AI-based Detection of Postoperative Abnormalities Following Lumbar Fusion Surgery in Spine Radiographs

Purpose or Learning object

The purpose of this study is to develop a deep learning-based system to detect postoperative abnormalities in spine radiographs following lumbar fusion surgery. This system aims to assist radiologists by detecting postoperative abnormalities.

Methods or Background

A total of 1,505 spine radiographs from 85 patients who underwent lumbar fusion surgery were collected at a secondary healthcare facility between February 2018 and January 2022. These radiographs, taken post-operation and during follow-up visits, included anteroposterior, lateral, flexion, and extension views. Annotations for periprosthetic loosening, cage subsidence, and compression fracture were performed by a musculoskeletal radiologist, and verified with CT scans. The Co-DETR model was trained on a subset of 634 radiographs from 74 patients with 726 annotations. The class distribution included 58, 24, and 17 patients yielding 278, 215, and 168 images respectively, with each image averaging 1.10 annotations. Initial training was conducted on a public dataset (FracAtlas), followed by transfer learning to enhance detection of postoperative abnormalities. Negative samples were included to boost training efficiency, and model performance was evaluated using mean Average Precision (mAP).

Results or Findings

Periprosthetic loosening achieved an mAP score of 0.601 with 0.5 IoU threshold. The mAP score for each class of periprosthetic loosening, cage subsidence, and compression fracture were 0.565, 0.667, 0.572, respectively.

Conclusion

The study demonstrates the potential of detecting postoperative abnormalities in spine radiographs after lumbar fusion surgery using deep learning. The results indicate a foundational potential for enhancing diagnostic capabilities in clinical settings. The potential of this approach to improve early detection of complications could lead to more timely interventions and better patient outcomes.

Limitations

Further validation is required to optimize its performance, particularly to support radiologists in settings with limited access to specialists. 

Funding for this study

Not applicable