SmartPhone iOralForens App for Forensic Luminosity and Colour Identification of Tooth Surfaces Using WiFi Wireless Intra-Oral Cameras

This article introduces the SmartPhone iOralForens App, an image-analysis application developed to support comparative forensic assessment of tooth-surface luminosity and colour using images acquired from WiFi-enabled or manually transferred intra-oral cameras. The application is intended as an adjunctive tool for dentists, forensic odontologists, forensic scientists, and appropriately trained researchers examining corresponding ante-mortem (AM) and post-mortem (PM) dental images. The iOralForens workflow incorporates Origin, LuminosityID%, and ColourID% image-processing functions, generating processed red and green representations and corresponding quantitative measurements from selected dental-image regions. For the present methodological evaluation, a simulated dataset comprising 132 dental-image observations distributed across 11 groups was analysed to demonstrate the proposed comparative statistical framework. Microsoft Excel was used to calculate mean values, standard deviations (SD), and percentage discrepancies between corresponding AM and PM measurements. The overall mean AM and PM values were 76.2% and 74.5% for LuminosityID%, respectively, and 78.5% and 76.7% for ColourID%, respectively. The mean group-level discrepancy was 2.42% for LuminosityID% and 2.46% for ColourID%, with observed discrepancies ranging from 0.65–4.55% and 0.64–4.70%, respectively. Under the proposed application-specific framework, the overall results were classified as moderate numerical similarity. These findings demonstrate the intended use of iOralForens for structured quantitative comparison but do not constitute independent forensic validation, because the dataset was simulated. The application emphasizes standardized acquisition, preferably using the same intra-oral camera model and comparable imaging conditions, because camera-specific optical characteristics may affect quantitative measurements. The proposed 0%, approximately 1%, 1–5%, and >5% discrepancy categories are exploratory and should not be interpreted as validated forensic identification thresholds. Further independent studies using real and controlled forensic dental datasets are required to establish repeatability, reproducibility, diagnostic performance, and evidentiary validity. iOralForens is therefore proposed as an adjunctive forensic dental image-analysis tool, with final identification decisions remaining the responsibility of appropriately qualified forensic professionals.