Evidence map›Paper›PMID 42992015›Full record

ReviewBioactive materials2027

Lifecycle tissue engineering: An AI-enabled multimodal sensing and digital twin roadmap.

Xing Liu, Yitao Zhou, Cuiyi Wang, Lewei Tu, Xiaoxuan Zhao, Jingjing Gu, Qiuhua Sun, Malcolm Xing, Yeqin Yang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Bioactive materials, 2027. 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

9 authors.

Xing LiuSchool of Nursing, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Yitao ZhouSchool of Nursing, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Cuiyi WangSchool of Nursing, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Lewei TuSchool of Nursing, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Xiaoxuan ZhaoSchool of Nursing, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Jingjing GuCollege of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Qiuhua SunSchool of Nursing, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Malcolm XingDepartment of Mechanical Engineering, University of Manitoba, Winnipeg, R3T 2N2, Canada.
Yeqin YangSchool of Nursing, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The clinical translation of tissue engineering (TE) is critically hampered by the absence of real-time, non-invasive monitoring throughout the entire lifecycle of tissue fabrication and repair. Current reliance on end-point assessments obscures dynamic biological events, making early detection of deviations and failures challenging. While multimodal sensing offers rich data streams, inherent issues such as long-term drift, noise accumulation, and difficulties in cross-modality comparability impede their transformation into reliable process metrics and actionable insights. This review introduces a novel, comprehensive four-layer framework consisting of Problem, Data, Model, and Application Evidence, to systematically integrate artificial intelligence (AI)-enabled multimodal sensing across the full TE lifecycle. The Problem Layer delineates critical biological uncertainties and monitoring requirements. The Data Layer surveys diverse

Indexed as

Artificial intelligenceClinical translationDigital twinMultimodal sensingTissue engineering

Identifiers

PMID42992015
PMCPMC13737428

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.