Evidence map›Paper›PMID 40785001›Full record

ArticleJournal of medical ultrasonics (2001)2026

Decoding fetal motion in 4D ultrasound with DeepLabCut.

Eisuke Inubashiri, Youhei Kaishi, Tarou Miyake, Riyouta Yamaguchi, Taishi Hamaguchi, Mayumi Inubashiri, Hajime Ota, Yukio Watanabe, Keizou Deguchi, Katumaru Kuroki and 1 more

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of medical ultrasonics (2001), 2026. 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

11 authors.

Eisuke InubashiriSapporo Toho Hospital, Kita 17, Higashi 15, Higashi-ku, Sapporo, 065-0017, Hokkaido, Japan. krieger2897@gmail.com.ORCID http://orcid.org/0000-0003-4843-6420
Youhei KaishiDepartment of Obstetrics and Gynecology, National Defense Medical College, 3-2 Namiki, Tokorozawa-shi, Saitama, Japan.
Tarou MiyakeDepartment of Obstetrics and Gynecology, National Defense Medical College, 3-2 Namiki, Tokorozawa-shi, Saitama, Japan.
Riyouta YamaguchiDepartment of Obstetrics and Gynecology, Japan Self-Defense Forces Sapporo Hospital, Makomanai 17, Minamiku, Sapporo, Hokkaido, Japan.
Taishi HamaguchiDepartment of Obstetrics and Gynecology, Japan Self-Defense Forces Sapporo Hospital, Makomanai 17, Minamiku, Sapporo, Hokkaido, Japan.
Mayumi InubashiriDepartment of Obstetrics and Gynecology, Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan.
Hajime OtaSapporo Toho Hospital, Kita 17, Higashi 15, Higashi-ku, Sapporo, 065-0017, Hokkaido, Japan.
Yukio WatanabeSapporo Toho Hospital, Kita 17, Higashi 15, Higashi-ku, Sapporo, 065-0017, Hokkaido, Japan.
Keizou DeguchiSapporo Toho Hospital, Kita 17, Higashi 15, Higashi-ku, Sapporo, 065-0017, Hokkaido, Japan.
Katumaru KurokiSapporo Toho Hospital, Kita 17, Higashi 15, Higashi-ku, Sapporo, 065-0017, Hokkaido, Japan.
Nobuhiko MaedaSapporo Toho Hospital, Kita 17, Higashi 15, Higashi-ku, Sapporo, 065-0017, Hokkaido, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aimed to objectively and quantitatively analyze fetal motor behavior using DeepLabCut (DLC), a markerless posture estimation tool based on deep learning, applied to four-dimensional ultrasound (4DUS) data collected during the second trimester. We propose a novel clinical method for precise assessment of fetal neurodevelopment.

methodsFifty 4DUS video recordings of normal singleton fetuses aged 12 to 22 gestational weeks were analyzed. Eight fetal joints were manually labeled in 2% of each video to train a customized DLC model. The model's accuracy was evaluated using likelihood scores. Intra- and inter-rater reliability of manual labeling were assessed using intraclass correlation coefficients (ICC). Angular velocity time series derived from joint coordinates were analyzed to quantify fetal movement patterns and developmental coordination.

resultsManual labeling demonstrated excellent reproducibility (inter-rater ICC = 0.990, intra-rater ICC = 0.961). The trained DLC model achieved a mean likelihood score of 0.960, confirming high tracking accuracy. Kinematic analysis revealed developmental trends: localized rapid limb movements were common at 12-13 weeks; movements became more coordinated and systemic by 18-20 weeks, reflecting advancing neuromuscular maturation. Although a modest increase in tracking accuracy was observed with gestational age, this trend did not reach statistical significance (p < 0.001).

conclusionDLC enables precise quantitative analysis of fetal motor behavior from 4DUS recordings. This AI-driven approach offers a promising, noninvasive alternative to conventional qualitative assessments, providing detailed insights into early fetal neurodevelopmental trajectories and potential early screening for neurodevelopmental disorders.

Indexed as

Deep LearningFetal MovementUltrasonography, PrenatalBiomechanical PhenomenaFemaleGestational AgeHumansPregnancyPregnancy Trimester, SecondReproducibility of ResultsVideo RecordingArtificial intelligenceDeepLabCutFetal movementFetal neurodevelopmentFour-dimensional ultrasoundMarkerless pose estimation

Identifiers

PMID40785001

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.