Evidence map›Paper›PMID 39192203›Full record

ArticleBMC musculoskeletal disorders2024

Clinical validation of artificial intelligence-based preoperative virtual reduction for Neer 3- or 4-part proximal humerus fractures.

Young Dae Jeon, Kwang-Hwan Jung, Moo-Sub Kim, Hyeonjoo Kim, Do-Kun Yoon, Ki-Bong Park

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC musculoskeletal disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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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

6 authors.

Young Dae JeonDepartment of Orthopaedic Surgery, University of Ulsan College of Medicine, Ulsan University Hospital, 25, Daehakbyeongwon-ro, Dong-Gu, Ulsan, 44033, Republic of Korea.ORCID https://orcid.org/0000-0003-4862-9679
Kwang-Hwan JungDepartment of Orthopaedic Surgery, University of Ulsan College of Medicine, Ulsan University Hospital, 25, Daehakbyeongwon-ro, Dong-Gu, Ulsan, 44033, Republic of Korea.ORCID https://orcid.org/0000-0002-6222-3803
Moo-Sub KimIndustrial R&D Center, KAVILAB Co. Ltd, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-8202-617X
Hyeonjoo KimIndustrial R&D Center, KAVILAB Co. Ltd, Seoul, Republic of Korea.ORCID https://orcid.org/0009-0007-9997-5578
Do-Kun YoonIndustrial R&D Center, KAVILAB Co. Ltd, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-0840-235X
Ki-Bong ParkDepartment of Orthopaedic Surgery, University of Ulsan College of Medicine, Ulsan University Hospital, 25, Daehakbyeongwon-ro, Dong-Gu, Ulsan, 44033, Republic of Korea. kbpark@uuh.ulsan.kr.ORCID https://orcid.org/0000-0002-2978-8300

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIf reduction images of fractures can be provided in advance with artificial-intelligence (AI)-based technology, it can assist with preoperative surgical planning. Recently, we developed the AI-based preoperative virtual reduction model for orthopedic trauma, which can provide an automatic segmentation and reduction of fractured fragments. The purpose of this study was to validate a quality of reduction model of Neer 3- or 4-part proximal humerus fractures established by AI-based technology.

methodsTo develop the AI-based preoperative virtual reduction model, deep learning performed the segmentation of fracture fragments, and a Monte Carlo simulation completed the virtual reduction to determine the best model. A total of 20 pre/postoperative three-dimensional computed tomography (CT) scans of proximal humerus fracture were prepared. The preoperative CT scans were employed as the input of AI-based automated reduction (AI-R) to deduce the reduction models of fracture fragments, meanwhile, the manual reduction (MR) was conducted using the same CT images. Dice similarity coefficient (DSC) and intersection over union (IoU) between the reduction model from the AI-R/MR and postoperative CT scans were evaluated. Working times were compared between the two groups. Clinical validity agreement (CVA) and reduction quality score (RQS) were investigated for clinical validation outcomes by 20 orthopedic surgeons.

resultsThe mean DSC and IoU were better when using AI-R that when using MR (0.78 ± 0.13 vs. 0.69 ± 0.16, p < 0.001 and 0.65 ± 0.16 vs. 0.55 ± 0.18, p < 0.001, respectively). The working time of AI-R was, on average, 1.41% of that of MR. The mean CVA of all cases was 81%±14.7% (AI-R, 82.25%±14.27%; MR, 76.75%±14.17%, p = 0.06). The mean RQS was significantly higher when AI-R compared with MR was used (91.47 ± 1.12 vs. 89.30 ± 1.62, p = 0.045).

conclusionThe AI-based preoperative virtual reduction model showed good performance in the reduction model in proximal humerus fractures with faster working times. Beyond diagnosis, classification, and outcome prediction, the AI-based technology can change the paradigm of preoperative surgical planning in orthopedic surgery. LEVEL OF EVIDENCE: Level IV.

Indexed as

Imaging, Three-DimensionalShoulder FracturesTomography, X-Ray ComputedAdultAgedArtificial IntelligenceDeep LearningFemaleHumansMaleMiddle AgedMonte Carlo MethodSurgery, Computer-AssistedArtificial intelligencePreoperative surgical planningProximal humerus fractureReduction quality

Identifiers

PMID39192203
PMCPMC11348784

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LicenceCC BY-NC-ND
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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.