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Free Pill Identifier by Picture: Advances and Applications
Introduction to Pill Identification Technology
The identification of pills through images has become increasingly important due to the rise in medication errors and the circulation of counterfeit drugs. Various technologies and methodologies have been developed to address this issue, leveraging advancements in image processing, deep learning, and mobile applications.
Pill-ID Systems: Features and Accuracy
Pill-ID: Matching and Retrieval System
The Pill-ID system is designed to match drug pill images based on features such as imprint, color, and shape. The system encodes color and shape information into a three-dimensional histogram and invariant moments, respectively, while the imprint is encoded using SIFT and MLBP descriptors. Experimental results show a retrieval accuracy of 73.04% at rank-1 and 84.47% at rank-20, demonstrating the system's effectiveness in identifying both illicit and legal drug pills.
Deep Learning-Based Systems
Deep learning has significantly improved pill identification accuracy. A notable system uses deep learning models to train on pill images and imprinted characters, achieving top-1 candidate accuracy levels of 85.6% in South Korea and 74.5% in the United States. This system can identify new pills without additional model updates, highlighting its robustness and adaptability.
Mobile Applications for Pill Identification
CNPI: Controlled and Narcotic Pill Identifier
The CNPI mobile application was developed to identify narcotic and controlled pills in Saudi Arabia. Users can identify pills by taking pictures and answering questions about the pill's shape, color, and imprint. Initial testing has shown the application's feasibility, acceptability, and practicability, making it a valuable tool for clinicians, patients, and government officials.
MobileDeepPill: A Mobile Deep Learning System
MobileDeepPill is a mobile application that uses a deep learning-based algorithm to recognize pills from images taken under real-world conditions. The system employs a triplet loss function, multi-CNNs model, and a deep model compression framework to ensure high recognition performance while maintaining a small footprint. This application won the NIH NLM Pill Image Recognition Challenge, demonstrating its effectiveness and potential societal impact.
National Library of Medicine Initiatives
Pill Image Recognition Challenge
The U.S. National Library of Medicine (NLM) organized a challenge to develop algorithms for matching consumer images of prescription pills with reference images. The challenge aimed to facilitate the identification of unknown prescription pills, especially in emergency settings. The winning algorithms achieved mean average precision scores of 0.27, 0.09, and 0.08, with the correct image among the top five ranked images 43% of the time.
Pillbox Database and Automatic Identification
Using the NLM Pillbox database, a system was developed to combine automatic segmentation, color, and shape identification with imprint recognition. Although imprint recognition remains challenging, the system has shown promising results in segmentation and color recognition, with errors measured at 1.9% and 2.2%, respectively.
Innovative Approaches and Future Directions
CandyCodes: Unique Edible Identifiers
CandyCodes is an innovative approach that uses nonpareils (tiny multicolor candy spheres) to create unique identifiers for each pill. By photographing and recording the pattern of nonpareils on each pill, manufacturers can create a database of authentic pills. Consumers can verify the authenticity of a pill by comparing its CandyCode with the database using a cellphone camera. This method offers a simple and effective way to combat counterfeit drugs.
Contextual Pill Recognition with Knowledge Graphs
The PIKA system enhances pill recognition accuracy by leveraging external knowledge from prescriptions. It uses a graph embedding model to extract relational features of pills and combines these with visual features for identification. This approach significantly improves recognition accuracy, demonstrating the potential of integrating external data sources in pill identification systems.
Conclusion
Advancements in image processing, deep learning, and mobile technology have led to the development of various systems and applications for pill identification. These innovations not only improve the accuracy and reliability of pill identification but also offer practical solutions for healthcare professionals and the general public. As technology continues to evolve, we can expect further improvements and new methodologies to enhance the safety and efficacy of medication use.
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