Deep learning-based identification of aberrant anterior tibial artery on knee MRI: a brazilian multicenter study.
Authors
Affiliations (10)
Affiliations (10)
- Graduate Program in Medicine (Clinical Radiology), Imaging Diagnosis Department, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.
- Division of Radiology, Delboni Medicina Diagnóstica, DASA, São Paulo, Brazil.
- Graduate Program in Medicine (Clinical Radiology), Imaging Diagnosis Department, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil. [email protected].
- Department of Evidence-Based Medicine, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil. [email protected].
- Hapvida Notredame Intermédica, São Paulo, Brazil. [email protected].
- Faculdade de Ciências Médicas, Santa Casa de Misericórdia de São Paulo, São Paulo, Brazil.
- Hospital Israelita Albert Einstein, São Paulo, Brazil.
- Department of Radiology, Fleury Medicina E Saúde, São Paulo, Brazil.
- Division of Musculoskeletal Radiology, Rede Dor, São Paulo, Brazil.
- Eden, Palo Alto, CA, USA.
Abstract
To develop and validate a deep learning model for the detection of aberrant anterior tibial artery (AATA) on axial T2-weighted knee MRI, given the surgical relevance of unrecognized AATA and the lack of automated detection tools. This retrospective study included 70,260 MRI images from 2315 examinations (1441 without AATA and 874 with AATA) collected after institutional review board approval. Musculoskeletal radiologists performed image-level annotations. Data were split at the patient level into training, validation, and internal test sets; an independent dataset from another institution served as an external test set. A convolutional neural network was implemented in Python and PyTorch. Model performance was assessed at the patient level. At the slice level, the model achieved an F1-score of 0.838 on the internal test set. Patient-level classification using the validation-derived threshold (0.17) yielded F1-scores of 0.966 on the validation set, 0.979 on the internal test set, and 0.786 on the external test set. The area under the receiver operating characteristic curve for the external cohort was 0.97, indicating strong generalization despite a decrease in precision due to false positives. To our knowledge, this is the first study to apply artificial intelligence for automated detection of AATA on knee MRI. The proposed deep learning model performs this task with high sensitivity. Despite reduced precision in the external cohort, it demonstrates strong potential for enhancing preoperative risk assessment and surgical planning. Broader multicenter validation is warranted before clinical deployment.