Evidence map›Paper›PMID 41372552›Full record

ArticleJapanese journal of radiology2026

Automated interstitial lung abnormalities detection at CT: external validation and potential recognition of traction bronchiectasis/bronchiolectasis.

Yusei Nakamura, Taiki Fukuda, Kota Aoyagi, Masami Kawagishi, Yuki Ko, Noriaki Wada, Takuya Hino, Tomoyuki Hida, Meike W Vernooij, Daniel Bos and 12 more

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

Article in Japanese journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

22 authors.

Yusei NakamuraCenter for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA. ynakamura4@bwh.harvard.edu.ORCID http://orcid.org/0009-0000-4619-8372
Taiki FukudaCenter for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA.
Kota AoyagiCanon Medical Systems Corporation, Tochigi, Japan.
Masami KawagishiCanon Inc., Tokyo, Japan.
Yuki KoCenter for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA.
Noriaki WadaDepartment of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Takuya HinoDepartment of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Tomoyuki HidaDepartment of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Meike W VernooijDepartment of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
Daniel BosDepartment of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
Daan W LothDepartment of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.
Masahiro OzakiInstitute for Advanced Diagnosis for Rare Diseases & Conditions K.K., Tokyo, Japan.
Akihiro KogaCanon Medical Systems Corporation, Tochigi, Japan.
Heida BjarnadottirFaculty of Medicine, University of Iceland, Reykjavik, Iceland.
Valborg GudmundsdottirFaculty of Medicine, University of Iceland, Reykjavik, Iceland.
Gunnar GudmundssonFaculty of Medicine, University of Iceland, Reykjavik, Iceland.
Vilmundur GudnasonFaculty of Medicine, University of Iceland, Reykjavik, Iceland.
Mizuki NishinoCenter for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA.
David C ChristianiPulmonary and Critical Care Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Gary M HunninghakePulmonary and Critical Care Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Kousei IshigamiDepartment of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Hiroto HatabuCenter for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA.

Funding

Clinical Genetics and Screening for Pulmonary FibrosisR01HL130974 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI GARY MATTHEW HUNNINGHAKE, Benjamin Alexander Raby · 2016 to 2026
$10.0M
Genetic and Genomic Characterization of the Occurrence and Progression of Interstitial Lung AbnormalitiesR01HL135142 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI MICHAEL H. CHO, GARY MATTHEW HUNNINGHAKE · 2017 to 2026
$7.7M
Interstitial Lung Abnormalities: Defining the Phenotype, Causes, and Consequences.R01HL111024 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI HUNNINGHAKE, GARY MATTHEW · 2013 to 2022
$6.4M
NHLBI NIH HHS R01 HL111024NHLBI NIH HHS R01 HL130974NHLBI NIH HHS R01 HL135142
6 · The paper itself

Abstract

purposeAn artificial intelligence (AI) system for detecting interstitial lung abnormalities (ILA) was previously developed but requires external validation. This study aimed to examine the robustness across different populations and investigate associations between the system outputs and traction bronchiectasis/bronchiolectasis severity patterns. MATERIALS AND

methodsCT scans from population-based samples of the Rotterdam Study (2018-2019) and the Age Gene/Environment Susceptibility Reykjavik (AGES-Reykjavik) Study (baseline CT: 2002-2006, follow-up CT: 2007-2011) were used in this secondary analysis of the two cohorts. The AI system calculated ILA probability score (AI score) in the range from 0 to 1. Three experienced readers evaluated independently all CT scans for ILA, and two chest radiologists assessed traction bronchiectasis/bronchiolectasis using the 4-scale traction bronchiectasis/bronchiolectasis index (TBI) for severity by consensus. Receiver operating characteristic (ROC) analysis and Kruskal-Wallis test were used for statistical analysis.

resultsThe system analyzed 932 CT scans of the Rotterdam Study (mean participant age, 79.6 years ± 4.3 (SD), 482 women) and 5242 CT scans of the AGES-Reykjavik Study (mean participant age, 76.4 years ± 5.6, 3032 women), and achieved area under the ROC curve of 0.841 (95% CI 0.804, 0.879) and 0.823 (95% CI 0.798, 0.847), respectively. AI scores correlated with readers' certainty, decreasing from unanimous ILA cases to No-ILA cases. Higher baseline AI scores correlated with greater severity of traction bronchiectasis/bronchiolectasis (TBI-3: 0.931 [IQR, 0.911-0.932], TBI-2: 0.738 [IQR, 0.406-0.880], TBI-1: 0.537 [IQR, 0.317-0.761], TBI-0: 0.250 [IQR, 0.136-0.455]).

conclusionThe system demonstrated robust ILA detection performance across different populations, with AI scores showing associations with traction bronchiectasis/bronchiolectasis severity.

Indexed as

Artificial IntelligenceBronchiectasisLung Diseases, InterstitialRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAgedAged, 80 and overFemaleHumansLungMaleSeverity of Illness IndexArtificial intelligenceCTExternal validationInterstitial lung abnormalitiesMachine learningTraction bronchiectasis/bronchiolectasis

Identifiers

PMID41372552
PMCPMC13038644

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.