Image-based identification and DEA-based optimization modeling of antibiotic packaging using unsupervised learning techniques

Phakdee Sukpornsawan, Yutthapoom Meepradist, Titinun Auamnoy, Ureerat Suksawatchon, Somchart Chokchaitam and Suthabordee Muongmee

PLOS ONE, 2026, vol. 21, issue 7, 1-16

Abstract: 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.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0354277

DOI: 10.1371/journal.pone.0354277

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