ArticleFrontiers in neurorobotics2024
YOLOv8-ACU: improved YOLOv8-pose for facial acupoint detection.
Article in Frontiers in neurorobotics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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.
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.
Who cites it
15 citing papers in PubMed, 35 citations in OpenAlex.
- Sparse Image Registration-Based Marker-Free Hand Acupoint Localization Using TCM Template Queries.Sensors (Basel, Switzerland) · 2026Article
- A Two-Stage Cascaded Regression Framework for Automatic Facial Acupoint Localization in Infrared Thermal Images.Sensors (Basel, Switzerland) · 2026Article
- Gonial Angle Estimation on Dental Panoramic X-Rays Using Deep Learning: A Transfer Learning Approach for Keypoint Detection.Journal of imaging informatics in medicine · 2026Article
- Real-time face keypoint detection for pre-anesthetic assessment with optimized YOLO11 model based on DeBiFormer.Frontiers in medical technology · 2026Article
- Artificial intelligence guided acupuncture decision making and treatment: a review of research.Frontiers in medicine · 2026Review
- Automatic measurement of Caton index on knee X-ray images using a key point detection model.European journal of radiology open · 2025Article
- A systematic review of computer-aided acupoint localization.iScience · 2025Article
- Evaluating Feature-Based Homography Pipelines for Dual-Camera Registration in Acupoint Annotation.Journal of imaging · 2025Article
- A review of acupoint localization based on deep learning.Chinese medicine · 2025Review
- Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images.Scientific reports · 2025Article
- Structure-guided deep learning for back acupoint localization via bone-measuring constraints.Frontiers in physiology · 2025Article
- A novel intelligent physiotherapy robot based on dynamic acupoint recognition method.Frontiers in neurorobotics · 2025Article
- Optimized deep learning model for diagnosing tonsil and adenoid hypertrophy through X-rays.Frontiers in oncology · 2025Article
- Monitoring Cattle Ruminating Behavior Based on an Improved Keypoint Detection Model.Animals : an open access journal from MDPI · 2024Article
- Real-time location of acupuncture points based on anatomical landmarks and pose estimation models.Frontiers in neurorobotics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Acupoint localization is integral to Traditional Chinese Medicine (TCM) acupuncture diagnosis and treatment. Employing intelligent detection models for recognizing facial acupoints can substantially enhance localization accuracy. Methods: This study introduces an advancement in the YOLOv8-pose keypoint detection algorithm, tailored for facial acupoints, and named YOLOv8-ACU. This model enhances acupoint feature extraction by integrating ECA attention, replaces the original neck module with a lighter Slim-neck module, and improves the loss function for GIoU. Results: The YOLOv8-ACU model achieves impressive accuracy, with an mAP@0.5 of 97.5% and an mAP@0.5-0.95 of 76.9% on our self-constructed datasets. It also marks a reduction in model parameters by 0.44M, model size by 0.82 MB, and GFLOPs by 9.3%. Discussion: With its enhanced recognition accuracy and efficiency, along with good generalization ability, YOLOv8-ACU provides significant reference value for facial acupoint localization and detection. This is particularly beneficial for Chinese medicine practitioners engaged in facial acupoint research and intelligent detection.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.