Developing approaches to incorporate donor-lung computed tomography images into machine learning models to predict severe primary graft dysfunction after lung transplantation.

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Bibliographic Details
Title: Developing approaches to incorporate donor-lung computed tomography images into machine learning models to predict severe primary graft dysfunction after lung transplantation.
Authors: Ma W; Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, USA., Oh I; Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, USA., Luo Y; Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, USA., Kumar S; Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, USA., Gupta A; Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, USA; Division of Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, USA., Lai AM; Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, USA., Puri V; Department of Surgery, Washington University School of Medicine, Saint Louis, Missouri, USA., Kreisel D; Department of Surgery, Washington University School of Medicine, Saint Louis, Missouri, USA., Gelman AE; Department of Surgery, Washington University School of Medicine, Saint Louis, Missouri, USA., Nava R; Department of Surgery, Washington University School of Medicine, Saint Louis, Missouri, USA., Witt CA; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA., Byers DE; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA., Halverson L; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA., Vazquez-Guillamet R; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA., Payne PRO; Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, USA., Sotiras A; Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, USA; Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, USA., Lu H; Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA., Niazi K; Department of Pathology, The Ohio State University, Columbus, OH, USA., Gurcan MN; Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA., Hachem RR; Division of Respiratory, Critical Care, and Occupational Pulmonary Medicine, University of Utah, Salt Lake City, Utah, USA., Michelson AP; Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, USA; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA. Electronic address: amichels@wustl.edu.
Source: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons [Am J Transplant] 2025 Jun; Vol. 25 (6), pp. 1339-1349. Date of Electronic Publication: 2025 Feb 07.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Country of Publication: United States NLM ID: 100968638 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1600-6143 (Electronic) Linking ISSN: 16006135 NLM ISO Abbreviation: Am J Transplant Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1600-6143
DOI:10.1016/j.ajt.2025.01.039