Evidence map›Paper›PMID 39152359›Full record

ArticleMedical & biological engineering & computing2025

Validity of machine learning algorithms for automatically extract growing rod length on radiographs in children with early-onset scoliosis.

Mohammad Humayun Kabir, Marek Reformat, Sarah Southon Hryniuk, Kyle Stampe, Edmond Lou

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Medical & biological engineering & computing, 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. Review
  2. Article
  3. Review
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

5 authors.

Mohammad Humayun KabirDepartment of Electrical and Computer Engineering, University of Alberta, 11-263 Donadeo Innovation Centre for Engineering, 9211-116 St, Edmonton, AB, T6G 1H9, Canada.
Marek ReformatDepartment of Electrical and Computer Engineering, University of Alberta, 11-263 Donadeo Innovation Centre for Engineering, 9211-116 St, Edmonton, AB, T6G 1H9, Canada.
Sarah Southon HryniukDepartment of Surgery, University of Alberta, Edmonton, AB, Canada.
Kyle StampeDepartment of Electrical and Computer Engineering, University of Alberta, 11-263 Donadeo Innovation Centre for Engineering, 9211-116 St, Edmonton, AB, T6G 1H9, Canada.
Edmond LouDepartment of Electrical and Computer Engineering, University of Alberta, 11-263 Donadeo Innovation Centre for Engineering, 9211-116 St, Edmonton, AB, T6G 1H9, Canada. elou@ualberta.ca.ORCID http://orcid.org/0000-0002-7531-8377

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The magnetically controlled growing rod technique is an effective surgical treatment for children who have early-onset scoliosis. The length of the instrumented growing rods is adjusted regularly to compensate for the normal growth of these patients. Manual measurement of rod length on posteroanterior spine radiographs is subjective and time-consuming. A machine learning (ML) system using a deep learning approach was developed to automatically measure the adjusted rod length. Three ML models-rod model, 58 mm model, and head-piece model-were developed to extract the rod length from radiographs. Three-hundred and eighty-seven radiographs were used for model development, and 60 radiographs with 118 rods were separated for final testing. The average precision (AP), the mean absolute difference (MAD) ± standard deviation (SD), and the inter-method correlation coefficient (ICC

Indexed as

Machine LearningScoliosisAlgorithmsChildChild, PreschoolFemaleHumansMaleRadiographyReproducibility of ResultsEarly-onset scoliosisMagnetically controlled growing rodMask RCNNRod length measurements

Identifiers

PMID39152359

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.