Efficacy and safety of artificial intelligence-assisted image-guided navigation in complex endovascular aortic repair: a systematic review and meta-analysis.

BACKGROUND

Complex endovascular aortic repair exposes patients and operators to substantial radiation, contrast, and long procedures. Artificial intelligence (AI)-assisted image-guided navigation, based on cloud-based three-dimensional image fusion, has been proposed to improve intraoperative guidance, but its comparative benefit is uncertain.

METHODS

We searched MEDLINE, Embase, the Cochrane Library, and Web of Science (last search July 2025) for studies comparing AI-assisted image-guided navigation with conventional fluoroscopy and digital subtraction angiography during complex endovascular aortic repair. No navigation platform was prespecified. Random-effects meta-analyses yielded mean differences (MD) with 95% confidence intervals (CI); heterogeneity was quantified with I2. Risk of bias was assessed with ROBINS-I and certainty with GRADE.

RESULTS

Three retrospective cohorts (332 patients; 189 AI, 143 control), all evaluating one cloud-based platform (Cydar EV), were eligible. AI-assisted navigation was associated with a lower radiation dose (2 studies, 216 patients; MD -1435.85 mGy, 95% CI -2208.84 to -662.86; I2 = 0%). No significant differences were found in fluoroscopy time (MD -11.04 minutes, 95% CI -25.37 to 3.29; I2 = 65%), operative time (MD -16.39 minutes, 95% CI -37.21 to 4.42; I2 = 0%), or contrast volume (MD -30.59 mL, 95% CI -73.95 to 12.76; I2 = 94%). Mortality and major adverse events (single studies) did not differ significantly.

CONCLUSIONS

Cydar EV-assisted navigation may reduce radiation dose during complex endovascular aortic repair, but evidence is limited to retrospective studies of a single platform and does not establish a class effect for other AI-assisted navigation systems. Prospective comparative studies across platforms are needed.