A review: artificial intelligence in image-guided spinal surgery

ABSTRACT

Introduction

Due to the complex anatomy of the spine and the intricate surgical procedures involved, spinal surgery demands a high level of technical expertise from surgeons. The clinical application of image-guided spinal surgery has significantly enhanced lesion visualization, reduced operation time, and improved surgical outcomes.

Areas covered

This article reviews the latest advancements in deep learning and artificial intelligence in image-guided spinal surgery, aiming to provide references and guidance for surgeons, engineers, and researchers involved in this field.

Expert opinion

Our analysis indicates that image-guided spinal surgery, augmented by artificial intelligence, outperforms traditional spinal surgery techniques. Moving forward, it is imperative to collect a more expansive dataset to further ensure the procedural safety of such surgeries. These insights carry significant implications for the integration of artificial intelligence in the medical field, ultimately poised to enhance the proficiency of surgeons and improve surgical outcomes.

KEYWORDS:

• Image-guided spinal surgery
• spine image segmentation
• three-dimensional reconstruction
• pre-operative planning
• surgical navigation
• intra-operative registration

Article highlights

• This article provides a comprehensive review of the status of computer-aided spinal surgery in artificial intelligence.

• Summarizing and analyzing the advancements and constraints of image segmentation and registration through deep learning in recent years.

• Introducing multi-modal three-dimensional (3D) reconstruction methods and deep learning three-dimensional (3D) reconstruction approaches.

• Introducing software development for pre-operative planning and intra-operative navigation, and summarizing the development of path planning techniques and optimization in intra-operative navigation.

• The limitations of artificial intelligence technology in image-guided spinal surgery are summarized and its future development is prospected.

Declaration of interest

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewers disclosure

Peer reviewers on this manuscript have no relevant financial relationships or otherwise to disclose.

Additional information

Funding

This study was supported by the Shanghai Science and Technology Commission Science and Technology Innovation Action Plan Biomedical Science and Technology Support Special Project [21S31901300].