Evidence map›Paper›PMID 40848226›Full record

ArticleLa Radiologia medica2025

Application of artificial intelligence in the diagnosis of scaphoid fractures: impact of automated detection of scaphoid fractures in a real-life study.

Ana Isabel Hernáiz Ferrer, Chandra Bortolotto, Luisa Carone, Emma Maria Preda, Cristina Fichera, Alice Lionetti, Giulia Gambini, Eleonora Fresi, Federico Alberto Grassi, Lorenzo Preda

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In one paragraph

Article in La Radiologia medica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. Pooled it
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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.

Ana Isabel Hernáiz FerrerDiagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy.
Chandra BortolottoDiagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy.
Luisa CaroneRadiology Institute, Fondazione IRCCS Policlinico San Matteo, 27100, Pavia, Italy.
Emma Maria PredaDiagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy. emmamaria.preda01@universitadipavia.it.ORCID http://orcid.org/0009-0004-8624-6673
Cristina FicheraDiagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy.
Alice LionettiDiagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy.
Giulia GambiniSSD Biostatistica E Clinical Trial Center, Direzione Scientifica, Fondazione IRCCS Policlinico San Matteo, 27100, Pavia, Italy.
Eleonora FresiSSD Biostatistica E Clinical Trial Center, Direzione Scientifica, Fondazione IRCCS Policlinico San Matteo, 27100, Pavia, Italy.
Federico Alberto GrassiDiagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy.
Lorenzo PredaDiagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWe evaluated the diagnostic performance of two AI software programs (BoneView and RBfracture) in assisting non-specialist radiologists (NSRs) in detecting scaphoid fractures using conventional wrist radiographs (X-rays).

methodsWe retrospectively analyzed 724 radiographs from 264 patients with wrist trauma. Patients were classified into two groups: Group 1 included cases with a definitive diagnosis by a specialist radiologist (SR) based on X-rays (either scaphoid fracture or not), while Group 2 comprised indeterminate cases for the SRs requiring a CT scan for a final diagnosis. Indeterminate cases were defined as negative or doubtful X-rays in patients with persistent clinical symptoms. The X-rays were evaluated by AI and two NSRs, independently and in combination. We compared their diagnostic performances using sensitivity, specificity, area under the curve (AUC), and Cohen's kappa for diagnostic agreement.

resultsGroup 1 included 174 patients, with 80 cases (45.97%) of scaphoid fractures. Group 2 had 90 patients, of which 44 with uncertain diagnoses and 46 negative cases with persistent symptoms. Scaphoid fractures were identified in 51 patients (56.67%) in Group 2 after further CT imaging. In Group 1, AI performed similarly to NSRs (AUC: BoneView 0.83, RBfracture 0.84, NSR1 0.88, NSR2 0.90), without significant contribution of AI to the performance of NSRs. In Group 2, performances were lower (AUC: BoneView 0.62, RBfracture 0.65, NSR1 0.46, NSR2 0.63), but AI assistance significantly improved NSR performance (NSR2 + BoneView AUC = 0.75, p = 0.003; NSR2 + RBfracture AUC = 0.72, p = 0.030). Diagnostic agreement between NSR1 with AI support and SR was moderate (kappa = 0.576), and substantial for NSR2 (kappa = 0.712).

conclusionsAI tools may effectively assist NSRs, especially in complex scaphoid fracture cases.

Indexed as

Artificial IntelligenceFractures, BoneScaphoid BoneWrist InjuriesAdolescentAdultAgedFemaleHumansMaleMiddle AgedRetrospective StudiesSensitivity and SpecificityTomography, X-Ray ComputedYoung AdultArtificial intelligenceRadiographsScaphoid boneWrist fractures

Identifiers

PMID40848226
PMCPMC12546487

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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.