Evidence map›Paper›PMID 39738835›Full record

ArticleMedical & biological engineering & computing2025

A fast-modeling framework for personalized human body models based on a single image.

Qiuqi Yuan, Zhi Xiao, Xiaoming Zhu, Bin Li, Jingzhou Hu, Yunfei Niu, Shiwei Xu

Abstract read
PubMed Publisher
In one paragraph

Article in Medical & biological engineering & computing, 2025. 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

7 authors.

Qiuqi YuanSchool of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, People's Republic of China.
Zhi XiaoSchool of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, People's Republic of China.
Xiaoming ZhuShanghai Motor Vehicle Inspection Certification &, Tech Innovation Center Co., Ltd, Shanghai, 201805, People's Republic of China.
Bin LiSchool of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, People's Republic of China.
Jingzhou HuDepartment of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200023, People's Republic of China.
Yunfei NiuChanghai Hospital, Shanghai, 200433, People's Republic of China.
Shiwei XuSchool of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, People's Republic of China. xushiwei@hnu.edu.cn.

Funding

Major Basic Research Project of the Natural Science Foundation of the Jiangsu Higher Education Institutions 2021JC0005National Natural Science Foundation of China U22A20187Science and Technology Innovation 2025 Major Project of Ningbo 2020Z033Science and Technology Innovation Leading Talent of Hunan Province Project 2020RC4013
6 · The paper itself

Abstract

Finite element human body models (HBMs) are the primary method for predicting human biological responses in vehicle collisions, especially personalized HBMs that allow accounting for diverse populations. Yet, creating personalized HBMs from a single image is a challenging task. This study addresses this challenge by providing a framework for HBM personalization, starting from a single image used to estimate the subject's skin point cloud, the skeletal point cloud, and the relative positions of the skeletons. Personalized HBMs were created by morphing the baseline HBM accounting skin and skeleton point clouds using a point cloud registration-based mesh morphing method. Using this framework, eight personalized HBMs with various biological characteristics (e.g., sex, height, and weight) were created, with comparable element quality to the baseline HBM. The mean geometric errors of the personalized FEMs generated by the framework are less than 7 mm, which was found to be acceptable based on biomechanical response evaluations conducted in this study.

Indexed as

Models, BiologicalAdultBiomechanical PhenomenaFemaleFinite Element AnalysisHumansImage Processing, Computer-AssistedMalePrecision MedicineBiological characteristicsFinite element modelHuman injuryMesh morphingPersonalized model

Identifiers

PMID39738835

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.