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

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.

Automated summary

Dispensing errors, which are the third leading cause of death in the United States, have prompted the World Health Organization (WHO) to initiate the Medication Without Harm Campaign. This study proposes a novel two-stage induced deep learning (TSIDL) system to efficiently classify similar drugs with diverse packaging, achieving a state-of-the-art classification accuracy of 99.39% and an inference time of only 3.12 ms per image.