searched for studies comparing DLIR with IR/FBP in chest CT: PubMed, Embase, Scopus, Web of Science, IEEE, and Cochrane Library. Eligible studies included human participants and reported diagnostic or image-quality outcomes. Quality assessment was performed using the QUADAS-2 tool. Given outcome heterogeneity, results were synthesised qualitatively using effect direction plots and sign tests. RESULTS From 1,967 records, 13 studies met the inclusion criteria. DLIR demonstrated superior diagnostic performance compared with IR/FBP and showed higher sensitivity for nodule detection (up to 96.9%), improved area under the curve (AUC) for lung texture analysis (0.97–1.0 vs 0.91–0.97 with hybrid IR), and stronger interobserver agreement for interstitial lung disease (ILD) pattern classification (κ up to 0.992). DLIR achieved substantial dose reductions (up to 97%) and faster reconstruction times while maintaining diagnostic consistency. CONCLUSION DLIR demonstrates noninferior to superior diagnostic performance compared with FBP/IR, supporting its role in routine chest CT. Large-scale studies remain essential to establish its impact on patient outcomes and guide clinical adoption.