A drug identification model developed using deep learning technologies: experience of a medical center in Taiwan

Abstract

Background: Issuing of correct prescriptions is a foundation of patient safety. Medication errors represent one of the most important problems in health care, with 'look-alike and sound-alike' (LASA) being the lead error. Existing solutions to prevent LASA still have their limitations. Deep learning techniques have revolutionized identification classifiers in many fields. In search of better image-based solutions for blister package identification problem, this study using a baseline deep learning drug identification (DLDI) aims to understand how identification confusion of look-alike images by human occurs through the cognitive counterpart of deep learning solutions and thereof to suggest further solutions to approach them. Methods: We collected images of 250 types of blister-packaged drug from the Out-Patient Department (OPD) of a medical center for identification. The deep learning framework of You Only Look Once (YOLO) was adopted for implementation of the proposed deep learning. The commonly-used F1 score, defined by precision and recall for large numbers of identification tests, was used as the performance criterion. This study trained and compared the proposed models based on images of either the front-side or back-side of blister-packaged drugs. Results: Our results showed that the total training time for the front-side model and back-side model was 5 h 34 min and 7 h 42 min, respectively. The F1 score of the back-side model (95.99%) was better than that of the frontside model (93.72%). Conclusions:In conclusion, this study constructed a deep learning-based model for blister-packaged drug identification, with an accuracy greater than 90%. This model outperformed identification using conventional computer vision solutions, and could assist pharmacists in identifying drugs while preventing medication errors caused by look-alike blister packages. By integration into existing prescription systems in hospitals, the results of this study indicated that using this model, drugs dispensed could be verified in order to achieve automated prescription and dispensing.

A Novel Method for Reducing Dispensing Error in Hospital Pharmacy

“…Our opinion is consistent with that of the latest scienti c literature. Ting et al [14] collected images of 250 types of blister-packaged drugs from the outpatient pharmacy of a medical center for identi cation and used the YOLO (version was not speci ed) algorithm to implement deep learning. The results show that the accuracy was greater than 90%.…”

Drug Verification System Based on Deep Learning Multiscale Rotating Rectangle Detector and Feature Embedding

“…Eleven categories of pill boxes were successfully identified, all of which were flat pill boxes, so there was no need to solve the problem of multiple differently placed feature surfaces. In 2020, Ting et al (11) proposed the use of YOLOv2 deep learning technology to identify blister packs of drugs. They developed a single-stage detection system that included positioning and recognition that trained a model for each side of the blister pack.…”

A Clinical Decision Support System for Diabetes Patients with Deep Learning: Experience of a Taiwan Medical Center

“…Recently, deep learning methods have dramatically improved different fields of medical care and research [21,22]. They have also been used as the core methods to build the CDSS [23,24]. For example, convolutional neural networks (CNNs) are used to process image data and recurrent neural networks (RNNs) are used for sequential pattern problems [23,25].…”