Evidence map›Paper›PMID 42799962›Full record

ArticleClinical oral investigations2026

The application of deep learning in intelligent assessment of horizontal bone loss in mandibular first molars.

Yuxuan Yang, Enwei Jin, Zirui Bian, Yifan Chen, Xiaoyu Chen, Yanmin Wu, Jingyi Tan

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Article in Clinical oral investigations, 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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1 · What the graph read from it

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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

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yuxuan YangDepartment of Periodontology, Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88, Jiefang Road, Hangzhou, Zhejiang Province, Hangzhou, China.
Enwei JinDepartment of Periodontology, Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88, Jiefang Road, Hangzhou, Zhejiang Province, Hangzhou, China.
Zirui BianDepartment of Periodontology, Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88, Jiefang Road, Hangzhou, Zhejiang Province, Hangzhou, China.
Yifan ChenDepartment of Periodontology, Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88, Jiefang Road, Hangzhou, Zhejiang Province, Hangzhou, China.
Xiaoyu ChenDepartment of Periodontology, Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88, Jiefang Road, Hangzhou, Zhejiang Province, Hangzhou, China.
Yanmin WuDepartment of Periodontology, Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88, Jiefang Road, Hangzhou, Zhejiang Province, Hangzhou, China. wuyanmin@zju.edu.cn.
Jingyi TanDepartment of Periodontology, Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88, Jiefang Road, Hangzhou, Zhejiang Province, Hangzhou, China. tanjingyi@zju.edu.cn.

Funding

Leading Goose Project of Zhejiang Provincial Department of Science and Technology 2025C02103Natural Science Foundation of Zhejiang Province LY24H140003
6 · The paper itself

Abstract

objectiveThis study aims to develop an assessment tool that utilizes deep learning (DL) techniques to automatically detect the extent of horizontal bone loss in mandibular first molars on cone-beam computed tomography (CBCT) images.

methodsA total of 505 CBCT images of patients with mandibular first molar horizontal bone loss were included in this study. Clinical experts annotated the horizontal bone loss areas, and a 3D-ResNets model was employed to classify the degree of horizontal bone loss. The diagnostic and classification performance of the model was then evaluated.

resultsThe 3D-ResNets model demonstrated accurate diagnosis and classification of healthy teeth, degree I/II horizontal bone loss teeth, and degree III horizontal bone loss teeth (Precision: 0.814 vs. 0.778 vs. 0.906; Recall: 0.921 vs. 0.737 vs. 0.829; F1-score: 0.864 vs. 0.757 vs. 0.866; Accuracy: 0.901 vs. 0.838 vs. 0.919). The model demonstrated reliable diagnostic performance, achieving an overall accuracy (OA) of 0.829, comparable to the dentist's OA of 0.820 (no significant difference).

conclusionThe 3D-ResNets model demonstrates high accuracy, sensitivity, and specificity in detecting the extent of horizontal bone loss in mandibular first molars on CBCT images, showing promising potential for clinical application. CLINICAL SIGNIFICANCE: The current study provides preliminary experimental support for the development of a clinical auxiliary diagnostic system to automatically detect the degree of horizontal bone loss in mandibular first molars.

Indexed as

Alveolar Bone LossCone-Beam Computed TomographyDeep LearningMandibleMolarFemaleHumansImaging, Three-DimensionalClassificationCone-beam computed tomographyDeep learningDiagnosisHorizontal bone loss

Identifiers

PMID42799962

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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.