Automated detection of bone lesions using CT and MRI: a systematic review.

PURPOSE

The aim of this study was to systematically review the use of automated detection systems for identifying bone lesions based on CT and MRI, focusing on advancements in artificial intelligence (AI) applications.

MATERIALS AND METHODS

A literature search was conducted on PubMed and MEDLINE. Data were extracted and grouped into three main categories, namely baseline study characteristics, model validation strategies, and the type of AI algorithms.

RESULTS

A total of 10 studies were selected and analyzed, including 2,768 patients overall with a median of 187 per study. These studies utilized various AI algorithms, predominantly deep learning models (6 studies) such as Convolutional Neural Networks. Among machine learning validation strategies, K-fold cross-validation was the mostly used (5 studies). Clinical validation was performed using data from the same institution (internal testing) in 8 studies and from both the same and different (external testing) institutions in 1 study, respectively.

CONCLUSION

AI, particularly deep learning, holds significant promise in enhancing diagnostic accuracy and efficiency. However, the review highlights several limitations, such as the lack of standardized validation methods and the limited use of external datasets for testing. Future research should address these gaps to ensure the reliability and applicability of AI-based detection systems in clinical settings.