Evidence map›Paper›PMID 42592112›Full record

ArticleEClinicalMedicine2026

A deep learning model for opportunistic screening of chronic kidney disease using chest radiographs: a multicentre validation study in the USA.

Theodorus Dapamede, Kéana Aitcheson, Pola Lydia Lagari, Awais Farooq, Peter Louis, Chad Robichaux, Frank Li, Bardia Khosravi, Mohammadreza Chavoshi, Aawez Mansuri and 6 more

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Article in EClinicalMedicine, 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
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0citing papers in PubMed
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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

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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

16 authors.

Theodorus DapamedeDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, USA.
Kéana AitchesonGKT School of Medical Education, King's College London, London, UK.
Pola Lydia LagariDepartment of Medicine, University of Illinois Chicago, Chicago, USA.
Awais FarooqDepartment of Medicine, Medical University of South Carolina, Charleston, USA.
Peter LouisDepartment of Pathology, University of Illinois Chicago, Chicago, USA.
Chad RobichauxDepartment of Biomedical Informatics, Emory University, Atlanta, USA.
Frank LiDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, USA.
Bardia KhosraviDepartment of Radiology, Yale University, New Haven, USA.
Mohammadreza ChavoshiDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, USA.
Aawez MansuriDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, USA.
Rohan Satya IsaacDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, USA.
Beatrice Brown-MulryDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, USA.
Hari TrivediDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, USA.
William GalanterDepartment of Medicine, University of Illinois Chicago, Chicago, USA.
Ayis PyrrosDuly Health and Care, Downers Grove, USA.
Judy GichoyaDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, USA.

Funding

Opportunistic Screening for ASCVD using a Multimodal Deep Learning Risk Prediction ModelR01HL167811 · NHLBI · MAYO CLINIC ARIZONA · PI Imon Banerjee, Judy Gichoya · 2024 to 2026
$2.1M
NHLBI NIH HHS R01 HL167811
6 · The paper itself

Abstract

Background: Chronic kidney disease (CKD) affects 843 million people worldwide yet remains underdiagnosed. Current risk stratification, including the CKD Prognosis Consortium (CKD-PC) score, relies on laboratory data rarely evaluated outside nephrology settings. Chest radiographs (CXR) are among the most frequently performed imaging procedures globally, offering an opportunistic screening pathway. CXR-CKD5, a CXR-derived AI score for predicting 5-year incident CKD without laboratory data, was developed and externally validated. Methods: In this retrospective two-centre study conducted at two sites in the USA, 97,553 adults (≥18 years) without baseline CKD who underwent routine CXR were included: Emory University (development cohort, 2008-2021, n = 75,683) and University of Illinois Chicago (external validation cohort, 2010-2020, n = 21,870). Patients with a CKD diagnosis within 90 days of the index CXR were excluded. A convolutional neural network extracted 10 cardiopulmonary and metabolic risk features from routine CXRs, which were used to train an XGBoost accelerated failure time model. The primary outcome was 5-year incident CKD. Discrimination (C-index) and calibration were evaluated against a clinical base model and the CKD-PC score. Findings: CXR-CKD5 achieved apparent C-indices of 0.774 (95% CI: 0.768-0.780) in development (optimism-corrected: 0.737) and 0.713 (95% CI: 0.697-0.729) at external validation. A combined imaging-clinical model achieved the highest external validation discrimination (C-index 0.734, 95% CI: 0.719-0.751). Performance was strongest in non-diabetic patients (development C-index 0.783, 95% CI: 0.775-0.790; external validation 0.714, 95% CI: 0.689-0.739) and attenuated in diabetic patients across both cohorts (development C-index 0.658, 95% CI: 0.645-0.671; external validation 0.607, 95% CI: 0.580-0.634). Absolute risk was overestimated at external validation, indicating that calibration would be required before deployment at new institutions. A threshold of ≥15% predicted risk identified the top 20% at risk. Interpretation: CXR-CKD5 demonstrates that routine chest radiographs can identify patients at elevated CKD risk without laboratory data, offering a scalable opportunistic screening pathway. Recalibration and prospective evaluation assessing clinical outcomes and workflow integration are needed before deployment. Funding: NHLBI and the University of Illinois Chicago AI.Health4All Initiative.

Indexed as

Artificial intelligenceChest radiographyChronic kidney diseaseOpportunistic screening

Identifiers

PMID42592112
PMCPMC13463794

What Socratic holds

Textmetadata
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 Socratic graph.