DOI: 10.1097/brs.0000000000004816 ISSN:

Artificial Intelligence to Preoperatively Predict Proximal Junction Kyphosis Following Adult Spinal Deformity Surgery

Graham W. Johnson, Hani Chanbour, Mir Amaan Ali, Jeffrey Chen, Tyler Metcalf, Derek Doss, Iyan Younus, Soren Jonzzon, Steven G. Roth, Amir M. Abtahi, Byron F. Stephens, Scott L. Zuckerman
  • Neurology (clinical)
  • Orthopedics and Sports Medicine
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Study Design:

Retrospective cohort

Objective:

In a cohort of patients undergoing adult spinal deformity (ASD) surgery, we used artificial intelligence to compare three models of preoperatively predicting radiographic proximal junction kyphosis (PJK) using: 1) traditional demographics and radiographic measurements, 2) raw preoperative scoliosis radiographs, and 3) raw preoperative thoracic magnetic resonance imaging (MRI).

Summary of Background Data:

Despite many proposed risk factors, PJK following ASD surgery remains difficult to predict.

Methods:

A single-institution, retrospective cohort study was undertaken for patients undergoing ASD surgery from 2009-21. PJK was defined as a sagittal Cobb angle of upper-instrumented vertebra (UIV) and UIV+2>10° and a postoperative change in UIV/UIV+2>10°. For Model-1, a support vector machine was used to predict PJK within 2 years postoperatively using clinical and traditional sagittal/coronal radiographic variables and intended levels of instrumentation. Next, for Model-2, a convolutional neural network (CNN) was trained on raw preoperative lateral and posterior-anterior scoliosis radiographs. Finally, for Model-3, a CNN was trained on raw preoperative thoracic T1 MRIs.

Results:

A total of 191 patients underwent ASD surgery with at least 2-year follow-up and 89 (46.6%) developed radiographic PJK within 2 years.

Model-1:
Using clinical variables and traditional radiographic measurements, the model achieved a sensitivity:57.2% and specificity:56.3%.
Model-2
: a CNN with raw scoliosis x-rays predicted PJK with sensitivity: 68.2% and specificity: 58.3%.
Model-3:
a CNN with raw thoracic MRIs predicted PJK with average sensitivity: 73.1% and specificity: 79.5%. Finally, an attention map outlined the imaging features used by Model-3 elucidated that soft tissue features predominated all true positive PJK predictions.

Conclusion:

The use of raw MRIs in an artificial intelligence model improved the accuracy of PJK prediction compared to raw scoliosis radiographs and traditional clinical/radiographic measurements. The improved predictive accuracy using MRI may indicate that PJK is best predicted by soft-tissue degeneration and muscle atrophy.

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