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AI accurate alternative to manual techniques for facial anthropometric measurements in prosthodontics: Study
![AI accurate alternative to manual techniques for facial anthropometric measurements in prosthodontics: Study AI accurate alternative to manual techniques for facial anthropometric measurements in prosthodontics: Study](https://medicaldialogues.in/h-upload/2024/06/12/750x450_240907-sample-images-with-24-anthropometric-distances-measured-for-a-male-tribe-b-male.webp)
Artificial Intelligence accurate alternative to manual techniques for facial anthropometric measurements in prosthodontics suggests a study published in the Journal of Prosthetic Dentistry.
Information regarding facial landmark measurement using machine learning (ML) techniques in prosthodontics is lacking. The objective of this study was to evaluate and compare the reliability, validity, and accuracy of facial anthropological measurements using both manual and ML landmark detection techniques. Two-dimensional (2D) frontal full-face photographs of 50 men and 50 women were made. The interpupillary width (IPW), interlateral canthus width (LCW), intermedial canthus width (MCW), interalar width (IAW), and intercommissural width (ICW) were measured on 2D digital images using manual and ML methods. The automated measurements were recorded using a programming language (Python), and a convolutional neural network (CNN) model was trained to detect human facial landmarks.
The obtained data from the manual and ML methods were analyzed using intraclass correlation coefficients (ICCs), the paired sample t test, Bland-Altman plots, and the Pearson correlation analysis (α=.05). Results: Intrarater and interrater reliability values were greater than 0.90, indicating excellent reliability. The mean difference between the manual and ML measurements of IPW, MCW, IAW, and ICW was 0.02 mm, while it was 0.01 mm for LCW. No statistically significant differences were found between the measurements obtained by the manual and ML methods (P>.05). Highly significant positive correlations (P<.001) were obtained between the results of the manual and ML methods: (r=0.996[IPW], r=0.977[LCW], r=0.944[MCW], r=0.965[IAW], and r=0.997[ICW]). In the field of prosthodontics, the use of ML methods provides a reliable alternative to manual digital techniques for carrying out facial anthropometric measurements.
Reference:
Koseoglu M, Ramachandran RA, Ozdemir H, Ariani MD, Bayindir F, Sukotjo C. Automated facial landmark measurement using machine learning: A feasibility study. J Prosthet Dent. 2024 Apr 25:S0022-3913(24)00282-8. doi: 10.1016/j.prosdent.2024.04.007. Epub ahead of print. PMID: 38670909.
Dr. Shravani Dali has completed her BDS from Pravara institute of medical sciences, loni. Following which she extensively worked in the healthcare sector for 2+ years. She has been actively involved in writing blogs in field of health and wellness. Currently she is pursuing her Masters of public health-health administration from Tata institute of social sciences. She can be contacted at editorial@medicaldialogues.in.
Dr Kamal Kant Kohli-MBBS, DTCD- a chest specialist with more than 30 years of practice and a flair for writing clinical articles, Dr Kamal Kant Kohli joined Medical Dialogues as a Chief Editor of Medical News. Besides writing articles, as an editor, he proofreads and verifies all the medical content published on Medical Dialogues including those coming from journals, studies,medical conferences,guidelines etc. Email: editorial@medicaldialogues.in. Contact no. 011-43720751