ArticleNature communications2023
A machine-learning approach to human ex vivo lung perfusion predicts transplantation outcomes and promotes organ utilization.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 44 citations in OpenAlex.
- Artificial intelligence and machine learning in heart and lung transplantation.EClinicalMedicine · 2026Review
- MetaboSense: An Integrated Quantum Dot-Mediated Aptamer Assay and Modular Microfluidic Platform for Continuous In-Line Monitoring of Lung Metabolism During Ex Vivo Perfusion.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Primary Graft Dysfunction After Lung Transplantation: A Temporal Classification and Machine Learning Clustering.Transplantation direct · 2026Article
- Digital twins of ex vivo human lungs enable accurate and personalized evaluation of therapeutic efficacy.Nature biotechnology · 2026Article
- From myth to machine: artificial intelligence and the future of organ transplantation.BJGP open · 2026Article
- Transforming lung transplantation with artificial intelligence: a narrative review from organ allocation to post-transplant management.Journal of thoracic disease · 2026Review
- In silico perturbations provide multivariate interpretability in predicting post-lung transplant outcomes.Scientific reports · 2026Article
- Time as a Therapeutic Ally: The Promise of Long-Term Solid Organ and Tissue Perfusion.Transplant international : official journal of the European Society for Organ Transplantation · 2026Review
- Review
- Advancing lung transplantation through machine learning and artificial intelligence.Current opinion in pulmonary medicine · 2025Review
- North American expert consensus on the clinical role of ex vivo lung perfusion (EVLP) with acellular perfusate.Journal of thoracic disease · 2025Review
- Ethics and Algorithms to Navigate AI's Emerging Role in Organ Transplantation.Journal of clinical medicine · 2025Review
- Application of artificial intelligence and machine learning in lung transplantation: a comprehensive review.Frontiers in digital health · 2025Review
- Review
- Immunocompatibility in transplantation: adapting to a changing therapeutic landscape.Frontiers in immunology · 2025Review
- Understanding Machine Learning Applications in Lung Transplantation: A Narrative Review.Transplant international : official journal of the European Society for Organ Transplantation · 2025Review
- Short-term and long-term outcomes of lung transplantation from marginal donors: a single-center retrospective study.Journal of thoracic disease · 2024Article
- Noninflammatory Causes of Pulmonary Edema During Ex Vivo Lung Perfusion.Annals of thoracic surgery short reports · 2024Article
- Machine learning-based prediction of DNA G-quadruplex folding topology with G4ShapePredictor.Scientific reports · 2024Article
- Improving prognostic accuracy in lung transplantation using unique features of isolated human lung radiographs.NPJ digital medicine · 2024Article
Corrections and comments
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Authors and funding
17 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ex vivo lung perfusion (EVLP) is a data-intensive platform used for the assessment of isolated lungs outside the body for transplantation; however, the integration of artificial intelligence to rapidly interpret the large constellation of clinical data generated during ex vivo assessment remains an unmet need. We developed a machine-learning model, termed InsighTx, to predict post-transplant outcomes using n = 725 EVLP cases. InsighTx model AUROC (area under the receiver operating characteristic curve) was 79 ± 3%, 75 ± 4%, and 85 ± 3% in training and independent test datasets, respectively. Excellent performance was observed in predicting unsuitable lungs for transplantation (AUROC: 90 ± 4%) and transplants with good outcomes (AUROC: 80 ± 4%). In a retrospective and blinded implementation study by EVLP specialists at our institution, InsighTx increased the likelihood of transplanting suitable donor lungs [odds ratio=13; 95% CI:4-45] and decreased the likelihood of transplanting unsuitable donor lungs [odds ratio=0.4; 95%CI:0.16-0.98]. Herein, we provide strong rationale for the adoption of machine-learning algorithms to optimize EVLP assessments and show that InsighTx could potentially lead to a safe increase in transplantation rates.
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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.