Evidence map›Paper›PMID 32782987›Full record

ArticleInternational journal of bioprinting2020

A Perspective on Using Machine Learning in 3D Bioprinting.

Chunling Yu, Jingchao Jiang

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of bioprinting, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 52 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 1 pooled it
–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

52 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Organ-on-a-chip toxicology.Innovation (Cambridge (Mass.)) · 2026
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  7. Bioprinting for craniofacial reconstruction: A review of advancements, clinical use, and challenges.Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery · 2025
    Review
  8. Article
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  14. Branding a New Technological Outlook for Future Orthopaedics.Bioengineering (Basel, Switzerland) · 2025
    Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Chunling YuFaculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China.
Jingchao JiangDepartment of Mechanical Engineering, University of Auckland, Auckland 1010, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, three-dimensional (3D) printing technologies have been widely applied in industry and our daily lives. The term 3D bioprinting has been coined to describe 3D printing at the biomedical level. Machine learning is currently becoming increasingly active and has been used to improve 3D printing processes, such as process optimization, dimensional accuracy analysis, manufacturing defect detection, and material property prediction. However, few studies have been found to use machine learning in 3D bioprinting processes. In this paper, related machine learning methods used in 3D printing are briefly reviewed and a perspective on how machine learning can also benefit 3D bioprinting is discussed. We believe that machine learning can significantly affect the future development of 3D bioprinting and hope this paper can inspire some ideas on how machine learning can be used to improve 3D bioprinting.

Indexed as

3D printingBioprintingMachine learning

Identifiers

PMID32782987
PMCPMC7415853

What OpenQuestion 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 OpenQuestion graph.