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Updated: Nov 9, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Predicting Spinal Surgery Candidacy From Imaging Data Using Machine Learning.

Bayard Wilson¹, Bilwaj Gaonkar¹, Bryan Yoo²

¹Department of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.

|April 7, 2021

Summary

This summary is machine-generated.

A new machine learning algorithm accurately identifies surgical candidacy from lumbar MRI scans, improving spine surgery referrals. This AI tool predicts the need for surgery, reducing unnecessary consultations.

Keywords:

MRI Machine learning Spine surgery Triage

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Last Updated: Nov 9, 2025

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Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Published on: May 7, 2019

Area of Science:

Neurosurgery
Radiology
Artificial Intelligence

Background:

The current process for referring patients to spine surgeons is inefficient.
Many referrals are for non-surgical conditions, leading to wasted appointments.

Purpose of the Study:

To create a machine learning algorithm for identifying spine surgery candidates using only MRI data.
The goal is to improve the accuracy and efficiency of surgical referrals.

Main Methods:

A deep U-Net model was trained on 100 normal lumbar MRI scans.
The model delineated spinal canals and calculated spinal stenosis severity.
The algorithm was tested on 140 patient MRI scans (60 surgical, 80 non-surgical).

Main Results:

The machine learning model achieved high accuracy (AUC=0.88) in predicting surgical candidacy.
Automated MRI interpretation correctly identified surgical need in nearly 90% of cases.

Conclusions:

AI-powered analysis of lumbar MRI scans can accurately determine surgical candidacy.
This tool can optimize patient triage and reduce inefficiencies in surgical referrals.