Machine learning-supported interpretation of kidney graft elementary lesions in combination with clinical data

التفاصيل البيبلوغرافية
العنوان: Machine learning-supported interpretation of kidney graft elementary lesions in combination with clinical data
المؤلفون: Marc Labriffe, Jean-Baptiste Woillard, Wilfried Gwinner, Jan-Hinrich Braesen, Dany Anglicheau, Marion Rabant, Priyanka Koshy, Maarten Naesens, Pierre Marquet
المصدر: American Journal of Transplantation. 22:2821-2833
بيانات النشر: Elsevier BV, 2022.
سنة النشر: 2022
مصطلحات موضوعية: Graft Rejection, Machine Learning, Transplantation, Isoantibodies, Artificial Intelligence, Biopsy, Immunology and Allergy, Pharmacology (medical), Kidney
الوصف: Interpretation of kidney graft biopsies using the Banff classification is still heterogeneous. In this study, extreme gradient boosting classifiers learned from two large training datasets (n = 631 and 304 cases) where the "reference diagnoses" were not strictly defined following the Banff rules but from central reading by expert pathologists and further interpreted consensually by experienced transplant nephrologists, in light of the clinical context. In three external validation datasets (n = 3744, 589, and 360), the classifiers yielded a mean ROC curve AUC (95%CI) of: 0.97 (0.92-1.00), 0.97 (0.96-0.97), and 0.95 (0.93-0.97) for antibody-mediated rejection (ABMR); 0.94 (0.91-0.96), 0.94 (0.92-0.95), and 0.91 (0.88-0.95) for T cell-mediated rejection;0.96 (0.90-1.00) with all three for interstitial fibrosis-tubular atrophy. We also developed a classifier to discriminate active and chronic active ABMR with 95% accuracy. In conclusion, we built highly sensitive and specific artificial intelligence classifiers able to interpret kidney graft scoring together with a few clinical data and automatically diagnose rejection, with excellent concordance with the Banff rules and reference diagnoses made by a group of experts. Some discrepancies may point toward possible improvements that could be made to the Banff classification.
تدمد: 1600-6135
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9d762daecbd8b2990a25547065451b7a
https://doi.org/10.1111/ajt.17192
حقوق: CLOSED
رقم الأكسشن: edsair.doi.dedup.....9d762daecbd8b2990a25547065451b7a
قاعدة البيانات: OpenAIRE