Highlighted Deep Learning based Identification of Pharmaceutical Blister Packages

Wang, Jing Syuan; Ambikapathi, ArulMurugan; Han, Yu; Chung, Sheng‐Luen; Ting, Hsien-Wei; Chen, Chih-Fang

doi:10.1109/etfa.2018.8502488

Cited by 18 publications

(8 citation statements)

References 8 publications

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“…Medicine Blister Package Identification (MedIdent) application is created to ensure the drug dispensing process in the hospital and assist the elderly in medicine reminding. The accuracy of the image classification model is improved by using a double-side transformed image dataset with download from Highlighted Deep Learning (HDL) work [13]. The dataset which is composed of twohundred seventy-two images for types of medicine blister packs, including 72 images of the front side and back side merged with a horizontal cropped background, is used for training the model.…”

Section: Bed Sensor For Elderly Care (Bedsense)mentioning

confidence: 99%

Data Analytics and Aggregation Platform for Comprehensive City-Scale AI Modeling

Sornlertlamvanich

Iamtrakul

Horanont

et al. 2023

Frontiers in Artificial Intelligence and Applications

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This research proposes an AI platform for data sharing across multiple domains. Since the data in the smart city concept are domain-specific processed, the existing smart city architecture is suffered from cross-domain data interpretation. To go beyond the digital transformation efforts in smart city development, the AI city is created on the architecture of cross-domain data connectivity and transform learning in the machine learning paradigm. In this research, the health and human behavioral data are targeted on human traceability and contactless technologies. To measure the inhabitants quality of life (QoL), the primary emotion expression study is conducted to interpret the emotional states and the mental health of people in the urbanized city. The results of information augmentation draw attention to the immersive visualization of the Thammasat model.

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Section: Bed Sensor For Elderly Care (Bedsense)mentioning

confidence: 99%

Data Analytics and Aggregation Platform for Comprehensive City-Scale AI Modeling

Sornlertlamvanich

Iamtrakul

Horanont

et al. 2023

Frontiers in Artificial Intelligence and Applications

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“…The data presented in Table 2 taking something else as the correct target [12]. The F1 measure is an evaluation that combines both sensitivity (recall) and precision.…”

Section: Outcome Measurementmentioning

confidence: 99%

“…Other major problems with the use of ADCs are the development of suitable software that can identify drugs accurately without the need for pre-processing of drugs or a large space in the pharmaceutical department before applying the systems. In addition, it needs to be ensured that these systems will not increase the burden on pharmacists during the prescription process [11,12].…”

Section: Introductionmentioning

confidence: 99%

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

Ting

Chung

Chen

et al. 2020

BMC Health Serv Res

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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.

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“…Compared with our early work [6], the improvements of this paper are: 1) a real-time BPIS is constructed by adopting an embedded technology which can optimizes the deep learning network. Two years of tests and optimization in hospitals prove that it is stable and effective.…”

Section: Introductionmentioning

confidence: 99%

Pharmaceutical Blister Package Identification Based on Induced Deep Learning

et al. 2021

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Prescription dispensing accuracy is of paramount importance for all hospitals. However, human errors are inevitable due to multiple reasons, such as fatigue, stress, heavy workload, lack of effective verification measures, mismanagement. Such human errors pose serious safety and health concerns on the part of patients and may as well lead to a series of medical disputes. Based on induced deep learning, this paper proposes a real-time Blister Package Identification System (BPIS) to assist pharmacists' drug verification and dispensing. Under the guidance of the induction strategy, image preprocessing is introduced to form a standardized image containing the front and back side of the blister package, which is subsequently sent to CNN-based object identification network for feature extraction and identification. This preprocessing method allows the identification system to promote the deep learning system to focus on feature learning to obtain more information about the appearance of the package ruling out confounding factors such as background noise, size, shape or positioning. In addition, this article collects and establishes an image dataset of adult lozenges. Under this dataset, this paper verifies the enhancement of Induced Deep Learning (IDL) on YOLO v2, ResNet, and SENet. By optimizing the deep learning identification network with the help of the embedded technology and a two-side extraction mechanism, a real-time BPIS is built. Long-term tests in hospitals prove the effectiveness of the proposed system.INDEX TERMS Blister package identification, deep learning, induction, dispensing error, CNN.

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Highlighted Deep Learning based Identification of Pharmaceutical Blister Packages

Cited by 18 publications

References 8 publications

Data Analytics and Aggregation Platform for Comprehensive City-Scale AI Modeling

Data Analytics and Aggregation Platform for Comprehensive City-Scale AI Modeling

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

Pharmaceutical Blister Package Identification Based on Induced Deep Learning

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