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Johnson et al. Art Int Surg 2024;4:401-10 Artificial
DOI: 10.20517/ais.2024.40
Intelligence Surgery

Original Article Open Access

Pseudarthrosis following adult spinal deformity
surgery may be predicted with preoperative MRI

adipose tissue features: an artificial intelligence
study on raw 3D imaging

2
2,3
1
1
Graham W. Johnson 1 , Hani Chanbour , Derek J. Doss , Ghassan S. Makhoul , Amir M. Abtahi , Byron F.
2,3
Stephens , Scott L. Zuckerman 2,3
1
Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA.
2
Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37212, USA.
3
Department of Orthopedic Surgery, Vanderbilt University Medical Center, Nashville, TN 37212, USA.
Correspondence to: Dr. Scott L. Zuckerman, Department of Neurological Surgery, Vanderbilt University Medical Center, Medical
Center North T-4224, Nashville, TN 37212, USA. E-mail: scott.zuckerman@vumc.org
How to cite this article: Johnson GW, Chanbour H, Doss DJ, Makhoul GS, Abtahi AM, Stephens BF, Zuckerman SL.
Pseudarthrosis following adult spinal deformity surgery may be predicted with preoperative MRI adipose tissue features: an
artificial intelligence study on raw 3D imaging. Art Int Surg 2024;4:401-10. https://dx.doi.org/10.20517/ais.2024.40
Received: 12 Jun 2024 First Decision: 10 Oct 2024 Revised: 26 Oct 2024 Accepted: 31 Oct 2024 Published: 1 Dec 2024
Academic Editor: Andrew A. Gumbs Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
Aim: The purpose of this study is to investigate the utility of incorporating magnetic resonance imaging (MRI) into
an artificial intelligence (AI) model to preoperatively predict pseudarthrosis for patients undergoing adult spinal
deformity (ASD) surgery.

Methods: A retrospective cohort study was conducted on patients undergoing ASD surgery at Vanderbilt
University Medical Center with at least 2 years of follow-up. We first collected demographic variables and
measured traditional radiographic variables with Surgimap software. The primary outcome of interest was
pseudarthrosis, defined as mechanical pain without evidence of bony union with or without a rod fracture. Next,
cohort differences between patients diagnosed with and without pseudarthrosis were evaluated with t-tests for
continuous variables and chi-squared tests for categorical variables using Bonferroni-Holm multiple comparison
correction. Using a subpopulation of patients with preoperative thoracic MRI available, a three-dimensional
convolutional neural network (3D-CNN) with five-fold nested cross-validation was developed to predict
pseudarthrosis - accuracy was evaluated with the Youden index. Finally, class activation mapping (CAM) was

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0
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