IGMNN: a diagnosis method for vertical root fractures based on an information gated memory neural network.

Jie Wang, Xinyan Jin, Ruimin Tang, Zhonglin Zeng, Zitong Lin, Yikun Li, Ying Chen

Abstract read

In one paragraph

Article in Scientific reports, 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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5 · Who and what money

Authors and funding

7 authors.

Jie WangSchool of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, China.

Xinyan JinSchool of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, China.

Ruimin TangDepartment of Dentomaxillofacial Radiology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China.

Zhonglin ZengSchool of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, China.

Zitong LinDepartment of Dentomaxillofacial Radiology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China.

Yikun LiDepartment of Radiation Oncology, Nanjing Jinling Hospital, Nanjing University, Nanjing, Jiangsu, China.

Ying ChenSchool of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, China. yingchen@nju.edu.cn.

Funding

Nanjing University Integrated Research Platform of the Ministry of Education - Top Talents Program 0210-14380255

6 · The paper itself

Abstract

In medical image analysis, acquiring large-scale labeled datasets remains challenging, and images often exhibit high overall similarity that requires expert-level interpretation, differing substantially from natural image processing. To address these issues, we introduce the Information-Gated Memory (IGM) unit, a memory mechanism that enables deep networks to store and compare category-specific information. Unlike traditional CNNs or RNNs, the IGM unit performs memory-guided contrastive matching, allowing the network to focus on diagnostically relevant features and enhance classification performance. Using a CBCT dataset of 392 individuals, divided according to the presence or absence of artifacts, the proposed IGMNN achieved classification accuracies of [Formula: see text] and [Formula: see text], respectively.

Indexed as

Image Processing, Computer-AssistedNeural Networks, ComputerAlgorithmsCone-Beam Computed TomographyHumansCategory memoryGating unitInformation similarityNeural NetworksVertical root fractures

Identifiers

PMID41723186

DOI10.1038/s41598-026-39857-x

PMCPMC13021917

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

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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.