Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence algorithm

To develop and validate an open-source artificial intelligence (AI) algorithm to accurately detect contrast phases in abdominal CT scans. Retrospective study aimed to develop an AI algorithm trained on 739 abdominal CT exams from 2016 to 2021, from 200 unique patients, covering 1545 axial series. We performed segmentation of five key anatomic structures—aorta, portal vein, inferior vena cava, renal parenchyma, and renal pelvis—using TotalSegmentator, a deep learning-based tool for multi-organ segmentation, and a rule-based approach to extract the renal pelvis. Radiomics features were extracted from the anatomical structures for use in a gradient-boosting classifier to identify four contrast phases: non-contrast, arterial, venous, and delayed. Internal and external validation was performed using the F1 score and other classification metrics, on the external dataset “VinDr-Multiphase CT”. The training dataset consisted of 172 patients (mean age, 70 years ± 8, 22