Deep learning algorithm for predicting rapid progression of abdominal aortic aneurysm by integrating CT images and clinical features.
Authors
Affiliations (12)
Affiliations (12)
- Department of Thoracic and Cardiovascular Surgery, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, 20, Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea. [email protected].
- Healthcare AI Research Institute, Seoul National University Hospital, Seoul, Republic of Korea. [email protected].
- Arontier Co., Ltd, Seoul, Republic of Korea.
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea.
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Seoul National University College of Medicine, Seoul, Republic of Korea.
- Healthcare AI Research Institute, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Clinical Research Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
- Department of Thoracic and Cardiovascular Surgery, Chungnam National University Hospital, Chungnam National University College of Medicine, Daejeon, Republic of Korea.
- Department of Thoracic and Cardiovascular Surgery, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, 20, Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea.
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. [email protected].
Abstract
Abdominal aortic aneurysm (AAA) progression carries a significant rupture risk, demanding accurate prediction models beyond traditional methods that rely on limited clinical parameters and often overlook complex factor interplay. We aimed to enhance prediction by developing and validating an end-to-end multi-modal deep learning (DL) model that integrates features extracted using ResNet from computed tomography (CT) images, geometric features derived from radiomics based on CT annotations, and clinical features obtained from clinical records. This retrospective study utilized data from 561 AAA patients sourced from Boramae Medical Center and Seoul National University Hospital, including 14,252 annotated CT axial images alongside detailed clinical information. Patients were categorized into rapid or slow progression groups based on an annual growth rate threshold of 2.5 mm/year. The multi-modal DL model that incorporated CT images, clinical features, and geometric features demonstrated superior predictive performance for rapid progression, achieving an area under the receiver operating characteristic curve (AUC) of 0.807 and an accuracy of 0.758. This significantly outperformed traditional machine learning models utilizing only clinical data (AUC: 0.716) or only geometric features (AUC: 0.715). The improvement in AUC was statistically significant according to DeLong's test. This study underscores the value of AI-driven, multi-modal approaches for enhancing patient-specific AAA risk stratification, potentially enabling more precise monitoring and optimized timing for clinical interventions.