Evidence map›Paper›PMID 41258976›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026

Automatic detection of spinal pathologies based on MRI scans.

Omer Dor, Oz Haim, Lee Azolai, Ariel Agur, May Cohen, Noam Shomron, Moran Artzi

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Article in 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, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Omer DorGray Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.
Oz HaimDepartment of Neurosurgery, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
Lee AzolaiDepartment of Neurosurgery, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
Ariel AgurDepartment of Neurosurgery, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
May CohenGray Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.
Noam ShomronGray Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.
Moran ArtziGray Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel. artzimy@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe study aims to develop a deep learning-based method for the automatic identification of pathological vertebrae in lumbar spine MRI scans.

methodsA retrospective cohort of 98 subjects was used, including 31 healthy controls and 67 patients with vertebral pathologies (infection, inflammation, or carcinoma). T2-weighted MRI scans were acquired from 1.5T and 3.0T MRI scanners. A RetinaNet object detection algorithm with a ResNet18 backbone was used for vertebrae localization in 2D T2-weighted MRI images. The detected vertebrae were classified as healthy or pathological using a ConvNeXt model, with hyperparameter optimization performed using Optuna.

resultsThe object detection model achieved an Intersection-Over-Union (IOU) score of 0.67 for pathological vertebrae. The 2D-based classification model, enhanced by post-processing using a LightGBM tree-based model, achieved a test accuracy of 95.38%, with precision of 86.67%, recall of 92.86%, and an F1-score of 89.66%.

conclusionsThis study demonstrates the feasibility of deep learning models for detecting pathological vertebrae in lumbar spine MRI scans. The integration of 2D and 3D imaging data enhances diagnostic accuracy and presents an automated radiologist assistant tool for improved spinal pathology diagnosis, potentially streamlining clinical workflows.

Indexed as

Deep LearningImage Interpretation, Computer-AssistedLumbar VertebraeMagnetic Resonance ImagingSpinal DiseasesAdultAgedDetection AlgorithmsFemaleHumansMaleMiddle AgedRetrospective StudiesClassificationDeep learningLumbar spine MRIObject detectionRadiologist assistant toolSpinal vertebrae pathologies

Identifiers

PMID41258976

What OpenQuestion holds

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Registered trials

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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.