Evidence map›Paper›PMID 37508736›Full record

ArticleChildren (Basel, Switzerland)2023

Mobile Device-Based Video Screening for Infant Head Lag: An Exploratory Study.

Hao-Wei Chung, Che-Kuei Chang, Tzu-Hsiu Huang, Li-Chiou Chen, Hsiu-Lin Chen, Shu-Ting Yang, Chien-Chih Chen, Kuochen Wang

Open access · goldAbstract read
In one paragraph

Article in Children (Basel, Switzerland), 2023. 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
0.6field-weighted citation impact, top 29% of its field
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, 1 citations in OpenAlex.

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

8 authors at 4 institutions in 1 country.

Hao-Wei ChungDepartment of Pediatrics, Kaohsiung Medical University Chung Ho Memorial Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan.
Che-Kuei ChangDepartment of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan.
Tzu-Hsiu HuangDepartment of Rehabilitation Medicine, Kaohsiung Medical University Hospital, Kaohsiung 807, Taiwan.ORCID 0009-0003-3103-2275
Li-Chiou ChenDepartment of Physical Therapy, Fooyin University, Kaohsiung 831, Taiwan.
Hsiu-Lin ChenDepartment of Pediatrics, Kaohsiung Medical University Chung Ho Memorial Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan.ORCID 0000-0002-0565-4631
Shu-Ting YangDepartment of Pediatrics, Kaohsiung Medical University Chung Ho Memorial Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan.ORCID 0000-0001-9934-6156
Chien-Chih ChenCenter for Fundamental Science, Kaohsiung Medical University, Kaohsiung 807, Taiwan.ORCID 0000-0002-6041-3689
Kuochen WangDepartment of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan.
Kaohsiung Medical University · TWNational Yang Ming Chiao Tung University · TWFooyin University · TWKaohsiung Medical University Chung-Ho Memorial Hospital · TW

Funding

Kaohsiung Medical University Chung-Ho Memorial Hospital KMUH SI11010Ministry of Science of Technology, Taiwan 110-2221-E-037-006
6 · The paper itself

Abstract

introductionVideo-based automatic motion analysis has been employed to identify infant motor development delays. To overcome the limitations of lab-recorded images and training datasets, this study aimed to develop an artificial intelligence (AI) model using videos taken by mobile phone to assess infants' motor skills.

methodsA total of 270 videos of 41 high-risk infants were taken by parents using a mobile device. Based on the Pull to Sit (PTS) levels from the Hammersmith Motor Evaluation, we set motor skills assessments. The videos included 84 level 0, 106 level 1, and 80 level 3 recordings. We used whole-body pose estimation and three-dimensional transformation with a fuzzy-based approach to develop an AI model. The model was trained with two types of vectors: whole-body skeleton and key points with domain knowledge.

resultsThe average accuracies of the whole-body skeleton and key point models for level 0 were 77.667% and 88.062%, respectively. The Area Under the ROC curve (AUC) of the whole-body skeleton and key point models for level 3 were 96.049% and 94.333% respectively.

conclusionsAn AI model with minimal environmental restrictions can provide a family-centered developmental delay screen and enable the remote monitoring of infants requiring intervention.

Indexed as

artificial intelligenceinfant head lagmotor development delayremote screensmartphone

Identifiers

PMID37508736
PMCPMC10378382
OpenAlexW4384827432

What OpenQuestion 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 OpenQuestion graph.