ArticleScientific reports2019
Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 65 citations in OpenAlex.
- Connecting links with code: AI in morphological evolution.Frontiers in bioinformatics · 2026Review
- Early humans and the balance of power: Homo habilis as prey.Annals of the New York Academy of Sciences · 2025Article
- Artificial intelligence for the identification of taphonomic bio-accumulator agents: an actualistic test of potential faunal accumulation agency applied to Tritons Cave (Lleida, Spain).Royal Society open science · 2024Article
- Computer vision enables taxon-specific identification of African carnivore tooth marks on bone.Scientific reports · 2024Article
- Artificial Intelligence and Diagnostics in Medicine and Forensic Science.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial intelligence in forensic medicine and forensic dentistry.The Journal of forensic odonto-stomatology · 2023Review
- Article
- Quaternary taphonomy: understanding the past through traces.Scientific reports · 2022Article
- Article
- Deep classification of cut-marks on bones from Arroyo del Vizcaíno (Uruguay).Proceedings. Biological sciences · 2021Article
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2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Accurate identification of bone surface modifications (BSM) is crucial for the taphonomic understanding of archaeological and paleontological sites. Critical interpretations of when humans started eating meat and animal fat or when they started using stone tools, or when they occupied new continents or interacted with predatory guilds impinge on accurate identifications of BSM. Until now, interpretations of Plio-Pleistocene BSM have been contentious because of the high uncertainty in discriminating among taphonomic agents. Recently, the use of machine learning algorithms has yielded high accuracy in the identification of BSM. A branch of machine learning methods based on imaging, computer vision (CV), has opened the door to a more objective and accurate method of BSM identification. The present work has selected two extremely similar types of BSM (cut marks made on fleshed an defleshed bones) to test the immense potential of artificial intelligence methods. This CV approach not only produced the highest accuracy in the classification of these types of BSM until present (95% on complete images of BSM and 88.89% of images of only internal mark features), but it also has enabled a method for determining which inconspicuous microscopic features determine successful BSM discrimination. The potential of this method in other areas of taphonomy and paleobiology is enormous.
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