Evidence map›Paper›PMID 41055854›Full record

ArticleAnnals of biomedical engineering2025

mRUST Estimation of Tibial Fracture Healing After Intramedullary Nailing Using Deep Forest Model with a Genetically Optimized Wearable Sensor Layout.

Wenxuan Chen, Mingxia Gong, Fang Pu, Weiyan Ren, Jie Tan, Yubo Fan

Abstract read
PubMed Publisher
In one paragraph

Article in Annals of biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

6 authors.

Wenxuan Chen *Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Mingxia Gong *Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Fang PuKey Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Weiyan RenKey Laboratory of Biomechanics and Mechanobiology, School of Engineering Medicine. Beihang University, Ministry of Education, No. 37 Xueyuan Road, Haidian District, Beijing, 100191, China. renweiyan@buaa.edu.cn.ORCID http://orcid.org/0009-0005-0356-8050
Jie TanDepartment of Orthopaedic Trauma, Beijing Jishuitan Hospital, Capital Medical University, Xi Cheng District, Beijing, 100035, China. tanjie@bjmu.edu.cn.
Yubo FanKey Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.

Funding

Fundamental Research Funds for the Central Universities YWF-23-YGQB-042National Key Research and Development Program of China 2023YFC3603700Natural Science Foundation of Beijing Municipality L244083
6 · The paper itself

Abstract

purposeThe assessment of tibial shaft fracture healing using the mRUST score is limited by radiation exposure and subjective interpretation. This study aimed to develop a quantitative model to estimate mRUST scores using continuous plantar pressure data from a portable insole system and to identify an optimal, cost-effective sensor layout.

methods23 Patients with tibial shaft fractures treated with intramedullary nails were enrolled. Plantar pressure data and corresponding mRUST scores were collected across 103 follow-up visits. During each visit, data from 5 gait analysis segments were recorded, yielding a total of 515 gait analysis segments. A Deep Forest Regression (DFR) model was developed to estimate mRUST from continuous gait data. A Genetic Algorithm (GA) optimized the sensor layout using the model's coefficient of determination (R

resultsThe optimization process identified an optimal 6-sensor layout, which achieved a Mean Absolute Error of 0.641 and an R

conclusionA DFR model with a GA-optimized plantar pressure insole provides an accurate, objective assessment of patients following intramedullary nailing of tibial fractures. This portable, data-driven approach presents a viable alternative to traditional radiographic methods, offering potential for timely and convenient clinical monitoring.

Indexed as

Fracture Fixation, IntramedullaryFracture HealingModels, BiologicalTibial FracturesWearable Electronic DevicesAdultAgedFemaleGaitHumansMaleMiddle AgedDeep ForestGenetic AlgorithmmRUSTPlantar pressure sensorsTibial fracture healing

Identifiers

PMID41055854

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.