A Novel Two-Stage Induced Deep Learning System for Classifying Similar Drugs with Diverse Packaging

Abstract

Dispensing errors play a crucial role in various medical errors, unfortunately emerging as the third leading cause of death in the United States. This alarming statistic has spurred the World Health Organization (WHO) into action, leading to the initiation of the Medication Without Harm Campaign. The primary objective of this campaign is to prevent dispensing errors from occurring and ensure patient safety. Due to the rapid development of deep learning technology, there has been a significant increase in the development of automatic dispensing systems based on deep learning classification to avoid dispensing errors. However, most previous studies have focused on developing deep learning classification systems for unpackaged pills or drugs with the same type of packaging. However, in the actual dispensing process, thousands of similar drugs with diverse packaging within a healthcare facility greatly increase the risk of dispensing errors. In this study, we proposed a novel two-stage induced deep learning (TSIDL)-based system to classify similar drugs with diverse packaging efficiently. The results demonstrate that the proposed TSIDL method outperforms state-of-the-art CNN models in all classification metrics. It achieved a state-of-the-art classification accuracy of 99.39%. Moreover, this study also demonstrated that the TSIDL method achieved an inference time of only 3.12 ms per image. These results highlight the potential of real-time classification for similar drugs with diverse packaging and their applications in future dispensing systems, which can prevent dispensing errors from occurring and ensure patient safety efficiently.

Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review

“…One of the best-known human factor-related prescription error examples is that of look-alike or sound-alike (LASA) drugs, in which the error occurs due to orthographic or phonetic similarity or packaging between drugs [ 72 , 73 ]. AI could be useful in clinical practice to prevent so-called “look-alike” errors due to similar packaging between different medications by applying deep learning to drug images [ 52 , 58 ].…”

TPTM-HANN-GA: A Novel Hyperparameter Optimization Framework Integrating the Taguchi Method, an Artificial Neural Network, and a Genetic Algorithm for the Precise Prediction of Cardiovascular Disease Risk

“…Franchini, G. (2024) proposed a green approach to hyperparameter tuning in deep learning models, utilizing performance predictors to minimize computational costs and environmental impact, thereby accelerating the neural architecture search process and improving efficiency in tasks such as image denoising and classification [32]. Other studies have explored their potential in medical image segmentation tasks (Malhotra et al, 2022) [33], real-time classification of diversely packaged drugs (You et al, 2023) [34], toxicology research for predicting compound toxicity (Pantic et al, 2022) [35], classifying liver cancer from histopathology images (Lin et al, 2021) [36], lung cancer recognition (He et al, 2022) [37], and even predicting the outcome of diabetic foot ulcer treatments (Poradzka et al, 2023) [38]. Furthermore, research such as Krasteva et al (2023) demonstrates their optimization capabilities for specific tasks such as arrhythmia classification [39].…”