ArticleJournal of medical imaging (Bellingham, Wash.)2023
Predicting left/right lung volumes, thoracic cavity volume, and heart volume from subject demographics to improve lung transplant.
Article in Journal of medical imaging (Bellingham, Wash.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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.
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Who cites it
11 citing papers in PubMed, 8 citations in OpenAlex.
- Artificial intelligence and machine learning in heart and lung transplantation.EClinicalMedicine · 2026Review
- Beyond nodules: body composition as a biomarker for future lung cancer.European radiology · 2025Article
- Enhancing atrial fibrillation risk prediction in an observational cohort of tobacco-exposed individuals: the role of pulmonary function tests, symptom scores, and imaging.Respiratory research · 2025Observational
- Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine-Learning and CT Features.Clinical transplantation · 2025Article
- Predicting post-lung transplant survival in systemic sclerosis using CT-derived features from preoperative chest CT scans.European radiology · 2025Article
- A generalized health index: automated thoracic CT-derived biomarkers predict life expectancy.The British journal of radiology · 2025Article
- Understanding Machine Learning Applications in Lung Transplantation: A Narrative Review.Transplant international : official journal of the European Society for Organ Transplantation · 2025Review
- Application of artificial intelligence and machine learning in lung transplantation: a comprehensive review.Frontiers in digital health · 2025Review
- CT-Based Lung Size Matching in Delayed Chest Closure for Systemic Sclerosis Lung Transplantation.Clinical transplantation · 2024Article
- CT-Derived Features as Predictors of Clot Burden and Resolution.Bioengineering (Basel, Switzerland) · 2024Article
- Pulmonary circulatory system characteristics are associated with future lung cancer risk.Medical physics · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Purpose: Lung transplantation is the standard treatment for end-stage lung diseases. A crucial factor affecting its success is size matching between the donor's lungs and the recipient's thorax. Computed tomography (CT) scans can accurately determine recipient's lung size, but donor's lung size is often unknown due to the absence of medical images. We aim to predict donor's right/left/total lung volume, thoracic cavity, and heart volume from only subject demographics to improve the accuracy of size matching. Approach: A cohort of 4610 subjects with chest CT scans and basic demographics (i.e., age, gender, race, smoking status, smoking history, weight, and height) was used in this study. The right and left lungs, thoracic cavity, and heart depicted on chest CT scans were automatically segmented using U-Net, and their volumes were computed. Eight machine learning models [i.e., random forest, multivariate linear regression, support vector machine, extreme gradient boosting (XGBoost), multilayer perceptron (MLP), decision tree, Results: The MLP model demonstrated the best performance for predicting the thoracic cavity volume ( Conclusions: Our results demonstrate the feasibility of predicting lung, heart, and thoracic cavity volumes from subject demographics with superior performance compared with available studies in predicting lung volumes.
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Registered trials
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.