Artificial Intelligence Detection and Segmentation Models: A Systematic Review and Meta-Analysis of Brain Tumors in Magnetic Resonance Imaging

Abstract

Objective

To thoroughly analyze factors affecting the generalization ability of deep learning algorithms on brain tumor detection and segmentation models.

Patients and Methods

We searched PubMed, Embase, Web of Science, Cochrane Library, and IEEE from inception to July 25, 2023, and 19 studies with 12,000 patients were identified. The criteria required studies to use magnetic resonance imaging (MRI) for brain tumor detection and segmentation, offer clear performance metrics, and use external validation data sets. The study focused on outcomes such as sensitivity and Dice score. Study quality was assessed using QUADAS-2 and CLAIM tools. The meta-analysis evaluated varying algorithms and their performance across different validation data sets.

Results

MRI hardware as the manufacturer may contribute to data set diversity, impacting AI model generalizability. The study found that the best algorithms had a pooled lesion-wise Dice score of 84%, with pooled sensitivities of 87% (patient-wise) and 86% (lesion-wise). Post-2022 methodologies highlighted evolving artificial intelligence techniques. Performance differences were evident among tumor types, likely due to size disparities. 3D models outperformed their 2D and ensemble counterparts in detection. Although specific preprocessing techniques improved segmentation outcomes, some hindered detection.

Conclusion

The study underscores the potential of deep learning in improving brain tumor diagnostics and treatment planning. We also identify the need for further research, including developing a comprehensive diversity index, expanded meta-analyses, and using generative adversarial networks for data diversification, paving the way for AI-driven advancements in oncological patient care.

Trial Registration

PROPERO (CRD42023459108).