Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Paediatric idiopathic syringomyelia - a follow-up of radiological and clinical outcomes into adulthood.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery·2025
Same author

AO Spine Clinical Practice Recommendations: Current Systemic Oncological Treatments with the Largest Impact on Patients with Metastatic Spinal Disease.

Global spine journal·2025
Same author

Evaluating neurosurgical training: a national survey examining the British trainee experience.

British journal of neurosurgery·2024
Same author

Intra-cranial hypertension and vision-threatening papilloedema caused by intradural spinal tumours: a case series of three.

British journal of neurosurgery·2023
Same author

Reconfiguration from emergency to urgent elective neurosurgery for glioblastoma patients improves length of stay, surgical adjunct use, and extent of resective surgery.

Neuro-oncology practice·2022
Same author

Identification of early neurodegenerative pathways in progressive multiple sclerosis.

Nature neuroscience·2022

Related Experiment Video

Updated: Jun 6, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

8.2K

Machine learning in predicting cauda equina imaging outcomes- a solution to the problem.

Rosa Sun1, Abdelmageed Abdelrahman Ramadan2, Thaaqib Nazar3

  • 1Department of Neurosurgery, Queen Elizabeth Hospital, Mindelsohn Way, Birmingham, B15 2TH, UK.

European Spine Journal : Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
|December 3, 2024
PubMed
Summary

Machine learning can significantly reduce unnecessary MRI scans for suspected Cauda Equina Syndrome (CES) by over 95%. This AI tool aids in accurately triaging patients, improving diagnostic efficiency for this rare surgical emergency.

Keywords:
Artificial intelligenceCauda equinaMachine learningNeurosurgerySpinal surgery

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression
07:00

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Published on: May 7, 2019

8.9K

Related Experiment Videos

Last Updated: Jun 6, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

8.2K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression
07:00

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Published on: May 7, 2019

8.9K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Neurosurgery

Background:

  • Cauda Equina Syndrome (CES) is a critical surgical emergency with severe quality of life implications.
  • No single symptom definitively diagnoses CES, complicating early management.
  • Machine learning (ML) offers a novel approach to improve diagnostic accuracy and patient triage.

Purpose of the Study:

  • To evaluate the efficacy of ML algorithms in triaging patients with suspected CES (CES-S).
  • To assess the potential of ML to reduce the number of emergency MRI scans performed.
  • To explore the role of Confidence of Prediction (CoP) in ML-driven diagnostic pathways.

Main Methods:

  • A dataset of 499 patients with suspected CES was analyzed.
  • An ML algorithm was trained to predict MRI-diagnosed CES.
  • Algorithm predictions and Confidence of Prediction (CoP) were evaluated on a test set.

Main Results:

  • The ML algorithm demonstrated high accuracy, correctly classifying 476 negative and 6 positive CES cases with high CoP.
  • A strategy of scanning only high CoP positive predictions and all low CoP cases could reduce MRI scans by over 95%.
  • Six false negatives were identified among low CoP predictions.

Conclusions:

  • ML algorithms show significant potential for reducing unnecessary emergency MRI scans in suspected CES cases.
  • Further validation with large-scale prospective data is required for widespread clinical adoption.
  • Continuous algorithm training is crucial for enhancing prediction accuracy and confidence.