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

TL;DR

This study introduces SPOILS, an AI tool that combines clinical data and radiomics to create personalized treatment plans for low back pain patients.

Contribution

SPOILS is a novel AI-based decision support system that integrates clinical intelligence and radiomics for personalized LBP therapy.

Findings

The DeepLabV3+ model achieved high accuracy in segmenting spinal structures.

The Gradient Boost classifier performed best with geometrical data for treatment prediction.

SPOILS demonstrated strong performance in predicting spondylosis severity with high IoU and accuracy.

Abstract

Low back pain (LBP) is a leading cause of disability worldwide, affecting people of all ages while showing increasing prevalence among younger demographics. Patients may present with different symptoms and treatment responses despite identical magnetic resonance imaging results, making it difficult to determine whether surgical and medical interventions are appropriate.

This study aimed to develop SPOILS (Software to Predict Outcome in Lumbar Spondylosis), an artificial intelligence–based decision support tool that merges clinical intelligence and radiomics to generate customized therapy plans for patients with LBP.

The SPOILS system used deep learning models to perform automated segmentation, enabling the extraction of geometrical parameters, including disk height, disk width, vertebrae height, vertebrae width, canal diameter, disk height index, signal intensity, and disk volume. A labeled dataset was created using expert-verified Pfirrmann and spondylosis severity gradings to address the clinical issues stemming from manual grading variability and subjectivity. Machine learning algorithms were used with this combined dataset to predict outcomes and recommend personalized treatment plans.

The DeepLabV3+ segmentation model with a ResNet50 encoder achieved 95.5% accuracy, which increased to 98.7% after 8-fold cross-validation and simultaneously improved precision (96.95%), recall (97.1%), Dice coefficient (96.9%), and intersection over union (IoU; 94.8%). The convolutional neural network with MobileNetV2 achieved 97.84% accuracy and 96.76% IoU for spondylosis severity prediction after cross-validation. The Gradient Boost classifier demonstrated the best results with geometrical data by achieving 91.65% accuracy and 84.59% IoU.

SPOILS introduced an innovative method to customize LBP treatment through the combination of artificial intelligence technology with radiological data and clinical expertise.

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