Evidence map›Paper›PMID 40487158›Full record

ArticleMaterials today. Bio2025

Additive manufacturing of patient-specific, biphasic implants with zonal design for regeneration of osteochondral defects-critical evaluation of the work flow from clinical MRI data to implantation.

Max von Witzleben, Suihong Liu, Philipp Sembdner, Stefan Holtzhausen, Sophia Freya Ulrike Blum, Jörg Lützner, David Kilian, Ute Nimtschke, Michael Gelinsky, Anja Lode and 1 more

Abstract read
In one paragraph

Article in Materials today. Bio, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. 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

11 authors.

Max von WitzlebenCentre for Translational Bone, Joint and Soft Tissue Research, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
Suihong LiuCentre for Translational Bone, Joint and Soft Tissue Research, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
Philipp SembdnerInstitute of Machine Elements and Machine Design, Faculty of Mechanical Engineering, Technische Universität Dresden, Dresden, Germany.
Stefan HoltzhausenInstitute of Machine Elements and Machine Design, Faculty of Mechanical Engineering, Technische Universität Dresden, Dresden, Germany.
Sophia Freya Ulrike BlumInstitute and Polyclinic for Diagnostic and Interventional Radiology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
Jörg LütznerUniversity Center of Orthopaedics, Trauma and Plastic Surgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
David KilianDepartment of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
Ute NimtschkeInstitute of Anatomy, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
Michael GelinskyCentre for Translational Bone, Joint and Soft Tissue Research, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
Anja LodeCentre for Translational Bone, Joint and Soft Tissue Research, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
Henriette BretschneiderCentre for Translational Bone, Joint and Soft Tissue Research, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ideally, the combination of clinical imaging techniques with additive manufacturing processes enables the fabrication of patient-specific regenerative implants that precisely fit into the defect site, promoting native tissue restoration while gradually degrading. Osteochondral defects, affecting both cartilage and subchondral bone in joints are best visualized using magnetic resonance imaging (MRI). In this study, a workflow for computer-aided manufacturing of patient-specific osteochondral implants based on geometrical data obtained from MRI scans was evaluated in a clinically relevant setting. Artificial osteochondral defects were created in femoral condyles of human body donors and scanned with MRI. 'Computer-Aided Design' (CAD) models for bone and cartilage components served as basis for designing defect-specific trizonal implants consisting of (i) a bone, (ii) an interlocking, and (iii) a cartilage zone. These implants were fabricated using multi-channel 3D extrusion printing, using a calcium phosphate cement as a bone substitute and an alginate-based hydrogel as a cartilage substitute material - with both materials alternately printed in the interlocking zone. After fabrication, the constructs were implanted into the corresponding defects, and assessed for fit accuracy via clinical imaging. The entire process chain was successfully conducted under near-clinical conditions by an interdisciplinary team of engineers, radiologist and surgeons, during which critical points were identified. Due to the inherent resolution limitations of clinical MRI and extrusion-based 3D printing, inaccuracies in implant fitting occurred; strategies to address these challenges were identified by integrating design tolerances and applying minor intraoperative adjustments.

Indexed as

Additive manufacturing (AM)Calcium phosphate bone cementComputer-aided design (CAD)Extrusion-based 3D printingHydrogelMagnetic resonance imaging (MRI)Osteochondrosis dissecans

Identifiers

PMID40487158
PMCPMC12145557

What OpenQuestion holds

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Registered trials

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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.