Do machine learning methods solve the main pitfall of linear regression in dental age estimation?
Although Machine Learning methods demonstrate high levels of accuracy, they may be prone to trends in error distribution when estimating dental age. Evaluating this error is crucial and should be an integral part of model performance evaluation. Future research should aim to improve accuracy while rigorously addressing systematic biases.
Authors
- Roberto Cameriere (ORCID: https://orcid.org/0000-0002-3775-2244)
- Luigi Ferrante (ORCID: https://orcid.org/0000-0001-9444-972X)
- Edlira Skrami (ORCID: https://orcid.org/0000-0001-7940-6748)
- Nikolaos Angelakopoulos (ORCID: https://orcid.org/0000-0001-8511-4645)
- Andrea Faragalli (ORCID: https://orcid.org/0000-0003-4414-4789)
Institutions
- University of Bern (CH)
- Marche Polytechnic University (IT)
- University of Molise (IT)
Publication Details
- Journal
- Forensic Science International
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1016/j.forsciint.2024.112353
- Citations
- 9
- Primary Topic
- Forensic Anthropology and Bioarchaeology Studies
- Type
- article
- Field-Weighted Citation Impact
- 26.32