Evidence map›Paper›PMID 38402535›Full record

ArticleInternational journal of computer assisted radiology and surgery2024

Automated personalization of biomechanical knee model.

Alexandra Yurova, Alexey Lychagin, Eugene Kalinsky, Yuri Vassilevski, Mikhail Elizarov, Andrey Garkavi

Abstract read
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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2024. 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
0.6field-weighted citation impact, top 40% of its field
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, 3 citations in OpenAlex.

  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 at 2 institutions in 1 country.

Alexandra YurovaMarchuk Institute of Numerical Mathematics, Russian Academy of Sciences, 8 Gubkin Str., Moscow, 119333, Russia. alexandra.yurova@gmail.com.ORCID http://orcid.org/0000-0002-4288-9069
Alexey LychaginSechenov University, 8-2 Trubetskaya str., Moscow, 119991, Russia.
Eugene KalinskySechenov University, 8-2 Trubetskaya str., Moscow, 119991, Russia.
Yuri VassilevskiMarchuk Institute of Numerical Mathematics, Russian Academy of Sciences, 8 Gubkin Str., Moscow, 119333, Russia.
Mikhail ElizarovSechenov University, 8-2 Trubetskaya str., Moscow, 119991, Russia.
Andrey GarkaviSechenov University, 8-2 Trubetskaya str., Moscow, 119991, Russia.
Sechenov University · RURussian Academy of Sciences · RU

Funding

Russian Science Foundation 21-71-30023
6 · The paper itself

Abstract

purposePatient-specific biomechanical models of the knee joint can effectively aid in understanding the reasons for pathologies and improve diagnostic methods and treatment procedures. For deeper research of knee diseases, the development of biomechanical models with appropriate configurations is essential. In this study, we mainly focus on the development of a personalized biomechanical model for the investigation of knee joint pathologies related to patellar motion using automated methods.

methodsThis study presents a biomechanical model created for patellar motion pathologies research and some techniques for automating the generation of the biomechanical model. To generate geometric models of bones, the U-Net neural network was adapted for 3D input datasets. The method uses the same neural network for segmentation of femur, tibia, patella and fibula. The total size of the train/validation (75/25%) dataset is 18,183 3D volumes of size

resultsThe neural network for bones segmentation achieves mean DICE 0.9838. A biomechanical model for realistic simulation of patellar movement within the trochlear groove was proposed. Generation of personalized biomechanical models was automated.

conclusionsIn this paper, we have implemented a neural network for the segmentation of 3D CT scans of the knee joint to produce a biomechanical model for the study of knee cap motion pathologies. Most stages of the generation process have been automated and can be used to generate patient-specific models.

Indexed as

Imaging, Three-DimensionalKnee JointNeural Networks, ComputerBiomechanical PhenomenaHumansModels, AnatomicModels, BiologicalRange of Motion, ArticularBiomechanical knee modelKnee CT segmentationKnee jointLateral patellar compression syndromeMachine learningPatellofemoral jointU-Net

Identifiers

PMID38402535
OpenAlexW4392144100

What Socratic 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 Socratic graph.