Evidence map›Paper›PMID 40410653›Full record

SynthesisSpine deformity2025

Artificial intelligence automated measurements of spinopelvic parameters in adult spinal deformity-a systematic review.

Anthony Bishara, Saarang Patel, Anmol Warman, Jacob Jo, Liam P Hughes, Jawad M Khalifeh, Tej D Azad

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Spine deformity, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Anthony BisharaDepartment of Neurosurgery, Johns Hopkins University, Baltimore, MD, 21287, USA.ORCID http://orcid.org/0009-0001-7231-9083
Saarang PatelDepartment of Neurosurgery, Johns Hopkins University, Baltimore, MD, 21287, USA.
Anmol WarmanDepartment of Neurosurgery, Johns Hopkins University, Baltimore, MD, 21287, USA.
Jacob JoDepartment of Neurosurgery, Johns Hopkins University, Baltimore, MD, 21287, USA.
Liam P HughesDepartment of Neurosurgery, Johns Hopkins University, Baltimore, MD, 21287, USA.
Jawad M KhalifehDepartment of Neurosurgery, Johns Hopkins University, Baltimore, MD, 21287, USA.
Tej D AzadDepartment of Neurosurgery, Johns Hopkins University, Baltimore, MD, 21287, USA. tazad1@jhmi.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis review evaluates advances made in deep learning (DL) applications to automatic spinopelvic parameter estimation, comparing their accuracy to manual measurements performed by surgeons.

methodsThe PubMed database was queried for studies on DL measurement of adult spinopelvic parameters between 2014 and 2024. Studies were excluded if they focused on pediatric patients, non-deformity-related conditions, non-human subjects, or if they lacked sufficient quantitative data comparing DL models to human measurements. Included studies were assessed based on model architecture, patient demographics, training, validation, testing methods, and sample sizes, as well as performance compared to manual methods.

resultsOf 442 screened articles, 16 were included, with sample sizes ranging from 15 to 9,832 radiograph images and reporting interclass correlation coefficients (ICCs) of 0.56 to 1.00. Measurements of pelvic tilt, pelvic incidence, T4-T12 kyphosis, L1-L4 lordosis, and SVA showed consistently high ICCs (>0.80) and low mean absolute deviations (MADs <6°), with substantial number of studies reporting pelvic tilt achieving an excellent ICC of 0.90 or greater. In contrast, T1-T12 kyphosis and L4-S1 lordosis exhibited lower ICCs and higher measurement errors. Overall, most DL models demonstrated strong correlations (>0.80) with clinician measurements and minimal differences compared to manual references, except for T1-T12 kyphosis (average Pearson correlation: 0.68), L1-L4 lordosis (average Pearson correlation: 0.75), and L4-S1 lordosis (average Pearson correlation: 0.65).

conclusionNovel computer vision algorithms show promising accuracy in measuring spinopelvic parameters, comparable to manual surgeon measurements. Future research should focus on external validation, additional imaging modalities, and the feasibility of integration in clinical settings to assess model reliability and predictive capacity.

Indexed as

Artificial IntelligencePelvisSpinal CurvaturesSpineAdultDeep LearningHumansKyphosisLordosisRadiographyArtificial intelligenceDeep learningMachine learningSpinal deformitySpinopelvic parameters

Identifiers

PMID40410653

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.