Evidence map›Paper›PMID 42365420›Full record

ArticleJournal of traditional Chinese medicine = Chung i tsa chih ying wen pan2026

Face acupoint recognition and localization based on geometric topological relationships.

Deng Yifan, Zhang Hongxing, H E Qiang

Abstract read
In one paragraph

Article in Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Deng YifanIntelligent Medical Engineering Research Center, School of Artificial Intelligence, Jianghan University, Wuhan 430056, China.
Zhang HongxingIntelligent Medical Engineering Research Center, School of Artificial Intelligence, Jianghan University, Wuhan 430056, China.
H E QiangIntelligent Medical Engineering Research Center, School of Artificial Intelligence, Jianghan University, Wuhan 430056, China.

Funding

Research on Intelligent Medical Diagnosis and Treatment under the Major Innovation Support Program of Jianghan University 2023ZDCX02
6 · The paper itself

Abstract

objectiveTo develop a method for accurate identification and localization of facial acupoints based on geometric and topological relationships (GTRs) among facial key points and organ features.

methodsA facial acupoint localization framework was constructed using the Google MediaPipe machine learning toolkit to extract 478 facial key points. Geometric and topological relationships between predefined acupoints and key facial landmarks were established. Based on these relationships, a rule-based mapping algorithm was designed to identify and localize facial acupoints. The method was applied to facial images collected from individuals undergoing acupuncture and physical therapy, and its localization performance was evaluated.

resultsThe proposed method successfully identified and localized facial acupoints based on stable geometric and topological relationships. The approach demonstrated consistent performance across different facial structures, enabling accurate positioning of acupoints without the need for large-scale annotated datasets. The results indicate that the method is feasible and reliable for practical applications.

conclusionThe GTR-based approach provides an effective and efficient solution for facial acupoint identification and localization, reducing dependence on manual annotation and improving applicability in clinical and intelligent acupuncture systems.

Indexed as

Acupuncture PointsFaceAcupuncture TherapyAlgorithmsHumansImage Processing, Computer-Assistedacupoint recognitiondeep learninggeometric topological relationshipskey point extractionMediaPipe

Identifiers

PMID42365420
PMCPMC13266317

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.