Evidence map›Paper›PMID 37115217›Full record

ArticleEuropean radiology2023

An artificial intelligence framework for the diagnosis of prosthetic joint infection based on

Liangbing Nie, Zhenkui Sun, Fengling Shan, Chengfan Li, Xuehai Ding, Chentian Shen

Abstract read
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In one paragraph

Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 6% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.

  1. Machine Learning Based Diagnostic Models for Hip and Knee Prosthetic Joint Infection: A Systematic Review.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2026
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4 · The record

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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

6 authors at 3 institutions in 1 country.

Liangbing Nie *School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China.
Zhenkui Sun *Department of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China.
Fengling Shan *Department of Nuclear Medicine, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, 201399, China.
Chengfan LiSchool of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China.
Xuehai DingSchool of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China.
Chentian ShenDepartment of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China. shenchentian@126.com.ORCID http://orcid.org/0000-0001-5943-4418
Shanghai University of Engineering Science · CNShanghai Jiao Tong University · CNFudan University · CN

Funding

Shanghai Municipal Health Commission Special Project for Clinical Research 20204Y0256the National Natural Science Foundation of China 81901773the Scientific Research Foundation Provided by Pudong Hospital Affiliated to Fudan University YJRCJJ201907the Shanghai Key Clinical Specialty of Medical Imaging shslczdzk03203the Shanghai Sixth People's Hospital Scientific Research Project YNLC201903
6 · The paper itself

Abstract

objectivesDynamic bone scintigraphy (DBS) is the first widely reliable and simple imaging modality in nuclear medicine that can be used to diagnose prosthetic joint infection (PJI). We aimed to apply artificial intelligence to diagnose PJI in patients after total hip or knee arthroplasty (THA or TKA) based on

methodsA total of 449 patients (255 THAs and 194 TKAs) with a final diagnosis were retrospectively enrolled and analyzed. The dataset was divided into a training and validation set and an independent test set. A customized framework composed of two data preprocessing algorithms and a diagnosis model (dynamic bone scintigraphy effective neural network, DBS-eNet) was compared with mainstream modified classification models and experienced nuclear medicine specialists on corresponding datasets.

resultsIn the fivefold cross-validation test, diagnostic accuracies of 86.48% for prosthetic knee infection (PKI) and 86.33% for prosthetic hip infection (PHI) were obtained using the proposed framework. On the independent test set, the diagnostic accuracies and AUC values were 87.74% and 0.957 for PKI and 86.36% and 0.906 for PHI, respectively. The customized framework demonstrated better overall diagnostic performance compared to other classification models and showed superiority in diagnosing PKI and consistency in diagnosing PHI compared to specialists.

conclusionThe customized framework can be used to effectively and accurately diagnose PJI based on KEY POINTS: • The proposed framework in the current study achieved high diagnostic performance for prosthetic knee infection (PKI) and prosthetic hip infection (PHI) with AUC values of 0.957 and 0.906, respectively. • The customized framework demonstrated better overall diagnostic performance compared to other classification models. • Compared to experienced nuclear medicine physicians, the customized framework showed superiority in diagnosing PKI and consistency in diagnosing PHI.

Indexed as

Arthritis, InfectiousArthroplasty, Replacement, KneeProsthesis-Related InfectionsArtificial IntelligenceHumansRadionuclide ImagingRetrospective StudiesTomography, X-Ray ComputedArtificial intelligenceHip arthroplastyInfectious arthritisKnee arthroplastyRadionuclide imaging

Identifiers

PMID37115217
OpenAlexW4367319998

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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.