Evidence map›Paper›PMID 41968213›Full record

ArticleAnnals of biomedical engineering2026

Quantitative Assessment of Third Molar Extraction Difficulty and Nerve Injury Risk Using Artificial Intelligence and Image Processing.

Jihyeong Ko, Sasi Sooksatra, Seungmin Kim, Shaohua Tang, Jung-Eun Ha, Dong-Hun Han, Kyu Hwan Lee, Young-Jin Jung, Min-Ji Kim

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Article in Annals of biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Jihyeong Ko *Department of Biomedical Engineering, Chonnam National University, Yeosu, 59626, Korea.ORCID http://orcid.org/0000-0003-3292-418X
Sasi Sooksatra *Department of Biomedical Engineering, Chonnam National University, Yeosu, 59626, Korea.ORCID http://orcid.org/0009-0007-8364-7617
Seungmin KimDepartment of Biomedical Engineering, Chonnam National University, Yeosu, 59626, Korea.
Shaohua TangDepartment of Biomedical Engineering, Chonnam National University, Yeosu, 59626, Korea.
Jung-Eun HaDepartment of Dental Hygiene, Baekseok University, Cheonan, 31065, Korea.
Dong-Hun HanDepartment of Preventive & Social Dentistry, School of Dentistry, Seoul National University, Seoul, 08826, Korea.
Kyu Hwan LeeDepartment of Health Promotion Center Preventive dental-clinic, Seoul National University Bundang Hospital, Seongnam, 13620, Korea.
Young-Jin JungDepartment of Biomedical Engineering, Chonnam National University, Yeosu, 59626, Korea. yj@jnu.ac.kr.ORCID http://orcid.org/0000-0003-0169-2865
Min-Ji KimDepartment of Dental Hygiene, College of Bio-Health Convergence, Dongseo University, Busan, 47011, Korea. icealswl@dongseo.ac.kr.ORCID http://orcid.org/0000-0001-6389-0272

Funding

National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) 2022R1F1A1075155National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) RS-2023-00246022Regional Innovation System & Education (RISE) of the Gwangju RISE Center, funded by the Ministry of Education (MOE) and the Gwangju Metropolitan Government, Republic of Korea 2025-RISE-05-011
6 · The paper itself

Abstract

purposeTooth pattern and surrounding tissue can affect the complexity of third molar (3M) extraction surgery. This study aimed to develop a new, more precise 3M extraction difficulty assessment scoring system by applying image computational techniques and artificial intelligence (AI)-based technologies to panoramic radiography images.

methodsWe used dental panoramic X-ray data of patients aged 16 to 86 years. The mandibular canal (MC), inner area of alveolar bone (IAAB), and 3M were detected and segmented using an AI-based deep learning model. We developed algorithms to evaluate the difficulty of 3M extraction and the risk of inferior alveolar nerve damage. The inclination of the 3M (1-4 points), impaction depth of the 3M (1-4 points), and proximity between the mandibular third molar (M3M) and MC (0-4) were classified and scored. The scores obtained from each algorithm were summed to classify the difficulty index as very easy (2-4), easy (5-6), slightly difficult (7-9), and very difficult (10-12).

resultsThe average precisions of the maxillary 3M and M3M detection models were 0.93 and 0.97, respectively. Moreover, the average accuracies of the 3M inclination classification algorithm, 3M impaction depth calculation algorithm, and the proximity of M3M and MC algorithms were 0.8544, 0.9515, and 0.8991, respectively.

conclusionThe developed extraction difficulty scoring model can aid dentists in assessing the risk of 3M extraction and establish an appropriate surgical plan. Future studies should include more diverse patient data and improve the performance of the AI models to realize clinical implementation.

Indexed as

Artificial intelligenceDental panoramic radiographyImage processingInferior alveolar nerve injuryQuantitative assessmentThird molar extraction

Identifiers

PMID41968213

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.