Automated Landmark Detection and Lip Thickness Classification Using a Convolutional Neural Network in Lateral Cephalometric Radiographs.

Miaomiao Han, Zhengqun Huo, Jiangyan Ren, Haiting Zhu, Huang Li, Jialing Li, Li Mei

Abstract read

In one paragraph

Article in Diagnostics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it

0cells of the map it votes in

3citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

Clinical Equivalence of a CNN-Based Automated Soft Tissue Landmark Detection System on 2D Facial Images.Diagnostics (Basel, Switzerland) · 2026
Article
Correlation Between Lip Prominence and Orthodontic Incisor Repositioning Within an Aesthetic Triangle Framework.Medicina (Kaunas, Lithuania) · 2026
Article
Reproducibility of AI in Cephalometric Landmark Detection: A Preliminary Study.Diagnostics (Basel, Switzerland) · 2025
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Miaomiao HanDepartment of Orthodontics, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing 210008, China.

Zhengqun HuoSchool of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.

Jiangyan RenDepartment of Orthodontics, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing 210008, China.

Haiting ZhuSchool of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.ORCID 0000-0002-5840-2251

Huang LiDepartment of Orthodontics, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing 210008, China.

Jialing LiDepartment of Orthodontics, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing 210008, China.

Li MeiDiscipline of Orthodontics, Department of Oral Science, Faculty of Dentistry, University of Otago, Dunedin 9016, New Zealand.ORCID 0000-0001-7856-8091

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

artificial intelligenceautomated landmark localizationdeep learninglip thickness

Identifiers

PMID40564789

DOI10.3390/diagnostics15121468

PMCPMC12192426

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Registered trials

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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.