Pills identifier
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Summary
10 papers analyzed
These studies suggest that various machine learning and deep learning systems can effectively identify pills based on features like imprint, color, and shape, with accuracy rates ranging from 73.04% to 97.16%.
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Advances in Pill Identification Technologies
Introduction to Pill Identification
Pill identification is crucial for ensuring medication safety, preventing counterfeit drugs, and aiding law enforcement in tracking illicit substances. Various technologies have been developed to address these needs, leveraging image processing, machine learning, and unique physical identifiers.
Image-Based Pill Identification Systems
Pill-ID System: Imprint, Color, and Shape Analysis
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 using a three-dimensional histogram and invariant moments, respectively. Imprints are encoded as feature vectors derived from SIFT and MLBP descriptors. This method has shown a retrieval accuracy of 73.04% at rank-1 and 84.47% at rank-20 when tested on a database of both illicit and legal drug pill images.
Deep Learning Approaches
Deep learning models have significantly improved the accuracy of pill identification. One system uses a combination of image classification and text detection models to recognize pill features and imprints. This system achieved an accuracy of 85.6% in South Korea and 74.5% in the United States for untrained pill types, demonstrating the potential of AI in reducing medication errors. Another deep learning-based system focuses on imprint extraction and description, achieving a rank-1 matching accuracy of 90.46% and 97.16% within the top five ranks.
Minimal Labeled Data and Data Augmentation
To address the challenge of limited labeled training data, some systems use data augmentation techniques to generate synthetic pill images. These methods, combined with convolutional neural networks (CNNs), have shown promising results in identifying pills from single images, even under varying environmental conditions.
Unique Physical Identifiers
CandyCodes: Edible Identifiers
CandyCodes introduce a novel approach by using nonpareils (tiny multicolor candy spheres) as unique physical identifiers for pills. Each pill is randomly decorated with nonpareils, creating a unique pattern that can be photographed and stored in a database. Consumers can verify the authenticity of a pill by comparing its CandyCode with the manufacturer's database using a cellphone camera. This method is highly scalable and resistant to counterfeiting, offering a simple yet effective solution for pharmaceutical authentication.
Pill Identification Using Machine Learning Models
Color and Shape Recognition
Color and shape are fundamental features for pill identification. Systems that combine automatic segmentation, color, and shape recognition with preprocessing steps have shown success in identifying pills. For instance, a system using the National Library of Medicine Pillbox database achieved low error rates in segmentation and color recognition, although imprint recognition remains challenging.
Imprint-Based Identification
Imprints are critical for distinguishing between pills that are otherwise similar in color and shape. Neural network-based systems have been developed to extract and analyze imprint features, achieving high accuracy in identifying pills. One such system reported an accuracy of 94.4% when tested on a limited number of pill types.
Conclusion
The advancements in pill identification technologies, ranging from image-based systems to unique physical identifiers like CandyCodes, are making significant strides in ensuring medication safety and authenticity. These technologies leverage sophisticated image processing, machine learning, and innovative physical markers to provide reliable and scalable solutions for pill identification. As these systems continue to evolve, they hold the promise of reducing medication errors, combating counterfeit drugs, and aiding law enforcement in tracking illicit substances.
Sources and full results
Most relevant research papers on this topic
10
Pill Identification with Imprints Using a Neural Network
10
4 Citations
2015