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Related Experiment Video

Updated: Dec 4, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predictive Model for Selection of Upper Treated Vertebra Using a Machine Learning Approach.

Renaud Lafage1, Bryan Ang1, Basel Sheikh Alshabab1

  • 1Department of Spine Surgery, Hospital for Special Surgery, Department of Spine Surgery, New York, New York, USA.

World Neurosurgery
|October 22, 2020
PubMed
Summary

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An artificial neural network accurately predicted the upper instrumented vertebra (UIV) level for adult spinal deformity surgery, mimicking experienced surgeons. This AI tool aids in complex spinal deformity correction decisions.

Area of Science:

  • Spine surgery
  • Artificial intelligence
  • Medical imaging

Background:

  • Adult spinal deformity (ASD) presents complex surgical challenges.
  • Selecting the optimal upper instrumented vertebra (UIV) is critical for successful surgical correction.
  • Current UIV selection relies heavily on surgeon experience.

Purpose of the Study:

  • To develop and validate an algorithm that replicates surgeon decision-making for UIV selection in ASD.
  • To utilize deep learning for predicting UIV levels (T1-T6 vs. T7-T12).

Main Methods:

  • Retrospective review of 143 adult spinal deformity patients (2013-2018).
  • Data included demographics, coronal/sagittal alignment, and pelvic incidence-lumbar lordosis mismatch.
  • A neural network was trained on 70% of data, validated on 15%, and tested on 15%.
Keywords:
Deep learningLumbarThoracolumbarTreatment outcome

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Main Results:

  • The neural network achieved 81.0% accuracy, 87.5% precision, and 87.5% recall in UIV selection.
  • Patients had moderate to severe deformity (Cobb angle: 43° ± 22°).
  • The study identified predictors for UIV placement in the upper or lower thoracic spine.

Conclusions:

  • An artificial neural network successfully mimicked expert surgeon UIV selection for ASD.
  • This AI approach shows promise for standardizing and improving surgical planning.
  • Future research should integrate surgical outcomes into AI models for enhanced predictive capabilities.