ArticleJMIR AI2026
AI-Based Personalized Therapy With Clinical Intelligence and Radiomics (SPOILS) for Patients With Low Back Pain: Prospective Observational Study.
Article in JMIR AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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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