Evidence map›Paper›PMID 38802519›Full record

ArticleScientific reports2024

Proximal femur fracture detection on plain radiography via feature pyramid networks.

İlkay Yıldız Potter, Diana Yeritsyan, Sarah Mahar, Nadim Kheir, Aidin Vaziri, Melissa Putman, Edward K Rodriguez, Jim Wu, Ara Nazarian, Ashkan Vaziri

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Review
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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

10 authors.

İlkay Yıldız PotterBioSensics, LLC, 57 Chapel Street, Newton, MA, 02458, USA. ilkay.yildiz@biosensics.com.
Diana YeritsyanCarl J. Shapiro Department of Orthopaedic Surgery, Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School, 330 Brookline Avenue, Stoneman 10, Boston, MA, 02215, USA.
Sarah MaharCarl J. Shapiro Department of Orthopaedic Surgery, Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School, 330 Brookline Avenue, Stoneman 10, Boston, MA, 02215, USA.
Nadim KheirCarl J. Shapiro Department of Orthopaedic Surgery, Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School, 330 Brookline Avenue, Stoneman 10, Boston, MA, 02215, USA.
Aidin VaziriBioSensics, LLC, 57 Chapel Street, Newton, MA, 02458, USA.
Melissa PutmanDivision of Endocrinology, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
Edward K RodriguezCarl J. Shapiro Department of Orthopaedic Surgery, Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School, 330 Brookline Avenue, Stoneman 10, Boston, MA, 02215, USA.
Jim WuDepartment of Radiology, Massachusetts General Brigham (MGB) and Harvard Medical School, 75 Francis Street, Boston, MA, 02215, USA.
Ara NazarianCarl J. Shapiro Department of Orthopaedic Surgery, Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School, 330 Brookline Avenue, Stoneman 10, Boston, MA, 02215, USA.
Ashkan VaziriBioSensics, LLC, 57 Chapel Street, Newton, MA, 02458, USA.

Funding

Automatic, Opportunistic Surveillance of Hip Bone Fragility in X-ray ImagesR44AG081031 · NIA · BIOSENSICS, LLC · PI NAZARIAN, ARA, VAZIRI, ASHKAN · 2023 to 2024
$1.9M
NIA NIH HHS R44 AG081031NIH HHS R44AG081031
6 · The paper itself

Abstract

Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240-310% by 2050. Hip fractures are predominantly diagnosed by radiologist review of radiographs. In this study, we developed a deep learning model by extending the VarifocalNet Feature Pyramid Network (FPN) for detection and localization of proximal femur fractures from plain radiography with clinically relevant metrics. We used a dataset of 823 hip radiographs of 150 subjects with proximal femur fractures and 362 controls to develop and evaluate the deep learning model. Our model attained 0.94 specificity and 0.95 sensitivity in fracture detection over the diverse imaging dataset. We compared the performance of our model against five benchmark FPN models, demonstrating 6-14% sensitivity and 1-9% accuracy improvement. In addition, we demonstrated that our model outperforms a state-of-the-art transformer model based on DINO network by 17% sensitivity and 5% accuracy, while taking half the time on average to process a radiograph. The developed model can aid radiologists and support on-premise integration with hospital cloud services to enable automatic, opportunistic screening for hip fractures.

Indexed as

Deep LearningRadiographyAgedAged, 80 and overFemaleFemoral FracturesHip FracturesHumansMaleMiddle AgedNeural Networks, ComputerProximal Femoral FracturesSensitivity and SpecificityDeep learningFractureHipPlain radiographyProximal femur

Identifiers

PMID38802519
PMCPMC11130146

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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.