Artificial intelligence in paleontology

Yu, Congyu; Qin, Fangbo; Watanabe, Akinobu; Yao, Weiqi; Li, Ying; Qin, Zichuan; Liu, Yuming; Wang, Haibing; Jiangzuo, Qigao; Hsiang, Allison Y.; Ma, Chao; Rayfield, Emily; Benton, Michael J.; Xu, Xing

doi:10.1016/j.earscirev.2024.104765

Cited by 4 publications

(1 citation statement)

References 145 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the introduction of conventional, synchrotron, and neutron micro-CT, datasets have become increasingly larger and more detailed, resulting in substantially longer post-processing times, in particular with respect to data segmentation 2,[4][5][6] With palaeontological material, where the density differences between the surrounding rock matrix and the fossil itself are usually low, manual image segmentation is often the only way to digitally extract the Regions of Interest (ROIs) from the CT slice stacks 7,8 . This process can take weeks to months to complete, thus being a considerable bottleneck in making data available for research.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Early Triassic Vertebrate Fossil CT Scans: Reducing Human Annotation Time through Deep Learning

Knutsen,

Konovalov

2024

Preprint

View full text Add to dashboard Cite

Recent developments in Deep Learning have opened the possibility for automated segmentation of large and highly detailed CT scan datasets of fossil material. However, previous methodologies have required large amounts of training data to reliably extract complex skeletal structures. Here we present a method for automated Deep Learning segmentation to obtain high-fidelity 3D models of fossils digitally extracted from the surrounding rock, training the model with less than 1%-2% of the total CT dataset. This workflow has the capacity to revolutionise the use of Deep Learning to significantly reduce the processing time of such data and boost the availability of segmented CT-scanned fossil material for future research outputs. Our final Unet segmentation model achieved a validation Dice similarity of 0.96.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Early Triassic Vertebrate Fossil CT Scans: Reducing Human Annotation Time through Deep Learning

Knutsen,

Konovalov

2024

Preprint

View full text Add to dashboard Cite

show abstract

Ostracod-based transfer function shifting to a broad prospect in palaeolimnology and palaeoclimate

Wang,

Zhao,

Zhou

et al. 2025

Science of The Total Environment

View full text Add to dashboard Cite

Opportunities and Challenges in Applying AI to Evolutionary Morphology

He,

Mulqueeney,

Watt

et al. 2024

Integrative Organismal Biology

View full text Add to dashboard Cite

Artificial intelligence (AI) is poised to revolutionise many aspects of science, including the study of evolutionary morphology. While classical AI methods such as principal component analysis and cluster analysis have been commonplace in the study of evolutionary morphology for decades, recent years have seen increasing application of deep learning to ecology and evolutionary biology. As digitised specimen databases become increasingly prevalent and openly available, AI is offering vast new potential to circumvent long standing barriers to rapid, big data analysis of phenotypes. Here, we review the current state of AI methods available for the study of evolutionary morphology, which are most developed in the area of data acquisition and processing. We introduce the main available AI techniques, categorising them into three stages based on their order of appearance: (i) machine learning, (ii) deep learning, and (iii) the most recent advancements in large-scale models and multimodal learning. Next, we present case studies of existing approaches using AI for evolutionary morphology, including image capture and segmentation, feature recognition, morphometrics, and phylogenetics. We then discuss the prospectus for near-term advances in specific areas of inquiry within this field, including the potential of new AI methods that have not yet been applied to the study of morphological evolution. In particular, we note key areas where AI remains underutilised and could be used to enhance studies of evolutionary morphology. This combination of current methods and potential developments have the capacity to transform the evolutionary analysis of the organismal phenotype into evolutionary phenomics, leading to an era of “Big Data'' that aligns the study of phenotypes with genomics and other areas of bioinformatics.

show abstract

Artificial intelligence in paleontology

Cited by 4 publications

References 145 publications

Automatic Segmentation of Early Triassic Vertebrate Fossil CT Scans: Reducing Human Annotation Time through Deep Learning

Automatic Segmentation of Early Triassic Vertebrate Fossil CT Scans: Reducing Human Annotation Time through Deep Learning

Ostracod-based transfer function shifting to a broad prospect in palaeolimnology and palaeoclimate

Opportunities and Challenges in Applying AI to Evolutionary Morphology

Contact Info

Product

Resources

About