Title
Artificial neural network in orthodontic therapeutic extractions
Authors
M.S. Rohith1,*, Shashanka P Kumar2, Anusha G Hegde3, Ajith Geevee4, Pooja Mastammanavar5 & Apoorva Jha6
Affiliation
1Department of Orthodontics and Dentofacial Orthopaedics, Vydehi Institute of Dental Sciences and Research Institute, Bangalore, India; 2Department of Orthodontics and Dentofacial Orthopaedics, Vokkaligara Sangha Dental College and Hospital, Bengaluru, India; 3Department of Orthodontics and Dentofacial Orthopaedics, Sri Dharmasthala Manjunatheshwara Dental College and Hospital, A constituent unit of Sri Dharmasthala Manjunatheshwara University, Dharwad, India; 4Department of Orthodontics and Dentofacial Orthopaedics, Vinayaka Mission’s Sankarachariyar Dental College, A constituent college of Vinayaka Mission’s Research Foundation (Deemed to be University), Salem, India; 5Department of Orthodontics and Dentofacial Orthopaedics, AJ Institute of Dental Sciences, Mangalore, India; 6Department of Orthodontics and Dentofacial Orthopaedics, BGS Global Institute of Dental Sciences, Bangalore, India; *Corresponding author
M.S Rohith - E-mail: ortho_rohith970@vimsmail.com; Phone: +91 7619210666
Shashanka P Kumar - E-mail: drshashankpkumar@gmail.com; Phone: +91 9886644329
Anusha G Hegde - E-mail: anu011092@gmail.com; Phone: +91 9886681981
Ajith Geevee - E-mail: drajithorthodontist@gmail.com; Phone: +91 8098585233
Pooja Mastammanavar - E-mail: poojahm80@gmail.com; Phone: +91 8050353203
Apoorva Jha - E-mail: apoorva274@gmail.com; Phone: +91 9535205305
Article Type
Research Article
Date
Received April 1, 2026; Revised April 30, 2026; Accepted April 30, 2026, Published April 30, 2026
Abstract
An unsubstantiated decision could result in several problems in its course as the extraction of teeth is an irreversible process. Therefore, it is of interest to develop an artificial intelligence decision making model for the diagnosis of extractions using neural network machine learning. The sample included 455 patients wherein input data consisted of 12 cephalometric variables and two additional indexes obtained from patients' records which were manually traced and digitized using NemoCeph 2D version 10software. It was observed that the accuracy of the binary classifier model, i.e., the decision of whether to extract or not, was 92.38 % and that of the multi-classifier model, i.e., the decision of which tooth to extract was also 92.38 %. Thus, we show that the technique of predicting orthodontic extractions using an artificial neural network is a reliable and valuable method.
Keywords
Artificial intelligence; artificial neural network; tracings; extraction
Citation
Rohith et al. Bioinformation 22(4): 2646-2652 (2026)
Edited by
Neelam Goyal & Shruti Dabi
ISSN
0973-2063
Publisher
License
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
