Research graph
References from Validation of a commercial intraoral auto-segmentation algorithm against expert-annotated clinical data with comparison to a transparent algorithm. Local targets link to admitted publications; unresolved targets remain external evidence.
Convolutional neural network for automated tooth segmentation on intraoral scans
10.1186/s12903-024-04582-2 · 2024 · External reference
Automated monitoring of tooth wear progression using AI on intraoral scans
10.1016/j.jdent.2024.105323 · 2024 · External reference
Evaluation of the accuracy of automated tooth segmentation of intraoral scans using artificial intelligence-based software packages
10.1016/j.ajodo.2024.05.015 · 2024 · External reference
Two-Stage mesh deep learning for automated tooth segmentation and landmark localization on 3D intraoral scans
10.1109/tmi.2022.3180343 · 2022 · External reference
Systematic literature review of validation methods for AI systems
10.1016/j.jss.2021.111050 · 2021 · External reference
The algorithmic audit: working with vendors to validate radiology-AI algorithms—how we do it
10.1016/j.acra.2019.09.009 · 2020 · External reference
How explainable artificial intelligence can increase or decrease Clinicians’ trust in AI applications in health care: systematic review
10.2196/53207 · 2024 · External reference
Explainable artificial intelligence (XAI): what we know and what is left to attain trustworthy artificial intelligence
10.1016/j.inffus.2023.101805 · 2023 · External reference
Development and application of a white box approach to integration testing
10.1016/0164-1212(84)90030-x · 1984 · External reference
A critical analysis of the limitation of deep learning based 3D dental mesh segmentation methods in segmenting partial scans
2023 · External reference
A fine-grained orthodontics segmentation model for 3D intraoral scan data
10.1016/j.compbiomed.2023.107821 · 2024 · External reference
Analysis of STL files
10.1016/s0895-7177(03)90079-3 · 2003 · External reference
Informatics in radiology: what can you see in a single glance and how might this guide visual search in medical images?
10.1148/rg.331125023 · 2013 · External reference
A review of perceptual expertise in radiology-how it develops, how we can test it, and why humans still matter in the era of artificial intelligence
10.1016/j.acra.2019.08.018 · 2020 · External reference
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2026 · External reference
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2016 · External reference
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2019 · External reference
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2024 · External reference
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2015 · External reference
How to assess market readiness for an innovative solution: the case of heat recovery technologies for SMEs
10.3390/su8111152 · 2016 · External reference
Artificial intelligence in dentistry: a descriptive review
10.3390/bioengineering11121267 · 2024 · External reference
Redefining radiology: a review of artificial intelligence integration in medical imaging
10.3390/diagnostics13172760 · 2023 · External reference
Towards a guideline for evaluation metrics in medical image segmentation
10.1186/s13104-022-06096-y · 2022 · External reference
Manual data labeling, radiology, and artificial intelligence: it is a dirty job, but someone has to do it
10.1016/j.mri.2024.110280 · 2025 · External reference
Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers
10.1148/ryai.2020200029 · 2020 · External reference
Classification performance bias between training and test sets in a limited mammography dataset
10.1371/journal.pone.0282402 · 2024 · External reference
Comparative analysis of open-source against commercial AI-based segmentation models for online adaptive MR-guided radiotherapy
10.1016/j.zemedi.2025.04.008 · 2025 · External reference
3D RoI-aware U-net for accurate and efficient colorectal tumor segmentation
10.1109/tcyb.2020.2980145 · 2021 · External reference
