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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
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 |
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author | Hehr, Matthias Sadafi, Ario Matek, Christian Lienemann, Peter Pohlkamp, Christian Haferlach, Torsten Spiekermann, Karsten Marr, Carsten |
author_facet | Hehr, Matthias Sadafi, Ario Matek, Christian Lienemann, Peter Pohlkamp, Christian Haferlach, Torsten Spiekermann, Karsten Marr, Carsten |
author_sort | Hehr, Matthias |
collection | PubMed |
description | 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 blood smears and found that high-attention cells identified by the model coincide with those labeled as diagnostically relevant by human experts. Based on over 80,000 single white blood cell images from digitized blood smears of 129 patients diagnosed with one of four WHO-defined genetic AML subtypes and 60 healthy controls, we trained SCEMILA, a single-cell based explainable multiple instance learning algorithm. SCEMILA could perfectly discriminate between AML patients and healthy controls and detected the APL subtype with an F1 score of 0.86±0.05 (mean±s.d., 5-fold cross-validation). Analyzing a novel multi-attention module, we confirmed that our algorithm focused with high concordance on the same AML-specific cells as human experts do. Applied to classify single cells, it is able to highlight subtype specific cells and deconvolve the composition of a patient’s blood smear without the need of single-cell annotation of the training data. Our large AML genetic subtype dataset is publicly available, and an interactive online tool facilitates the exploration of data and predictions. SCEMILA enables a comparison of algorithmic and expert decision criteria and can present a detailed analysis of individual patient data, paving the way to deploy AI in the routine diagnostics for identifying hematopoietic neoplasms. |
format | Online Article Text |
id | pubmed-10016704 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-100167042023-03-16 Explainable AI identifies diagnostic cells of genetic AML subtypes Hehr, Matthias Sadafi, Ario Matek, Christian Lienemann, Peter Pohlkamp, Christian Haferlach, Torsten Spiekermann, Karsten Marr, Carsten PLOS Digit Health Research Article 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 blood smears and found that high-attention cells identified by the model coincide with those labeled as diagnostically relevant by human experts. Based on over 80,000 single white blood cell images from digitized blood smears of 129 patients diagnosed with one of four WHO-defined genetic AML subtypes and 60 healthy controls, we trained SCEMILA, a single-cell based explainable multiple instance learning algorithm. SCEMILA could perfectly discriminate between AML patients and healthy controls and detected the APL subtype with an F1 score of 0.86±0.05 (mean±s.d., 5-fold cross-validation). Analyzing a novel multi-attention module, we confirmed that our algorithm focused with high concordance on the same AML-specific cells as human experts do. Applied to classify single cells, it is able to highlight subtype specific cells and deconvolve the composition of a patient’s blood smear without the need of single-cell annotation of the training data. Our large AML genetic subtype dataset is publicly available, and an interactive online tool facilitates the exploration of data and predictions. SCEMILA enables a comparison of algorithmic and expert decision criteria and can present a detailed analysis of individual patient data, paving the way to deploy AI in the routine diagnostics for identifying hematopoietic neoplasms. Public Library of Science 2023-03-15 /pmc/articles/PMC10016704/ /pubmed/36921004 http://dx.doi.org/10.1371/journal.pdig.0000187 Text en © 2023 Hehr et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Hehr, Matthias Sadafi, Ario Matek, Christian Lienemann, Peter Pohlkamp, Christian Haferlach, Torsten Spiekermann, Karsten Marr, Carsten Explainable AI identifies diagnostic cells of genetic AML subtypes |
title | Explainable AI identifies diagnostic cells of genetic AML subtypes |
title_full | Explainable AI identifies diagnostic cells of genetic AML subtypes |
title_fullStr | Explainable AI identifies diagnostic cells of genetic AML subtypes |
title_full_unstemmed | Explainable AI identifies diagnostic cells of genetic AML subtypes |
title_short | Explainable AI identifies diagnostic cells of genetic AML subtypes |
title_sort | explainable ai identifies diagnostic cells of genetic aml subtypes |
topic | Research Article |
url | 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 |
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