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Patients regularly continue assessment or treatment in other facilities thanthey began them in, receiving their previous imaging studies as a CD-ROM andrequiring clinical staff at the new hospital to import these studies into theirlocal database. However, between different facilities, standards fornomenclature, contents, or even medical procedures may vary, often requiringhuman intervention to accurately classify the received studies in the contextof the recipient hospital's standards. In this study, the authors present MOMO(MOdality Mapping and Orchestration), a deep learning-based approach toautomate this mapping process utilizing metadata substring matching and aneural network ensemble, which is trained to recognize the 76 most commonimaging studies across seven different modalities. A retrospective study isperformed 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 thelocal hospital's PACS database to train the neural networks. A set of 843completely anonymized external studies was hand-labeled to assess theperformance of our algorithm. Additionally, an ablation study was performed tomeasure the performance impact of the network ensemble in the algorithm, and acomparative performance test with a commercial product was conducted. Incomparison to a commercial product (96.20% predictive power, 82.86% accuracy,1.36% minor errors), a neural network ensemble alone performs theclassification task with less accuracy (99.05% predictive power, 72.69%accuracy, 10.3% minor errors). However, MOMO outperforms either by a largemargin in accuracy and with increased predictive power (99.29% predictivepower, 92.71% accuracy, 2.63% minor errors).

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