Convolutional neural network-based ambient light-independent panel digit surveillance technique for infusion pumps

Abstract

For effective patient therapy and improved patient safety, it is critical to administer medication accurately in accordance with doctor’s prescription. However, accidents owing to the erroneous programing of infusion pumps caused by users have been consistently reported in several documents. In this study, the authors propose a novel surveillance technique for infusion pumps to continuously monitor the variations in panel digits using a convolutional neural network model, and evaluate the performance of the implemented technique. During the experimental evaluation, 1st-step ROIs and 2nd-step ROIs were successfully extracted from the frame images regardless of the ambient lighting conditions. The final accuracies of the implemented CNN model are 99.9% for both the training (172,800 images) and validation (1080 images) dataset while the final losses for the training and validation datasets are 0.48 and 0.45 after 13th epoch, respectively. In the 24-h continuous monitoring test, the accuracy of the model for volume recognition considering all the 1440 measurements (960 for day-lighting and 480 for night-lighting) is 95.5%, whereas in day-lighting and night-lighting modes the accuracies of the model are 98.2% and 90.0%, respectively. Based on these experimental results, the proposed surveillance technique incorporating infusion pumps is expected to improve the safety of patients who need long-term treatments via infusion pumps, reducing the burden on the nurses and hospitals.

Deep learning-based monitoring technique for real-time intravenous medication bag status

“…First, the current prototype cannot be applied in the ward during nighttime, when the lights are completely turned off for sleep. To enable nighttime monitoring, several modifications, that is, using an infrared camera and backlight-independent image-processing techniques, should be applied to the current prototype pole in further studies [ 32 ]. Second, in Fig.…”