MOMO – Deep Learning-driven classification of external DICOM studies for PACS archivation

12/01/2021
by   Frederic Jonske, et al.
0

Patients regularly continue assessment or treatment in other facilities than they began them in, receiving their previous imaging studies as a CD-ROM and requiring clinical staff at the new hospital to import these studies into their local database. However, between different facilities, standards for nomenclature, contents, or even medical procedures may vary, often requiring human intervention to accurately classify the received studies in the context of the recipient hospital's standards. In this study, the authors present MOMO (MOdality Mapping and Orchestration), a deep learning-based approach to automate this mapping process utilizing metadata substring matching and a neural network ensemble, which is trained to recognize the 76 most common imaging studies across seven different modalities. A retrospective study is performed to measure the accuracy that this algorithm can provide. To this end, a set of 11,934 imaging series with existing labels was retrieved from the local hospital's PACS database to train the neural networks. A set of 843 completely anonymized external studies was hand-labeled to assess the performance of our algorithm. Additionally, an ablation study was performed to measure the performance impact of the network ensemble in the algorithm, and a comparative performance test with a commercial product was conducted. In comparison to a commercial product (96.20 1.36 classification task with less accuracy (99.05 accuracy, 10.3 margin in accuracy and with increased predictive power (99.29 power, 92.71

READ FULL TEXT

page 1

page 4

page 5

page 9

research
09/17/2021

CardiSort: a convolutional neural network for cross vendor automated sorting of cardiac MR images

Objectives: To develop an image-based automatic deep learning method to ...
research
11/13/2018

Neuroimaging Modality Fusion in Alzheimer's Classification Using Convolutional Neural Networks

Automated methods for Alzheimer's disease (AD) classification have the p...
research
02/15/2020

Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning

Uncertainty estimation and ensembling methods go hand-in-hand. Uncertain...
research
05/14/2023

Predicting Unplanned Readmissions in the Intensive Care Unit: A Multimodality Evaluation

A hospital readmission is when a patient who was discharged from the hos...
research
07/02/2018

Confounding variables can degrade generalization performance of radiological deep learning models

Early results in using convolutional neural networks (CNNs) on x-rays to...
research
10/29/2019

Semi-Supervised Natural Language Approach for Fine-Grained Classification of Medical Reports

Although machine learning has become a powerful tool to augment doctors ...
research
07/01/2021

Long-Short Ensemble Network for Bipolar Manic-Euthymic State Recognition Based on Wrist-worn Sensors

Manic episodes of bipolar disorder can lead to uncritical behaviour and ...

Please sign up or login with your details

Forgot password? Click here to reset