OK computer: AI reaches human-level accuracy in fission track counting in apatite and mica
Earth and Planetary Science Letters, vol.691, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 691
- Publication Date: 2026
- Doi Number: 10.1016/j.epsl.2026.120216
- Journal Name: Earth and Planetary Science Letters
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Artic & Antarctic Regions, Chemical Abstracts Core, Compendex, Environment Index, Geobase, INSPEC, Zoological Record, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO)
- Keywords: Artificial intelligence, Decision fatigue, Deep learning, Digital microscopy, Feature selection, Fission-track analysis, Image recognition
- Karadeniz Technical University Affiliated: No
Abstract
We report the first image-based comparative study of human and AI fission track counts using apatite standards, real-world samples, and mica external detectors. A trained AI algorithm (HALtracks 2D, Boone et al., 2025) and an independent human analyst analyzed identical image datasets. On pristine grains, AI counts match human results within 96%. On complex samples, the AI deviates by ∼20% on average, but remains indistinguishable from the average expert analyst in the previous inter-laboratory study (Tamer et al., 2025). This study is bounded by the exclusion of highly problematic grains with a noise-to-signal ratio (N/S > 0.5), which the human analyst filtered out due to high subjective uncertainty. Consequently, even without manual review, HALtracks 2D performs as well as the average expert on standard and moderately complex datasets. Our results also provide the first quantitative evidence of decision fatigue in human counting. As track densities increase, cognitive load triggers more permissive identification criteria, whereas the AI maintains consistent performance regardless of complexity. Current AI remains prone to miscounting dislocations as tracks, but this work provides a blueprint for improving AI discrimination by detailing visual rejection criteria. Even now, adopting AI analysis would resolve much of the inter-laboratory irreproducibility that zeta calibration only partially corrects, replacing idiosyncratic bias with a shared criterion. Combining this removal of variable operator bias with robust non-track discrimination could deliver the "total annealing" of manual fission track analysis in the near future.