Institute for Theoretical Information Technology

A Deep Learning Approach for Managing Medical ConsumableMaterials in Intensive Care Units via Convolutional NeuralNetworks:Technical Proof-of-Concept Study

Authors

A. Peine, A. Hallawa, O. Schöffski, G. Dartmann, L. B. Fazlic, A. Schmeink, G. Marx, L. Martin,

Abstract

        High numbers of consumable medical materials (eg, sterile needles and swabs) are used during the daily routineof intensive care units (ICUs) worldwide. Although medical consumables largely contribute to total ICU hospital expenditure,many hospitals do not track the individual use of materials. Current tracking solutions meeting the specific requirements of themedical environment, like barcodes or radio frequency identification, require specialized material preparation and high infrastructureinvestment. This impedes the accurate prediction of consumption, leads to high storage maintenance costs caused by largeinventories, and hinders scientific work due to inaccurate documentation. Thus, new cost-effective and contactless methods forobject detection are urgently needed.

BibT_EX Reference Entry 

@article{PeHaScDaFaScMaMa19,
	author = {Arne Peine and Ahmed Hallawa and Oliver Sch{\"o}ffski and Guido Dartmann and Lejla Begic Fazlic and Anke Schmeink and Gernot Marx and Lukas Martin},
	title = "A Deep Learning Approach for Managing Medical ConsumableMaterials in Intensive Care Units via Convolutional NeuralNetworks:Technical Proof-of-Concept Study",
	pages = "1-13",
	journal = "JMIR Med Inform ",
	volume = "7",
	number = "4",
	doi = 10.2196/14806,
	month = Oct,
	year = 2019,
	hsb = RWTH-2020-04844,
	}

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