Evidence map›Paper›PMID 39897708›Full record

ArticleIEEE sensors journal2024

Adaptive Machine Learning Head Model Across Different Head Impact Types Using Unsupervised Domain Adaptation and Generative Adversarial Networks.

Xianghao Zhan, Jiawei Sun, Yuzhe Liu, Nicholas J Cecchi, Enora Le Flao, Olivier Gevaert, Michael M Zeineh, David B Camarillo

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PubMed Full text at PMC Publisher
In one paragraph

Article in IEEE sensors journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Xianghao ZhanDepartment of Bioengineering, Stanford University, CA, 94305, USA.
Jiawei SunDepartment of Bioengineering, Stanford University, CA, 94305, USA.
Yuzhe LiuSchool of Biological Science and Medical Engineering, BeiHang University, Beijing, 10019, China.
Nicholas J CecchiDepartment of Bioengineering, Stanford University, CA, 94305, USA.
Enora Le FlaoDepartment of Bioengineering, Stanford University, CA, 94305, USA.
Olivier GevaertDepartment of Biomedical Data Science, Stanford University, CA, 94305, USA.
Michael M ZeinehDepartment of Radiology, Stanford University, CA, 94305, USA.
David B CamarilloDepartment of Bioengineering, Stanford University, CA, 94305, USA.

Funding

Disseminating a validated mouthguard sensor to investigate the effect of head impacts on brain healthR24NS098518 · NINDS · STANFORD UNIVERSITY · PI CAMARILLO, DAVID, GRANT, GERALD ARTHUR · 2018 to 2021
$1.1M
NINDS NIH HHS R24 NS098518
6 · The paper itself

Abstract

Machine learning head models (MLHMs) are developed to estimate brain deformation from sensor-based kinematics for early detection of traumatic brain injury (TBI). However, the overfitting to simulated impacts and the decreasing accuracy caused by distributional shift of different head impact datasets hinders the broad clinical applications of current MLHMs. We propose a new MLHM configuration that integrates unsupervised domain adaptation with a deep neural network to predict whole-brain maximum principal strain (MPS) and MPS rate (MPSR). With 12,780 simulated head impacts, we performed unsupervised domain adaptation on target head impacts from 302 college football (CF) impacts and 457 mixed martial arts (MMA) impacts using domain regularized component analysis (DRCA) and cycle-GAN-based methods. The new model improved the MPS/MPSR estimation accuracy, with the DRCA method outperforming other domain adaptation methods in prediction accuracy: MPS mean absolute error (MAE): 0.017 (CF) and 0.020 (MMA); MPSR MAE: 4.09 s

Indexed as

domain regularized component analysiskinematics sensor informaticsstrain and strain rateTraumatic brain injuryunsupervised domain adaptation

Identifiers

PMID39897708
PMCPMC11781752

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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.