Discussion on "Infectious medical waste characterization using X-ray transmission with Machine learning"

This article is a discussion on the non-contact automated characterization framework for infectious healthcare waste by Vielsack et al. (2026). Two critical scientific and operational boundaries are critically analyzed while recognizing their pioneering combination of X-ray transmission (XRT) and machine learning in field conditions. First, single-energy XRT technology limitations hinder fine-grained polymer discrimination; overlapping attenuation profiles cannot differentiate high-value recyclable polymers from problematic polyvinyl chloride (PVC), risking contamination of downstream mechanical recycling. Second, the deployment of the model is hampered by structural barriers to transferability, such as systemic waste co-mingling, non-standardized disposal vessels and the absence of centralized institutional registers within developing countries, which were exposed in the disciplined field trial by the 38.2 % database matching failure. To realize global validity, this letter puts forward the paradigm shift from broad batch identification (e.g., RFID, barcodes) to definitive material quantification through: three-dimensional tomographic sensing (specifically prioritizing limited-angle computed tomography over standard two-dimensional configurations), digital procurement data-harmonization, and indelible manufacturing-stage tracking mechanisms (QR/RFID).