Artificial neural network in orthodontic therapeutic extractions.

M S Rohith, Shashanka P Kumar, Anusha G Hegde, Ajith Geevee, Pooja Mastammanavar, Apoorva Jha

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Article in Bioinformation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

M S RohithDepartment of Orthodontics and Dentofacial Orthopaedics, Vydehi Institute of Dental Sciences and Research Institute, Bangalore, India.

Shashanka P KumarDepartment of Orthodontics and Dentofacial Orthopaedics, Vokkaligara Sangha Dental College and Hospital, Bengaluru, India.

Anusha G HegdeDepartment of Orthodontics and Dentofacial Orthopaedics, Sri Dharmasthala Manjunatheshwara Dental College and Hospital, A constituent unit of Sri Dharmasthala Manjunatheshwara University, Dharwad, India.

Ajith GeeveeDepartment 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.

Pooja MastammanavarDepartment of Orthodontics and Dentofacial Orthopaedics, AJ Institute of Dental Sciences, Mangalore, India.

Apoorva JhaDepartment of Orthodontics and Dentofacial Orthopaedics, BGS Global Institute of Dental Sciences, Bangalore, India.

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6 · The paper itself

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.

Indexed as

Artificial intelligenceartificial neural networkextractiontracings

Identifiers

PMID42282388

DOI10.6026/973206300222646

PMCPMC13252329

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