Evidence map›Paper›PMID 41182393›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

SpineScan: a deep learning model for lumbar spine MRI annotation and Pfirrmann grading assessment.

Aleksandr Minin, Olga Leonova, Aleksandr Krutko, Elizaveta Elgaeva, Denis Antonets, Dmitriy Shtokalo, Yakov Tsepilov

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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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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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

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Aleksandr MininInstitute for Artificial Intelligence, Lomonosov Moscow State University, Moscow, Russian Federation.
Olga LeonovaCentral Scientific Research Institute of Traumatology and Orthopedics, Moscow, Russian Federation. onleonova@gmail.com.
Aleksandr KrutkoCentral Scientific Research Institute of Traumatology and Orthopedics, Moscow, Russian Federation.
Elizaveta ElgaevaInstitute of Cytology and Genetics, Novosibirsk, Russian Federation.
Denis AntonetsInstitute for Artificial Intelligence, Lomonosov Moscow State University, Moscow, Russian Federation.
Dmitriy ShtokaloInstitute for Artificial Intelligence, Lomonosov Moscow State University, Moscow, Russian Federation.
Yakov TsepilovInstitute of Cytology and Genetics, Novosibirsk, Russian Federation.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWhile recent advances in deep learning have enabled automated Pfirrmann grading systems for intervertebral disc degeneration (IDD), many models remain inaccessible due to proprietary restrictions. This study aimed to develop and validate a convolutional neural network (CNN) for automated Pfirrmann grading using a diverse clinical dataset, to compare our model's performance with previously published results, and to create an open-source web application with a graphical user interface capable of grading both DICOM studies and individual MRI slices provided as image files.

methodsWe trained a CNN-based model using the YOLOv8x architecture on two datasets: a well-curated Russian disc degeneration study (RuDDS) cohort and an open-access dataset, totaling 484 lumbar MRI scans. Ground truth grading was provided by expert radiologists. The model was designed to simultaneously detect intervertebral discs and classify degeneration grades from single MRI slices. Performance was evaluated using standard metrics, including precision, recall, and mean average precision (mAP) across Pfirrmann grades I to V.

resultsOur model achieved predictive accuracy between 0.78 and 0.82 depending on lumbar level. The highest performance was observed for Grade IV discs (mAP50 = 0.872), while performance for Grade V was lower (mAP50-95 = 0.525), likely due to poor contrast and indistinct boundaries in highly degenerated discs. Overall, the model demonstrated a precision of 0.75 and a recall of 0.808. Comparison with previous studies revealed that our results are consistent with expert-level performance. The developed model formed the basis of a specialized web application, SpineScan, implemented using the Streamlit framework.

conclusionsThe developed model shows strong potential for automated grading of lumbar disc degeneration and performs comparably to expert radiologists in most cases. Our findings support the potential applicability of SpineScan for AI-assisted Pfirrmann grading.

Indexed as

Deep LearningIntervertebral Disc DegenerationLumbar VertebraeMagnetic Resonance ImagingAdultFemaleHumansMaleMiddle AgedConvolutional neural networkDeep learningDisc degenerationLumbar spineMRI

Identifiers

PMID41182393

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.