Evidence map›Paper›PMID 42618592›Full record

ArticleScientific reports2026

Default-threshold operating-point validation of a commercial chest radiograph AI system for selected CT-anchored thoracic findings: a bi-national multicenter retrospective study.

Yeliz Basar, Mustafa Ege Seker, Galina Ivanova Kirova-Nedyalkova, Elina Plamenova Milkovska, Boyan Emilov Manov, Atahan Tamturk, Ilke Tasci, Melih Karadag, Nuri Sarac, Selin Ardali Duzgun and 9 more

Abstract readMulticenter StudyValidation Study
In one paragraph

Article in Scientific reports, 2026. 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
–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

1 citing paper in PubMed.

  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

19 authors.

Yeliz Basar *Department Radiology, Acibadem Maslak Hospital, Istanbul, Turkey.ORCID 0000-0002-1321-3617
Mustafa Ege Seker *Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA.ORCID 0000-0001-7664-5786
Galina Ivanova Kirova-NedyalkovaImaging Department, Acibadem City Clinic Tokuda Hospital, Sofia, Bulgaria.ORCID 0000-0002-0158-8972
Elina Plamenova MilkovskaImaging Department, Acibadem City Clinic Tokuda Hospital, Sofia, Bulgaria.
Boyan Emilov ManovImaging Department, Acibadem City Clinic Tokuda Hospital, Sofia, Bulgaria.
Atahan TamturkDepartment of Radiology, School of Medicine, Hacettepe University, Ankara, Turkey.
Ilke TasciDepartment of Radiology, School of Medicine, Hacettepe University, Ankara, Turkey.ORCID 0009-0000-8143-6583
Melih KaradagDepartment of Radiology, School of Medicine, Hacettepe University, Ankara, Turkey.
Nuri SaracDepartment of Radiology, School of Medicine, Hacettepe University, Ankara, Turkey.
Selin Ardali DuzgunDepartment of Radiology, School of Medicine, Hacettepe University, Ankara, Turkey.ORCID 0000-0002-2623-2542
Erencan KarakocDepartment of Radiology, School of Medicine, Ege University, Izmir, Turkey.ORCID 0000-0002-8945-5364
Ahmet KarabulutDepartment of Radiology, Adiyaman Training and Research Hospital, Adiyaman, Turkey.
Cemre KaviDepartment of Radiology, School of Medicine, Ege University, Izmir, Turkey.ORCID 0000-0003-0181-1393
Kaan BuyukkirliDepartment of Radiology, School of Medicine, Ege University, Izmir, Turkey.
Mehmet Onur OnalDepartment of Radiology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey.ORCID 0009-0001-9300-2907
Deniz AlisDepartment of Radiology, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey.ORCID 0000-0002-7045-1793
Recep SavasDepartment of Radiology, School of Medicine, Ege University, Izmir, Turkey.ORCID 0000-0002-7520-760X
Ercan KaraarslanDepartment of Radiology, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey. ercan.karaarslan@acibadem.edu.tr.ORCID 0000-0002-4581-4273
Gamze DurhanDepartment of Radiology, School of Medicine, Hacettepe University, Ankara, Turkey.ORCID 0000-0002-6281-9287

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This retrospective bi-national multicenter diagnostic accuracy study evaluated the locked default-threshold performance of a commercial chest radiograph AI system for selected thoracic findings using a CT-anchored, radiographic-detectability reference framework. Consecutive eligible adult patients who underwent frontal chest radiography and chest CT at four tertiary-care centers in Turkey and Bulgaria between June and December 2024 were included. Reference labels were assigned using temporally paired CT, with adjudication of whether CT-confirmed abnormalities had a corresponding radiographic manifestation on the paired chest radiograph. For potentially dynamic findings, the allowable CT-CXR interval was restricted to ≤ 2 days. The AI system was evaluated at the manufacturer's default threshold of 0.50. Diagnostic performance was summarized using prevalence, sensitivity, specificity, positive predictive value, negative predictive value, and false-positive burden with 95% confidence intervals. The study included 940 patients with paired CXR-CT examinations. Reference-positive prevalence ranged from 2.2% for pneumothorax to 21.0% for pleural effusion. Sensitivity was highest for fracture (91%) and pneumothorax (90%) and lowest for atelectasis (64%); specificity ranged from 82% for consolidation/opacity to 98% for fracture and pneumothorax. PPV ranged from 43% to 63%, indicating a non-trivial false-positive burden at the evaluated operating point. Because continuous probability scores, human-reader comparison, and workflow outcomes were unavailable, these findings should be interpreted as default-threshold technical validation and support further evaluation of the system as radiologist-supervised decision support with local performance monitoring, rather than standalone diagnosis.

Indexed as

Artificial IntelligenceRadiography, ThoracicTomography, X-Ray ComputedAdultAgedBulgariaFemaleHumansMaleMiddle AgedRetrospective StudiesSensitivity and SpecificityTurkeyArtificial intelligenceChest radiographyClinical decision supportComputed tomographyDiagnostic accuracyMulticenter validationThoracic imaging

Identifiers

PMID42618592
PMCPMC13490484

What Socratic holds

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LicenceCC BY
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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 Socratic graph.