Evidence map›Paper›PMID 42427496›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2026

Composite CT scoring system for Diffuse Idiopathic Skeletal Hyperostosis (DISH): a validated and reproducible framework for diagnosis and severity assessment.

Lily Gao, Andrew Hoyek, Enobong Anako, Kathryn Jordan, Trey McGonigle, Shi Huang, Manish Kota, Bennett Landman, Katherine Van Schaik

Abstract read
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 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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1 · What the graph read from it

What it found

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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.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Lily GaoVanderbilt University School of Medicine, Nashville, TN, USA.
Andrew HoyekNorth Carolina State University, Raleigh, NC, USA.
Enobong AnakoSpelman College, Atlanta, GA, USA.
Kathryn JordanVanderbilt University Medical Center, Department of Radiology, Nashville, TN, USA.
Trey McGonigleVanderbilt University Medical Center, Department of Biostatistics, Nashville, TN, USA.
Shi HuangVanderbilt University Medical Center, Department of Biostatistics, Nashville, TN, USA.
Manish KotaVanderbilt University, Nashville, TN, USA.
Bennett LandmanVanderbilt University Medical Center, Department of Radiology, Nashville, TN, USA.
Katherine Van SchaikVanderbilt University Medical Center, Department of Radiology, Nashville, TN, USA.

Funding

Mechanistic association of epigenetic bone age and frailty measures with bone imaging studies using novel artificial intelligence approaches - Research Continuity and Retention SupplementK08AG088493 · NIA · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Katherine Douglas Van Schaik · 2025 to 2026
$375k
NIA NIH HHS K08 AG088493
6 · The paper itself

Abstract

Variability among observers remains a major barrier to standardizing imaging findings for spinal disease, hindering clinical diagnosis, research reproducibility, and the development of automated tools for assessment of spinal disease. Diffuse Idiopathic Skeletal Hyperostosis (DISH) is underdiagnosed in part due to the lack of consistent, CT-adapted scoring frameworks. We develop and validate a reproducible CT-based scoring system that integrates level-wise ossification scoring (Kuperus) with a structured severity classification (Holton) to capture DISH presence and bridging severity based on the number and distribution of bridged levels (scored 2 or 3). Five raters of varying experience levels (undergraduate students to an attending radiologist) scored 24 training scans; junior raters resolved discrepancies collaboratively, while senior raters reviewed independently. Pairwise comparisons showed perfect specificity (1.00) and moderate sensitivity (0.62-0.75) for DISH presence among less experienced raters compared to the attending radiologist. McNemar's tests showed no significant differences in pairwise disagreement rates for DISH presence, while paired Wilcoxon tests revealed significant differences in bridged level counts, with less experienced raters underestimating the number of bridged levels. Scoring time was 2-3 minutes per scan following training. Subsequently, two independent raters scored 614 thoracolumbar CT scans. Interobserver reliability was excellent for DISH presence (Gwet's AC2 = 0.957 [95% CI: 0.936-0.977]), bridged level count (ICC = 0.933 [95% CI: 0.922-0.943]), and bridging severity (AC2 = 0.950 [95% CI: 0.931-0.969]). Level-wise scores also showed high agreement (median AC2 = 0.881, range: 0.862-0.937), with discrepancies occurring primarily due to transitional anatomy and mildly ossified levels. This CT-based scoring framework enables reliable and reproducible labeling of DISH presence and severity across raters of varying experience levels. Its structured format supports scalable image annotation and minimizes label noise, providing a robust foundation for training computer vision models to automate detection and stratification of DISH in spinal imaging.

Indexed as

computer visionCT scoringDISHimaging informaticsinterobserver reliabilitymachine learningreproducibilityspinal imaging

Identifiers

PMID42427496
PMCPMC13348741

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.