Ensuring the safety of medications, particularly antibiotics, is crucial in modern healthcare systems. The reliance on automation in pharmacy workflows increases the need for accurate identification of antibiotic packaging. As automation and high-throughput dispensing rise, visual assessments of pharmaceutical products are critical yet vulnerable, especially when faced with challenges like varied packaging designs, reflective materials, and high workloads in pharmacy settings.
Antibiotics are among the most commonly dispensed medications worldwide, crucial for patient safety and responsible prescribing practices. In several lower to middle-income countries, including Thailand, the market is saturated with generic antibiotic products that often feature similar packaging. This could lead to confusion and errors in medication dispensing.
Recent advances in computer vision and artificial intelligence (AI) have paved the way for more robust identification of pharmaceutical products based on their visual attributes. Previous studies largely focused on pill-level identification using characteristics such as shape and color. However, these methods may not perform well in routine pharmacy workflows, where identification of medications often occurs at the packaging level.
Optical character recognition (OCR) is also widely utilized, being effective in reading labels and batch codes. Yet, its performance can be influenced by factors like print quality and surface reflections. A comprehensive framework integrating both image analysis and OCR metrics is needed.
This study investigates how physical and textual features of antibiotic packaging can interact to influence automated identification outcomes through a unified analytical framework that uses unsupervised learning and DEA.
The research encompassed a systematic approach to automated identification of antibiotic packaging. High-resolution images of 36 commonly used antibiotic packages from Thailand were captured under standardized laboratory conditions. The imagery served as the basis for extracting significant features like texture descriptors and packaging area ratios.
Unsupervised methods, including K-means clustering, were performed to classify packaging into distinct groups based on image-derived characteristics, while DEA was utilized for comparative efficiency assessment of these features.
High-resolution images were obtained using diverse camera types and controlled lighting to ensure consistency. A methodology for isolating regions of interest (ROIs) corresponding to packaging involved employing advanced detection models.
A structured multi-stage pre-processing pipeline was applied to refine the captured images, including edge detection and contrast optimization for clearer feature extraction, ensuring that each package's critical attributes were measurable for analysis.
The analysis revealed nine distinct clusters of packaging based on image metrics. Packages exhibiting mid-range entropy values and packaging area ratios showed higher identification consistency. Additionally, OCR-derived confidence scores indicated significant effects on the outcomes of package identification.
DEA successfully benchmarked the efficiency of input-output configurations established in the clustering phase, clarifying which combinations of metrics led to more reliable identifications.
By integrating image features with textual confidence from OCR, this study presents a significant advancement in automated antibiotic identification frameworks. This methodological shift places packaging-level analysis at the forefront of improving medication safety and operational efficiency in pharmacy environments. The findings also indicate that utilizing a combination of unsupervised clustering and DEA can effectively bridge gaps between exploratory analysis and practical evaluations.
The structured approach not only emphasizes the importance of both visual characteristics and OCR metrics but also suggests that the adoption of such frameworks in pharmacy workflows could substantially enhance the accuracy of medication dispensing.
This research supports a transition towards more comprehensive analytical frameworks that incorporate physical packaging characteristics and OCR-derived features. Such integration is fundamental for optimizing automated identification systems in pharmacies, thereby bolstering overall medication safety. Enhanced identification protocols could lead to a more reliable and safer dispensing process for antibiotics, promoting better patient outcomes and a more efficient healthcare system.