The Potential of Generative Deep Learning Methods in Spine Surgery: A Focus on Generative Adversarial Networks

Introduction: Generative methods have spurred a disruptive shift in deep learning, capable of synthesizing voluminous artificial media datasets with high fidelity to real images. One particular sub-architecture is the generative adversarial network (GAN), which involves a dynamic exchange between two neural networks to create and classify distributions. GANs have recently been introduced to spine surgery and in limited test cases have demonstrated significant potential to optimize patient-specific interventions. Our review is the first to aggregate and analyze research in the space.

Methods: We conducted a review of primary studies using the PubMed database. Data on the development, applications, and performances of GANs in human spine surgery were collected and summarized by two reviewers.

Results: Of 294 articles, 12 were included in the review. Four studies used a variation of the original Spine-GAN, achieving Dice Coefficients between 81.92%-94.90% for automated classification of distinct anatomies and abnormal pathology. Five additional studies centered around inter-modality image translations (CT to MRI; x-ray to MRI) or intra-modality image translations (T1W to T2W fat sat., etc.; coronal to sagittal x-ray planar interchange.) Only two studies evaluated the performance of GANs in x-ray-based 3D reconstruction, with one notably using autoencoder regularization to improve image denoising. Across all studies, strengths of GAN-based architectures are remarkable pixel-level accuracy, high inception scores, and faster model processing speeds relative to convolutional neural network (CNN) standards. Clinical applications of GANs include pathological classification, personalized pre-operative planning, improved intraoperative navigation, and multimodal imaging data fusion. Limitations of current models include low-volume datasets (range of 160-5343 training images), high costs of operation, and inability to process sequential multimedia (video) data; existing models must also be validated.

Conclusion : This review summarizes the merits of GANs in spinal imaging and surgery and highlights scope for innovation. Overall, the most common uses of GANs are segmentation and image conversions. Unexplored hybrid approaches such as ensemble learning promise to merge high-confidence outputs from additional frameworks—including attention-based transformer methods—thus minimizing reliance on extensive annotated medical data.