Preliminary study on detection and diagnosis of focal liver lesions based on a deep learning model using multimodal PET/CT images.

Objectives: To develop and validate a deep learning model using multimodal PET/CT imaging for detecting and classifying focal liver lesions (FLL).

Methods: This study included 185 patients who underwent F-FDG PET/CT imaging at our institution from March 2022 to February 2023. We analyzed serological data and imaging.

Detection of Endoleak after Endovascular Aortic Repair through Deep Learning Based on Non-contrast CT.

Objectives: To develop and validate a deep learning model for detecting post-endovascular aortic repair (EVAR) endoleak from non-contrast CT.

Methods: This retrospective study involved 245 patients who underwent EVAR between September 2016 and December 2022. All patients underwent both non-enhanced and enhanced follow-up CT.