SPINNE: An app for human vertebral height estimation based on artificial neural networks
D Vilas-Boas1, S N Wasterlain2, J d'Oliveira Coelho3
1Research Centre for Anthropology and Health (CIAS), Department of Life Sciences, University of Coimbra, Calçada Martim Freitas, 3000-456, Coimbra, Portugal.
Forensic Science International
|March 22, 2019
Summary
This study developed artificial neural network (ANN) models to estimate vertebral height, enabling stature estimation even with missing or damaged vertebrae. A web app, SPINNE, makes these predictive models accessible for forensic anthropology applications.
Area of Science:
- Forensic Anthropology
- Bioinformatics
- Biometrics
Background:
- Vertebral preservation is crucial for anatomical stature estimation.
- Absent or poorly preserved vertebrae hinder accurate height determination in forensic contexts.
Purpose of the Study:
- To develop artificial neural network (ANN) models for estimating vertebral height from existing vertebrae.
- To create a web application (SPINNE) for accessible application of these ANN models.
- To facilitate the anatomical method for stature estimation in cases with incomplete skeletal remains.
Main Methods:
- ANN models were trained using vertebral height data (C2-S1) from 125 adult individuals (56 male, 69 female).
- Statistical analysis and model development were conducted using R.
- Intra- and inter-observer errors for vertebral height measurements were assessed (<0.45 mm).
Main Results:
- Significant ANN models were developed for estimating vertebral height in both sexes and pooled groups.
- Root Mean Square Error (RMSE) for predicted vs. observed vertebral height was generally <1.0 mm.
- R-squared values exceeded 0.6 for most vertebrae, with some variation at the vertebral column ends (C3, L4, L5).
Conclusions:
- ANN models demonstrate significant potential for predicting vertebral height.
- The developed web app, SPINNE, provides a practical tool for applying these models.
- This approach enhances the applicability of the anatomical method for stature estimation with incomplete skeletal evidence.
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