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

Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

6.3K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
6.3K

You might also read

Related Articles

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

Sort by
Same journal

Performance Gaps and Optimization Strategies in Chinese Medical Large Language Models Based on MedBench: Evaluation Study.

JMIR AI·2026
Same journal

Metrics Used for the Evaluation of Chatbots Providing Cancer Genetic Risk Assessment and Education: Systematic Review.

JMIR AI·2026
Same journal

AI as the Interpreter for Identifying Root Causes and Emotional Themes in Mental Health Narratives on Reddit Using AutoML and PaLM 2: Mixed Methods Study.

JMIR AI·2026
Same journal

Effectiveness of Humanized AI Avatars and Messenger Gender for Dental Postprocedure Instructions: Two Randomized Experiments.

JMIR AI·2026
Same journal

Exploring the Role of AI in Enhancing Nuclear Medicine Report Impressions Generated by Trainees and ChatGPT-4o: Comparative Evaluation Study.

JMIR AI·2026
Same journal

AI-Driven Digital Twin Architecture for Multimodal Prediction and Adaptive Intervention in Cognitive Aging.

JMIR AI·2026

Related Experiment Video

Updated: Mar 13, 2026

Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation
06:28

Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation

Published on: December 13, 2024

1.6K

AI-Based Personalized Therapy With Clinical Intelligence and Radiomics (SPOILS) for Patients With Low Back Pain:

Purushottam Kumar1, Suyash Singh1, Bunil Kumar Balabantaray2

  • 1Department of Neurosurgery, All India Institute of Medical Sciences, Raebareli, Dalmau Road, Munshiganj, Raebareli, Uttar Pradesh, 229405, India, 91 6393627740.

JMIR AI
|March 11, 2026
PubMed
Summary

This study developed SPOILS, an AI tool for low back pain (LBP). It uses radiomics and clinical data to create personalized treatment plans, improving patient outcomes and decision-making for LBP management.

Keywords:
AIartificial intelligenceclinical intelligence and radiomicsdegenerative diseaselumbar spondylosislumbar spondylosis diagnosis and treatmentpersonalized treatmentspine segmentation

More Related Videos

Quantitative [18F]-Naf-PET-MRI Analysis for the Evaluation of Dynamic Bone Turnover in a Patient with Facetogenic Low Back Pain
06:31

Quantitative [18F]-Naf-PET-MRI Analysis for the Evaluation of Dynamic Bone Turnover in a Patient with Facetogenic Low Back Pain

Published on: August 8, 2019

7.8K
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

8.9K

Related Experiment Videos

Last Updated: Mar 13, 2026

Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation
06:28

Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation

Published on: December 13, 2024

1.6K
Quantitative [18F]-Naf-PET-MRI Analysis for the Evaluation of Dynamic Bone Turnover in a Patient with Facetogenic Low Back Pain
06:31

Quantitative [18F]-Naf-PET-MRI Analysis for the Evaluation of Dynamic Bone Turnover in a Patient with Facetogenic Low Back Pain

Published on: August 8, 2019

7.8K
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

8.9K

Area of Science:

  • Spine surgery and medical imaging analysis
  • Artificial intelligence in healthcare
  • Radiomics and machine learning applications

Background:

  • Low back pain (LBP) is a global health issue causing significant disability, with increasing prevalence in younger populations.
  • Variability in patient symptoms and treatment responses complicates diagnosis and intervention planning, even with consistent MRI findings.

Purpose of the Study:

  • To develop SPOILS (Software to Predict Outcome in Lumbar Spondylosis), an AI-driven decision support system.
  • To integrate clinical intelligence and radiomics for personalized LBP therapy recommendations.
  • To overcome limitations of manual grading subjectivity in spinal imaging analysis.

Main Methods:

  • Utilized deep learning models (DeepLabV3+, ResNet50, MobileNetV2) for automated image segmentation and feature extraction.
  • Extracted geometrical parameters (e.g., disk height, canal diameter, disk volume) and applied expert-verified grading (Pfirrmann, spondylosis severity).
  • Employed machine learning algorithms (Gradient Boost classifier) on a combined dataset for outcome prediction and treatment personalization.

Main Results:

  • Achieved high accuracy (up to 98.7%) in automated segmentation and spondylosis severity prediction via deep learning models.
  • Demonstrated strong performance in predicting outcomes using geometrical data (91.65% accuracy).
  • Successfully integrated radiomic features and clinical grading for robust LBP analysis.

Conclusions:

  • SPOILS offers an innovative AI-powered approach to customize LBP treatment strategies.
  • The system effectively combines radiological data, radiomics, and clinical expertise.
  • This AI tool enhances personalized medicine for patients suffering from low back pain.