Evidence map›Paper›PMID 37300244›Full record

ArticleProceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine2023

A convolutional neural network for high throughput screening of femoral stem taper corrosion.

Anastasia M Codirenzi, Brent A Lanting, Matthew G Teeter

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine, 2023. 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
1.9field-weighted citation impact, top 15% of its field
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, 6 citations in OpenAlex.

  1. 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

3 authors at 1 institution in 1 country.

Anastasia M CodirenziSchool of Biomedical Engineering, Western University, London, ON, Canada.
Brent A LantingDivision of Orthopaedic Surgery, Department of Surgery, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.
Matthew G TeeterSchool of Biomedical Engineering, Western University, London, ON, Canada.ORCID 0000-0002-3911-3171
Western University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Corrosion at the modular head-neck taper interface of total and hemiarthroplasty hip implants (trunnionosis) is a cause of implant failure and clinical concern. The Goldberg corrosion scoring method is considered the gold standard for observing trunnionosis, but it is labor-intensive to perform. This limits the quantity of implants retrieval studies typically analyze. Machine learning, particularly convolutional neural networks, have been used in various medical imaging applications and corrosion detection applications to help reduce repetitive and tedious image identification tasks. 725 retrieved modular femoral stem arthroplasty devices had their trunnion imaged in four positions and scored by an observer. A convolutional neural network was designed and trained from scratch using the images. There were four classes, each representing one of the established Goldberg corrosion classes. The composition of the classes were as follows: class 1 (

Indexed as

Arthroplasty, Replacement, HipHip ProsthesisCorrosionFemur HeadHigh-Throughput Screening AssaysHumansPolyethyleneProsthesis DesignProsthesis FailureReproducibility of ResultsPolyethylenearthroplastycorrosionMachine learningtrunnionosis

Identifiers

PMID37300244
PMCPMC10350735
OpenAlexW4379982989

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.