Gemtelligence: Accelerating Gemstone classification with Deep Learning
This addresses the need for consistent and automated analysis in the luxury goods and gemstone industry, representing a domain-specific incremental improvement.
The study tackled the problem of subjective and time-consuming gemstone origin and authenticity determination by proposing Gemtelligence, a deep learning approach that achieved comparable predictive performance to expensive ICP-MS analysis and human experts using cheaper data.
The value of luxury goods, particularly investment-grade gemstones, is greatly influenced by their origin and authenticity, sometimes resulting in differences worth millions of dollars. Traditionally, human experts have determined the origin and detected treatments on gemstones through visual inspections and a range of analytical methods. However, the interpretation of the data can be subjective and time-consuming, resulting in inconsistencies. In this study, we propose Gemtelligence, a novel approach based on deep learning that enables accurate and consistent origin determination and treatment detection. Gemtelligence comprises convolutional and attention-based neural networks that process heterogeneous data types collected by multiple instruments. Notably, the algorithm demonstrated comparable predictive performance to expensive laser-ablation inductively-coupled-plasma mass-spectrometry (ICP-MS) analysis and visual examination by human experts, despite using input data from relatively inexpensive analytical methods. Our innovative methodology represents a major breakthrough in the field of gemstone analysis by significantly improving the automation and robustness of the entire analytical process pipeline.