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Image-based identification and DEA-based optimization modeling of antibiotic packaging using unsupervised learning
Phakdee Sukpornsawan1, Yutthapoom Meepradist1, Titinun Auamnoy1
1Faculty of Pharmaceutical Sciences, Burapha University, Chonburi, Thailand.
Background:
Ensuring medication safety requires accurate identification of antibiotic packaging, especially within pharmacy automation and dispensing systems. Advanced imaging and machine learning offer novel avenues for physical package recognition.
Objective:
To investigate visual and textual features of antibiotic packages and evaluate their relationship with identification outcomes using unsupervised learning and efficiency-based analysis.
Methods:
Thirty-six antibiotic formulations from Thailand (2016-2021) were analyzed using binary imaging, entropy metrics, packaging area ratio (PAR), and optical character recognition (OCR). K-means clustering was applied to segment package groups, and data envelopment analysis (DEA) was used to assess relative efficiency without assuming predefined functional relationships between inputs and outputs.
Results:
Nine distinct image clusters were identified. Packages with mid-range entropy (7.1-7.5) and PAR (1.2-1.45) were associated with higher identification consistency. OCR text confidence influenced identification outcomes. DEA identified clusters with relatively efficient input-output configurations.
Conclusion:
Integrating image-derived metrics and OCR-based features supports automated antibiotic package identification. This framework provides a structured approach for evaluating packaging characteristics in pharmacy workflows.
