Explainable synthetic image generation to improve risk assessment of rare pediatric heart transplant rejection
Rattachement africain : us. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Expert microscopic analysis of cells obtained from frequent heart biopsies is vital for early detection of pediatric heart transplant rejection to prevent heart failure. Detection of this rare condition is prone to low levels of expert agreement due to the difficulty of identifying subtle rejection signs within biopsy samples. The rarity of pediatric heart transplant rejection also means that very few gold-standard images are available for developing machine learning models. To solve this urgent clinical challenge, we developed a deep learning model to automatically quantify rejection risk within digital images of biopsied tissue using an explainable synthetic data augmentation approach. We developed this explainable AI framework to illustrate how our progressive and inspirational generative adversarial network models distinguish between normal tissue images and those containing cellular rejection signs. To quantify biopsy-level rejection risk, we first detect local rejection features using a binary image classifier trained with expert-annotated and synthetic examples. We converted these local predictions into a biopsy-wide rejection score via an interpretable histogram-based approach. Our model significantly improves upon prior works with the same dataset with an area under the receiver operating curve (AUROC) of 98.84% for the local rejection detection task and 95.56% for the biopsy-rejection prediction task. A biopsy-level sensitivity of 83.33% makes our approach suitable for early screening of biopsies to prioritize expert analysis. Our framework provides a solution to rare medical imaging challenges currently limited by small datasets.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Explainable synthetic image generation to improve risk assessment of rare pediatric heart transplant rejection
- Date Crossref
- 01/03/2023
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
Georgia Institute of Technology Wallace H. Coulter Department of Biomedical Engineering pays non établi dans la noticeUniversité ou école supérieure
-
The Wallace H. Coulter Department of Biomedical Engineering pays non établi dans la noticeUniversité ou école supérieure
-
Jackson Memorial Hospital pays non établi dans la noticeÉtablissement de santé
-
University of Mississippi Medical Center Department of Pediatric Cardiology pays non établi dans la noticeÉtablissement de santé
-
Wayne State University Department of Pathology pays non établi dans la noticeUniversité ou école supérieure
-
Children's National pays non établi dans la noticeÉtablissement de santé
-
School of Electrical and Computer Engineering Georgia Institute of Technology pays non établi dans la noticeUniversité ou école supérieure
-
Department of Pediatric Cardiology pays non établi dans la noticeInstitution
Wallace H. Coulter Department of Biomedical Engineering — Georgia Institute of Technology, The Wallace H. Coulter Department of Biomedical Engineering et Jackson Memorial Hospital, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.