Genetic algorithm-based optimization of columella shape with FEM surrogate modeling: convergence analysis and application to ossicular chain reconstruction.

Takumi Asakura, Koyo Ozono, Takuma Ichino, Motoki Hirabayashi, Sho Kurihara, Yuta Kurashina

Abstract read

In one paragraph

Article in Biomechanics and modeling in mechanobiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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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

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Takumi AsakuraDepartment of Mechanical and Aerospace Engineering, Faculty of Science and Engineering, Tokyo University of Science, Chiba, Japan. t_asakura@rs.tus.ac.jp.

Koyo OzonoDepartment of Mechanical and Aerospace Engineering, Faculty of Science and Engineering, Tokyo University of Science, Chiba, Japan.

Takuma IchinoDepartment of Mechanical and Aerospace Engineering, Faculty of Science and Engineering, Tokyo University of Science, Chiba, Japan.

Motoki HirabayashiDepartment of Otorhinolaryngology, The Jikei University School of Medicine, Tokyo, Japan.

Sho KuriharaDepartment of Otorhinolaryngology, The Jikei University School of Medicine, Tokyo, Japan.

Yuta KurashinaDivision of Advanced Mechanical Systems Engineering, Institute of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan.

Funding

AMED JP 22ym0126813JSPS KAKENHI JP23K22706

6 · The paper itself

Abstract

Ossicular chain reconstruction is a vital surgical approach for restoring hearing in patients with ossicular chain disruption. Despite advancements in prosthetic design, selecting optimal material and geometric configurations remains a challenge due to the complex, nonlinear dynamics of middle ear biomechanics. This study presents a hybrid optimization framework that integrates genetic algorithms (GA) with machine learning-trained finite element (FE) surrogate models to design middle ear columella prostheses with enhanced acoustic performance. Four tympanoplasty models (III

Indexed as

AlgorithmsEar OssiclesFinite Element AnalysisGenetic AlgorithmsPlastic Surgery ProceduresComputer SimulationHumansModels, BiologicalOssicular ProsthesisFinite element modelingGenetic algorithmHearing prosthesis optimizationOssicular chain reconstructionSurrogate modeling

Identifiers

PMID41910673

DOI10.1007/s10237-026-02044-y

PMCPMC13035726

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LicenceCC BY-NC-ND

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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.