Advancing Neurodiagnostic with Generative AI: Synthetic Data-Driven Lesion Detection in MRI

Abstract

Recent advancements in generative artificial intelligence (AI) are redefining the landscape of non-invasive diagnostics in medical imaging, with significant impact on brain lesion detection via Magnetic Resonance Imaging (MRI). Conventional radiological assessment relies heavily on expert interpretation and manual lesion delineation—procedures that are inherently time-consuming and susceptible to inter- and intra-observer variability. In contrast, generative AI introduces automated, data-driven solutions capable of enhancing diagnostic precision through image synthesis, augmentation, and high-resolution segmentation. State-of-the-art architectures, including Generative Adversarial Networks (GANs) and diffusion-based models, can simulate anatomically consistent MRI scans, reconstruct obscured or missing structures, and amplify subtle pathological signatures that may evade traditional evaluation. By learning complex mappings between healthy and pathological tissue distributions, these models generate high-fidelity synthetic data that benefit both clinical prediction workflows and the training of conventional discriminative algorithms. Additionally, generative augmentation alleviates the scarcity of labeled datasets—a persistent limitation in medical imaging—by producing realistic and diverse lesion-focused samples. This study introduces a generative AI-driven framework for automated brain lesion recognition and classification, comprising standardized preprocessing, targeted data augmentation, and a hybrid discriminative–generative modeling pipeline. The system emphasizes robustness and clinical transparency through explainable inference modules and integrated uncertainty quantification. Experimental results demonstrate that generative learning markedly improves segmentation and classification accuracy, particularly in cases involving rare or morphologically ambiguous lesions. These findings support the integration of generative AI as a cornerstone technology for next-generation precise, scalable, and non-invasive neurodiagnostic workflows

Introduction

Magnetic Resonance Imaging (MRI) is a central tool in neurodiagnostics due to its non-invasive nature, high spatial resolution, and superior soft-tissue contrast. Despite its strengths, MRI interpretation remains challenging because it relies heavily on radiologist expertise and manual lesion delineation, which introduces variability and uncertainty—particularly for small or diffuse lesions.

Recent advances in deep learning, especially convolutional neural networks (CNNs) and generative AI models such as VAEs, GANs, and diffusion models, have transformed automated MRI analysis. Generative AI helps overcome data heterogeneity across scanners and institutions by learning domain-invariant representations and producing synthetic MRIs that enhance training diversity, improve robustness, and reduce overfitting. Hybrid pipelines combining generative and discriminative models further enhance lesion detection and segmentation accuracy, particularly in low-contrast or ambiguous regions. These developments position AI as an assistive tool to radiologists, enabling faster, more consistent, and more objective diagnostic workflows.

The study uses 200 brain MRI scans from OpenNeuro, covering both healthy subjects and lesion-bearing cases. Preprocessing includes intensity normalization, skull stripping, registration, and data augmentation. A U-Net CNN architecture is implemented using TensorFlow, Keras, and PyTorch for lesion segmentation. Generative augmentation is introduced using conditional GANs and diffusion models to enrich the dataset with anatomically realistic synthetic MRIs. Training is performed on an NVIDIA RTX 4090 using Adam optimization and evaluated using Dice score, Precision, Recall, AUC, and Hausdorff Distance.

Experimental results show that generative augmentation significantly improves performance. The model achieves high precision (≈90.5%), excellent sensitivity (≈97.8%), and strong specificity (≈99.1%). Models trained without generative augmentation perform notably worse, demonstrating the value of synthetic data. Interpretability methods such as Grad-CAM are used to highlight lesion-relevant regions, though the “black-box” challenge remains.

Comparisons with manual segmentations from expert neuroradiologists reveal strong alignment. The model achieves a Dice score of ~0.95 with low variance, high precision–recall performance (AP = 0.97), and near-zero bias in Bland–Altman analysis. Only a few difficult cases fall outside the agreement limits, mostly involving diffuse lesions where even expert annotations vary.

Overall, the integration of CNN-based segmentation with GAN and diffusion-based augmentation yields human-comparable performance, high reproducibility, and robust lesion detection. The findings emphasize that generative AI represents a significant paradigm shift in MRI diagnostics, enabling more automated, scalable, and precise neuroimaging workflows and expanding access to advanced diagnostic tools across different clinical settings.

Conclusion

This work presents a preliminary investigation into a generative AI–enabled framework for non-invasive brain lesion detection and classification using magnetic resonance imaging (MRI). The proposed architecture integrates complementary deep learning approaches, employing GAN- and diffusion-based techniques for synthetic data augmentation alongside CNN-driven discriminative models for lesion recognition. The combined pipeline demonstrated promising performance, achieving a precision of 90.5% on a dataset of 200 MRI scans. Despite these encouraging results, the study should be regarded as an initial proof of concept. The dataset, although varied, does not fully reflect the wide spectrum of lesion types, acquisition protocols, and clinical demographics encountered in real-world practice. Consequently, larger multi-center datasets are required to assess generalizability, evaluate robustness across imaging environments, and ensure reliability in diverse clinical scenarios. Future work will focus on increasing model interpretability through advanced visualization methods and attention-guided explanations, as well as investigating multi-modal integration—such as MRI–CT fusion—to enhance diagnostic depth. Further research will also involve prospective clinical validation at scale, with the goal of establishing the algorithm as a decision-support tool in neuroradiology. In conclusion, this preliminary study provides a solid foundation for the development of next-generation AI systems capable of supporting clinicians in the accurate and non-invasive diagnosis of brain lesions, paving the way toward their integration in future clinical workflows.

References

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Copyright

Copyright © 2025 Franchini Roberto, Bianco Andrea. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.