Article in Radiology. Artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing 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.
Jong Hyuk Lee *From the Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., J.M.G., H.K.); Department of Biomedical Engineering, Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon, Korea (D.L.); Massachusetts General Hospital Cardiovascular Imaging Research Center and Harvard Medical School, Boston, Mass (M.T.L., V.K.R.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea (J.M.G.); Cancer Research Institute, Seoul National University, Seoul, Korea (J.M.G.); Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Korea (Y.C.); and Department of Internal Medicine, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea (S.H.C.).ORCID 0000-0002-9594-683X
Dongheon Lee *From the Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., J.M.G., H.K.); Department of Biomedical Engineering, Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon, Korea (D.L.); Massachusetts General Hospital Cardiovascular Imaging Research Center and Harvard Medical School, Boston, Mass (M.T.L., V.K.R.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea (J.M.G.); Cancer Research Institute, Seoul National University, Seoul, Korea (J.M.G.); Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Korea (Y.C.); and Department of Internal Medicine, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea (S.H.C.).ORCID 0000-0002-3121-7099
Michael T LuFrom the Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., J.M.G., H.K.); Department of Biomedical Engineering, Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon, Korea (D.L.); Massachusetts General Hospital Cardiovascular Imaging Research Center and Harvard Medical School, Boston, Mass (M.T.L., V.K.R.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea (J.M.G.); Cancer Research Institute, Seoul National University, Seoul, Korea (J.M.G.); Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Korea (Y.C.); and Department of Internal Medicine, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea (S.H.C.).ORCID 0000-0003-4696-9610
Vineet K RaghuFrom the Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., J.M.G., H.K.); Department of Biomedical Engineering, Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon, Korea (D.L.); Massachusetts General Hospital Cardiovascular Imaging Research Center and Harvard Medical School, Boston, Mass (M.T.L., V.K.R.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea (J.M.G.); Cancer Research Institute, Seoul National University, Seoul, Korea (J.M.G.); Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Korea (Y.C.); and Department of Internal Medicine, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea (S.H.C.).ORCID 0000-0003-3524-3945
Jin Mo GooFrom the Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., J.M.G., H.K.); Department of Biomedical Engineering, Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon, Korea (D.L.); Massachusetts General Hospital Cardiovascular Imaging Research Center and Harvard Medical School, Boston, Mass (M.T.L., V.K.R.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea (J.M.G.); Cancer Research Institute, Seoul National University, Seoul, Korea (J.M.G.); Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Korea (Y.C.); and Department of Internal Medicine, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea (S.H.C.).ORCID 0000-0003-1791-7942
Yunhee ChoiFrom the Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., J.M.G., H.K.); Department of Biomedical Engineering, Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon, Korea (D.L.); Massachusetts General Hospital Cardiovascular Imaging Research Center and Harvard Medical School, Boston, Mass (M.T.L., V.K.R.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea (J.M.G.); Cancer Research Institute, Seoul National University, Seoul, Korea (J.M.G.); Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Korea (Y.C.); and Department of Internal Medicine, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea (S.H.C.).ORCID 0000-0002-7422-0466
Seung Ho Choi *From the Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., J.M.G., H.K.); Department of Biomedical Engineering, Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon, Korea (D.L.); Massachusetts General Hospital Cardiovascular Imaging Research Center and Harvard Medical School, Boston, Mass (M.T.L., V.K.R.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea (J.M.G.); Cancer Research Institute, Seoul National University, Seoul, Korea (J.M.G.); Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Korea (Y.C.); and Department of Internal Medicine, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea (S.H.C.).ORCID 0000-0002-9834-9926
Hyungjin Kim *From the Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea (J.H.L., J.M.G., H.K.); Department of Biomedical Engineering, Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon, Korea (D.L.); Massachusetts General Hospital Cardiovascular Imaging Research Center and Harvard Medical School, Boston, Mass (M.T.L., V.K.R.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea (J.M.G.); Cancer Research Institute, Seoul National University, Seoul, Korea (J.M.G.); Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Korea (Y.C.); and Department of Internal Medicine, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea (S.H.C.).ORCID 0000-0003-0722-0033
Funding
Deep learning to assess cardiovascular disease risk from chest imagingK01HL168231 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Vineet Raghu · 2024 to 2026
$526k
NHLBI NIH HHS K01 HL168231
6 · The paper itself
Abstract
Purpose To assess the prognostic value of a deep learning-based chest radiographic age (hereafter, CXR-Age) model in a large external test cohort of Asian individuals. Materials and Methods This single-center, retrospective study included chest radiographs from consecutive, asymptomatic Asian individuals aged 50-80 years who underwent health checkups between January 2004 and June 2018. This study performed a dedicated external test of a previously developed CXR-Age model, which predicts an age adjusted based on the risk of all-cause mortality. Adjusted hazard ratios (HRs) of CXR-Age for all-cause, cardiovascular, lung cancer, and respiratory disease mortality were assessed using multivariable Cox or Fine-Gray models, and their added values were evaluated by likelihood ratio tests. Results A total of 36 924 individuals (mean chronological age, 58 years ± 7 [SD]; CXR-Age, 60 years ± 5; 22 352 male) were included. During a median follow-up of 11.0 years, 1250 individuals (3.4%) died, including 153 cardiovascular (0.4%), 166 lung cancer (0.4%), and 98 respiratory (0.3%) deaths. CXR-Age was a significant risk factor for all-cause (adjusted HR at chronological age of 50 years, 1.03; at 60 years, 1.05; at 70 years, 1.07), cardiovascular (adjusted HR, 1.11), lung cancer (adjusted HR for individuals who formerly smoked, 1.12; for those who currently smoke, 1.05), and respiratory disease (adjusted HR, 1.12) mortality (
Indexed as
Deep LearningRadiography, ThoracicAgedAged, 80 and overAgingFemaleHumansLung NeoplasmsMaleMiddle AgedPrognosisRetrospective StudiesRisk FactorsConventional RadiographyConvolutional Neural Network (CNN)HeartLungMediastinumOutcomes AnalysisPrognosisQuantificationThorax
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.
External Testing of a Deep Learning Model to Estimate Biologic Age Using Chest Radiographs. · full record | OpenQuestion