  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% predictive power, 82.86% accuracy,
1.36% minor errors), a neural network ensemble alone performs the
classification task with less accuracy (99.05% predictive power, 72.69%
accuracy, 10.3% minor errors). However, MOMO outperforms either by a large
margin in accuracy and with increased predictive power (99.29% predictive
power, 92.71% accuracy, 2.63% minor errors).
