Search Results

You are looking at 1 - 4 of 4 items for

  • Author or Editor: Craig K. Jones x
  • Refine by Access: all x
Clear All Modify Search
Free access

Automated prediction of the Thoracolumbar Injury Classification and Severity Score from CT using a novel deep learning algorithm

Sophia A. Doerr, Carly Weber-Levine, Andrew M. Hersh, Tolulope Awosika, Brendan Judy, Yike Jin, Divyaansh Raj, Ann Liu, Daniel Lubelski, Craig K. Jones, Haris I. Sair, and Nicholas Theodore


Damage to the thoracolumbar spine can confer significant morbidity and mortality. The Thoracolumbar Injury Classification and Severity Score (TLICS) is used to categorize injuries and determine patients at risk of spinal instability for whom surgical intervention is warranted. However, calculating this score can constitute a bottleneck in triaging and treating patients, as it relies on multiple imaging studies and a neurological examination. Therefore, the authors sought to develop and validate a deep learning model that can automatically categorize vertebral morphology and determine posterior ligamentous complex (PLC) integrity, two critical features of TLICS, using only CT scans.


All patients who underwent neurosurgical consultation for traumatic spine injury or degenerative pathology resulting in spine injury at a single tertiary center from January 2018 to December 2019 were retrospectively evaluated for inclusion. The morphology of injury and integrity of the PLC were categorized on CT scans. A state-of-the-art object detection region-based convolutional neural network (R-CNN), Faster R-CNN, was leveraged to predict both vertebral locations and the corresponding TLICS. The network was trained with patient CT scans, manually labeled vertebral bounding boxes, TLICS morphology, and PLC annotations, thus allowing the model to output the location of vertebrae, categorize their morphology, and determine the status of PLC integrity.


A total of 111 patients were included (mean ± SD age 62 ± 20 years) with a total of 129 separate injury classifications. Vertebral localization and PLC integrity classification achieved Dice scores of 0.92 and 0.88, respectively. Binary classification between noninjured and injured morphological scores demonstrated 95.1% accuracy. TLICS morphology accuracy, the true positive rate, and positive injury mismatch classification rate were 86.3%, 76.2%, and 22.7%, respectively. Classification accuracy between no injury and suspected PLC injury was 86.8%, while true positive, false negative, and false positive rates were 90.0%, 10.0%, and 21.8%, respectively.


In this study, the authors demonstrate a novel deep learning method to automatically predict injury morphology and PLC disruption with high accuracy. This model may streamline and improve diagnostic decision support for patients with thoracolumbar spinal trauma.

Restricted access

Oral Presentations 2016 AANS Annual Scientific Meeting Chicago, IL • April 30–May 4, 2016

Published online April 1, 2016; DOI: 10.3171/2016.4.JNS.AANS2016abstracts

Free access

Oral Presentations 2014 AANS Annual Scientific Meeting San Francisco, California • April 5–9, 2014

Published online June 1, 2015; DOI: 10.3171/2015.6.JNS.AANS2014abstracts

Free access

Oral Presentations 2015 AANS Annual Scientific Meeting Washington, DC • May 2–6, 2015

Published online August 1, 2015; DOI: 10.3171/2015.8.JNS.AANS2015abstracts