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Deep Learning–driven classification of external DICOM studies for PACS archiving

OBJECTIVES: Over the course of their treatment, patients often switch hospitals, requiring staff at the new hospital to import external imaging studies to their local database. In this study, the authors present MOdality Mapping and Orchestration (MOMO), a Deep Learning–based approach to automate th...

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Autores principales: Jonske, Frederic, Dederichs, Maximilian, Kim, Moon-Sung, Keyl, Julius, Egger, Jan, Umutlu, Lale, Forsting, Michael, Nensa, Felix, Kleesiek, Jens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9705446/
https://www.ncbi.nlm.nih.gov/pubmed/35788757
http://dx.doi.org/10.1007/s00330-022-08926-w
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author Jonske, Frederic
Dederichs, Maximilian
Kim, Moon-Sung
Keyl, Julius
Egger, Jan
Umutlu, Lale
Forsting, Michael
Nensa, Felix
Kleesiek, Jens
author_facet Jonske, Frederic
Dederichs, Maximilian
Kim, Moon-Sung
Keyl, Julius
Egger, Jan
Umutlu, Lale
Forsting, Michael
Nensa, Felix
Kleesiek, Jens
author_sort Jonske, Frederic
collection PubMed
description OBJECTIVES: Over the course of their treatment, patients often switch hospitals, requiring staff at the new hospital to import external imaging studies to their local database. In this study, the authors present MOdality Mapping and Orchestration (MOMO), a Deep Learning–based approach to automate this mapping process by combining metadata analysis and a neural network ensemble. METHODS: A set of 11,934 imaging series with existing anatomical labels was retrieved from the PACS database of the local hospital to train an ensemble of neural networks (DenseNet-161 and ResNet-152), which process radiological images and predict the type of study they belong to. We developed an algorithm that automatically extracts relevant metadata from imaging studies, regardless of their structure, and combines it with the neural network ensemble, forming a powerful classifier. A set of 843 anonymized external studies from 321 hospitals was hand-labeled to assess performance. We tested several variations of this algorithm. RESULTS: MOMO achieves 92.71% accuracy and 2.63% minor errors (at 99.29% predictive power) on the external study classification task, outperforming both a commercial product (82.86% accuracy, 1.36% minor errors, 96.20% predictive power) and a pure neural network ensemble (72.69% accuracy, 10.3% minor errors, 99.05% predictive power) performing the same task. We find that the highest performance is achieved by an algorithm that combines all information into one vote-based classifier. CONCLUSION: Deep Learning combined with metadata matching is a promising and flexible approach for the automated classification of external DICOM studies for PACS archiving. KEY POINTS: • The algorithm can successfully identify 76 medical study types across seven modalities (CT, X-ray angiography, radiographs, MRI, PET (+CT/MRI), ultrasound, and mammograms). • The algorithm outperforms a commercial product performing the same task by a significant margin (> 9% accuracy gain). • The performance of the algorithm increases through the application of Deep Learning techniques. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-022-08926-w.
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spelling pubmed-97054462022-11-30 Deep Learning–driven classification of external DICOM studies for PACS archiving Jonske, Frederic Dederichs, Maximilian Kim, Moon-Sung Keyl, Julius Egger, Jan Umutlu, Lale Forsting, Michael Nensa, Felix Kleesiek, Jens Eur Radiol Imaging Informatics and Artificial Intelligence OBJECTIVES: Over the course of their treatment, patients often switch hospitals, requiring staff at the new hospital to import external imaging studies to their local database. In this study, the authors present MOdality Mapping and Orchestration (MOMO), a Deep Learning–based approach to automate this mapping process by combining metadata analysis and a neural network ensemble. METHODS: A set of 11,934 imaging series with existing anatomical labels was retrieved from the PACS database of the local hospital to train an ensemble of neural networks (DenseNet-161 and ResNet-152), which process radiological images and predict the type of study they belong to. We developed an algorithm that automatically extracts relevant metadata from imaging studies, regardless of their structure, and combines it with the neural network ensemble, forming a powerful classifier. A set of 843 anonymized external studies from 321 hospitals was hand-labeled to assess performance. We tested several variations of this algorithm. RESULTS: MOMO achieves 92.71% accuracy and 2.63% minor errors (at 99.29% predictive power) on the external study classification task, outperforming both a commercial product (82.86% accuracy, 1.36% minor errors, 96.20% predictive power) and a pure neural network ensemble (72.69% accuracy, 10.3% minor errors, 99.05% predictive power) performing the same task. We find that the highest performance is achieved by an algorithm that combines all information into one vote-based classifier. CONCLUSION: Deep Learning combined with metadata matching is a promising and flexible approach for the automated classification of external DICOM studies for PACS archiving. KEY POINTS: • The algorithm can successfully identify 76 medical study types across seven modalities (CT, X-ray angiography, radiographs, MRI, PET (+CT/MRI), ultrasound, and mammograms). • The algorithm outperforms a commercial product performing the same task by a significant margin (> 9% accuracy gain). • The performance of the algorithm increases through the application of Deep Learning techniques. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-022-08926-w. Springer Berlin Heidelberg 2022-07-05 2022 /pmc/articles/PMC9705446/ /pubmed/35788757 http://dx.doi.org/10.1007/s00330-022-08926-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Imaging Informatics and Artificial Intelligence
Jonske, Frederic
Dederichs, Maximilian
Kim, Moon-Sung
Keyl, Julius
Egger, Jan
Umutlu, Lale
Forsting, Michael
Nensa, Felix
Kleesiek, Jens
Deep Learning–driven classification of external DICOM studies for PACS archiving
title Deep Learning–driven classification of external DICOM studies for PACS archiving
title_full Deep Learning–driven classification of external DICOM studies for PACS archiving
title_fullStr Deep Learning–driven classification of external DICOM studies for PACS archiving
title_full_unstemmed Deep Learning–driven classification of external DICOM studies for PACS archiving
title_short Deep Learning–driven classification of external DICOM studies for PACS archiving
title_sort deep learning–driven classification of external dicom studies for pacs archiving
topic Imaging Informatics and Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9705446/
https://www.ncbi.nlm.nih.gov/pubmed/35788757
http://dx.doi.org/10.1007/s00330-022-08926-w
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