Cargando…

Explainable AI identifies diagnostic cells of genetic AML subtypes

Explainable AI is deemed essential for clinical applications as it allows rationalizing model predictions, helping to build trust between clinicians and automated decision support tools. We developed an inherently explainable AI model for the classification of acute myeloid leukemia subtypes from bl...

Descripción completa

Detalles Bibliográficos
Autores principales:	Hehr, Matthias, Sadafi, Ario, Matek, Christian, Lienemann, Peter, Pohlkamp, Christian, Haferlach, Torsten, Spiekermann, Karsten, Marr, Carsten
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Public Library of Science 2023
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10016704/ https://www.ncbi.nlm.nih.gov/pubmed/36921004 http://dx.doi.org/10.1371/journal.pdig.0000187

Ejemplares similares

Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set
por: Matek, Christian, et al.
Publicado: (2021)

RedTell: an AI tool for interpretable analysis of red blood cell morphology
por: Sadafi, Ario, et al.
Publicado: (2023)

P694: RISK ASSESSMENT ACCORDING TO IPSS-M IS SUPERIOR TO AML ELN 2022 RISK CLASSIFICATION IN MDS/AML PATIENTS DEFINED BY ICC – IS AN INCLUSION INTO AML TRIALS JUSTIFIED?
por: Huber, Sandra, et al.
Publicado: (2023)

Risk assessment according to IPSS-M is superior to AML ELN risk classification in MDS/AML overlap patients defined by ICC
por: Huber, Sandra, et al.
Publicado: (2023)

AML classification in the year 2023: How to avoid a Babylonian confusion of languages
por: Huber, Sandra, et al.
Publicado: (2023)

SHAPR predicts 3D cell shapes from 2D microscopic images
por: Waibel, Dominik J.E., et al.
Publicado: (2022)

Specific subtype distribution with impact on prognosis of TP53 single-hit and double-hit events in AML and MDS
por: Stengel, Anna, et al.
Publicado: (2023)

P533: A FULLY AUTOMATED SUPERVISED AI-BASED CELL CLASSIFIER TO ACCURATELY FLAG PATHOLOGICAL SIGNATURE CELLS IN 28,285 REAL-WORLD BLOOD SMEARS
por: Maier, Christopher, et al.
Publicado: (2023)

The Role of Therapeutic Leukapheresis in Hyperleukocytotic AML
por: Pastore, Friederike, et al.
Publicado: (2014)

AML, NOS and AML-MRC as defined by multilineage dysplasia share a common mutation pattern which is distinct from AML-MRC as defined by MDS-related cytogenetics
por: Fuhrmann, Irene, et al.
Publicado: (2022)

Explainable AI
por: Samek, Wojciech, et al.
Publicado: (2019)

Inhibiting glutaminase in acute myeloid leukemia: metabolic dependency of selected AML subtypes
por: Matre, Polina, et al.
Publicado: (2016)

Microenvironmental Hypoxia regulates FLT3 expression and biology in AML
por: Sironi, Silvia, et al.
Publicado: (2015)

Molecular characterization of AML with RUNX1-RUNX1T1 at diagnosis and relapse reveals net loss of co-mutations
por: Höllein, Alexander, et al.
Publicado: (2019)

Interplay of TP53 allelic state, blast count, and complex karyotype on survival of patients with AML and MDS
por: Stengel, Anna, et al.
Publicado: (2023)

Medical Informatics and Explainable AI
por: Aliyev, Taghi
Publicado: (2019)

SF3B1 mutations in AML are strongly associated with MECOM rearrangements and may be indicative of an MDS pre-phase
por: Huber, Sandra, et al.
Publicado: (2022)

Indeterminate and oncogenic potential: CHIP vs CHOP mutations in AML with NPM1 alteration
por: Cappelli, Luca Vincenzo, et al.
Publicado: (2021)

Putting explainable AI in context: institutional explanations for medical AI
por: Theunissen, Mark, et al.
Publicado: (2022)

Tens of images can suffice to train neural networks for malignant leukocyte detection
por: Schouten, Jens P. E., et al.
Publicado: (2021)

Hands-On Explainable AI (XAI) with Python
por: Rothman, Denis
Publicado: (2020)

Decision Theory Meets Explainable AI
por: Främling, Kary
Publicado: (2020)

Explainable AI in Fintech Risk Management
por: Bussmann, Niklas, et al.
Publicado: (2020)

Explainable AI as evidence of fair decisions
por: Leben, Derek
Publicado: (2023)

Informing antimicrobial stewardship with explainable AI
por: Cavallaro, Massimo, et al.
Publicado: (2023)

Explainable AI via learning to optimize
por: Heaton, Howard, et al.
Publicado: (2023)

Survey of Explainable AI Techniques in Healthcare
por: Chaddad, Ahmad, et al.
Publicado: (2023)

Impact of extramedullary disease in AML patients undergoing sequential RIC for HLA-matched transplantation: occurrence, risk factors, relapse patterns, and outcome
por: Fraccaroli, Alessia, et al.
Publicado: (2023)

Expl(AI)n It to Me – Explainable AI and Information Systems Research
por: Bauer, Kevin, et al.
Publicado: (2021)

Frequency and Prognostic Impact of CEBPA Proximal, Distal and Core Promoter Methylation in Normal Karyotype AML: A Study on 623 Cases
por: Fasan, Annette, et al.
Publicado: (2013)

P689: CLASSIFICATION OF MDS AND AML BASED SOLELY ON GENETICS OMITTING BLAST COUNTING – A WAY TO RESOLVE THE DISCREPANCIES BETWEEN CLASSIFICATIONS ACCORDING TO WHO AND ICC?
por: Haferlach, Claudia, et al.
Publicado: (2023)

Maturation State-Specific Alternative Splicing in FLT3-ITD and NPM1 Mutated AML
por: Wojtuszkiewicz, Anna, et al.
Publicado: (2021)

Modeling early treatment response in AML from cell-free tumor DNA
por: Wang, Dantong, et al.
Publicado: (2023)

The time has come for next-generation sequencing in routine diagnostic workup in hematology
por: Haferlach, Torsten
Publicado: (2021)

Dual-targeting triplebody 33-16-123 (SPM-2) mediates effective redirected lysis of primary blasts from patients with a broad range of AML subtypes in combination with natural killer cells
por: Braciak, Todd A., et al.
Publicado: (2018)

Advancing leukemia diagnostics: Role of Next Generation Sequencing (NGS) in acute myeloid leukemia
por: Haferlach, Torsten
Publicado: (2020)

The challenges of explainable AI in biomedical data science
por: Han, Henry, et al.
Publicado: (2022)

Explainable AI and Multi-Modal Causability in Medicine
por: Holzinger, Andreas
Publicado: (2021)

Automated machine learning and explainable AI (AutoML-XAI) for metabolomics: improving cancer diagnostics
por: Bifarin, Olatomiwa O., et al.
Publicado: (2023)

More than just sound: Harnessing metadata to improve neural network classifiers for medical auscultation
por: Matek, Christian
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_3mil3dprtvvbvt7j9efud3kvcn