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A Deep Learning Approach for Managing Medical Consumable Materials in Intensive Care Units via Convolutional Neural Networks: Technical Proof-of-Concept Study

Authors:
Peine, A. ,  Hallawa, A. ,  Schöffski, O. ,  Dartmann, G. ,  Begic Fazlic, L. ,  Schmeink, A. ,  Marx, G. ,  Martin, L.
Journal:
JMIR Med Inform
Volume:
7
Page(s):
e14806
number:
4
Date:
Oct. 2019
Note:

url: medinform.jmir.org/2019/4/e14806/

ISSN:
2291-9694
DOI:
10.2196/14806
hsb:
RWTH-2020-00049
Language:
English

Abstract

High numbers of consumable medical materials (eg, sterile needles and swabs) are used during the daily routine of 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 the medical environment, like barcodes or radio frequency identification, require specialized material preparation and high infrastructure investment. This impedes the accurate prediction of consumption, leads to high storage maintenance costs caused by large inventories, and hinders scientific work due to inaccurate documentation. Thus, new cost-effective and contactless methods for object detection are urgently needed.

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