Evidence map›Paper›PMID 41116910›Full record

ArticleBiomedical engineering and computational biology2025

Clinical Evaluation of Novel Custom 3D-Printed Meshed-Silicone Orthotics Utilizing Standing Foot Scans and Dynamic Gait Data.

Joshua Kubach, Mario Pasurka, Julia Lueg, Marcel Betsch

Abstract read
In one paragraph

Article in Biomedical engineering and computational biology, 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

4 authors.

Joshua KubachDepartment of Trauma Surgery and Orthopaedics, Friedrich-Alexander-University Erlangen-Nuremberg, University Hospital Erlangen, Germany.ORCID https://orcid.org/0000-0002-9594-5263
Mario PasurkaDepartment of Trauma Surgery and Orthopaedics, Friedrich-Alexander-University Erlangen-Nuremberg, University Hospital Erlangen, Germany.ORCID https://orcid.org/0000-0001-5460-9792
Julia LuegDepartment of Trauma Surgery and Orthopaedics, Friedrich-Alexander-University Erlangen-Nuremberg, University Hospital Erlangen, Germany.
Marcel BetschDepartment of Trauma Surgery and Orthopaedics, Friedrich-Alexander-University Erlangen-Nuremberg, University Hospital Erlangen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Conventional orthotic insoles demonstrate limited accommodation for individual foot morphology and plantar pressure distribution patterns, resulting in biomechanical inefficiencies and patient discomfort. Computational approaches integrating artificial intelligence with additive manufacturing technologies offer promising solutions for personalized orthotic design. This study investigates the clinical efficacy of AI-driven 3D-printed meshed-silicone orthotics through comprehensive biomechanical assessment. Methods: A prospective cohort study (n = 21; 8 females, 13 males; age 25.6 ± 3.68 years; BMI 25.48 ± 3.46) evaluated custom orthotics fabricated using machine learning algorithms applied to individual foot and gait data. Pre- and post-intervention assessments included Visual Analog Scale (VAS), Foot Function Index (FFI), Foot Posture Index (FPI), plantar pressure distribution analysis, and 3-dimensional gait analysis over a 4-week period. Results: FFI scores showed minimal variation (pre-intervention: 13.48 ± 13.14; post-intervention: 14.10 ± 12.96). Significant biomechanical modifications were observed: multi-planar lower extremity alignment correction at hip, knee, and ankle joints. Plantar pressure redistribution demonstrated decreased heel loading with unchanged forefoot pressure distribution, accompanied by significant maximum metatarsal pressure elevation ( Conclusions: II. AI-integrated 3D-printed meshed-silicone orthotics demonstrated measurable biomechanical improvements including lower extremity alignment optimization and plantar pressure redistribution. These computational design methodologies combined with advanced manufacturing technologies establish a foundation for personalized orthotic interventions in clinical biomechanics applications.

Indexed as

3D printingbiomechanicscustomized insolesleg axis correctionorthotics

Identifiers

PMID41116910
PMCPMC12535632

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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.