Evidence map›Paper›PMID 41003421›Full record

ArticleJournal of functional biomaterials2025

Data-Driven Design and Additive Manufacturing of Patient-Specific Lattice Titanium Scaffolds for Mandibular Bone Reconstruction.

Nail Beisekenov, Bagdat Azamatov, Marzhan Sadenova, Dmitriy Dogadkin, Daniyar Kaliyev, Sergey Rudenko, Boris Syrnev

Abstract read
In one paragraph

Article in Journal of functional biomaterials, 2025. 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. Review
  2. Advances in Functional Scaffolds for Bone and Joint Surgery.Journal of functional biomaterials · 2025
    Article
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

7 authors.

Nail BeisekenovDepartment of Ecology and Conservation Biology, Texas A&M University, College Station, TX 77843, USA.
Bagdat AzamatovSmart Engineering Competence Centre, D. Serikbayev East Kazakhstan Technical University, 19 Serikbayev Street, Ust-Kamenogorsk 070010, Kazakhstan.
Marzhan SadenovaCenter of Excellence "VERITAS", D. Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk 070004, Kazakhstan.ORCID 0000-0002-2870-6668
Dmitriy DogadkinSmart Engineering Competence Centre, D. Serikbayev East Kazakhstan Technical University, 19 Serikbayev Street, Ust-Kamenogorsk 070010, Kazakhstan.ORCID 0000-0001-5589-1638
Daniyar KaliyevSmart Engineering Competence Centre, D. Serikbayev East Kazakhstan Technical University, 19 Serikbayev Street, Ust-Kamenogorsk 070010, Kazakhstan.
Sergey RudenkoSmart Engineering Competence Centre, D. Serikbayev East Kazakhstan Technical University, 19 Serikbayev Street, Ust-Kamenogorsk 070010, Kazakhstan.ORCID 0000-0002-2740-9143
Boris SyrnevCenter of Excellence "VERITAS", D. Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk 070004, Kazakhstan.

Funding

The Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan BR24992786
6 · The paper itself

Abstract

The reconstruction of segmental bone defects requires patient-specific scaffolds that combine mechanical safety, biological functionality, and rapid manufacturing. We converted CT-derived mandibular geometry into a functionally graded Ti-6Al-4V lattice and optimised porosity, screw layout, and strut thickness through a cyber-physical loop that joins high-fidelity FEM, millisecond ANN, and a BN for uncertainty quantification. Fifteen candidate scaffolds were fabricated by direct metal laser sintering and hot isostatic pressing and were mechanically tested. FEM predicted stress and stiffness with 98% accuracy; the ANN reproduced these outputs with 94% fidelity while evaluating 10,000 designs in real time, and the BN limited failure probability to <3% under worst-case loads. The selected 55-65% porosity design reduced titanium use by 15%, shortened development time by 25% and raised multi-objective optimisation efficiency by 20% relative to a solid-plate baseline, while resisting a 600 N bite with a peak von Mises stress of 225 MPa and micromotion < 150 µm. Integrating physics-based simulation, AI speed, and probabilistic rigour yields a validated, additively manufactured scaffold that meets surgical timelines and biomechanical requirements, offering a transferable blueprint for functional scaffolds in bone and joint surgery.

Indexed as

additive manufacturing (AM)artificial neural network (ANN)Bayesian network (BN)direct metal laser sintering (DMLS)finite-element modelling (FEM)lattice architecturematerial property predictionpatient-specific implant design

Identifiers

PMID41003421
PMCPMC12470917

What Socratic holds

Textmetadata
LicenceCC BY
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.