Evidence map›Paper›PMID 42135376›Full record

ArticleScientific reports2026

Machine learning-based analysis of the relationship between brain lesion sites and swallowing and cognitive functions in stroke patients.

Yusuke Hata, Seiji Hama, Akane Hamago, Shizuko Kiregawa, Yuki Sato, Ziqiang Xu, Akira Furui, Zu Soh, Harutoyo Hirano, Stephanie Sutoko and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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

16 authors.

Yusuke HataGraduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan.
Seiji HamaHibino Hospital, Hiroshima, Japan. shama@hiroshima-u.ac.jp.
Akane HamagoHibino Hospital, Hiroshima, Japan.
Shizuko KiregawaHibino Hospital, Hiroshima, Japan.
Yuki SatoDepartment of Otorhinolaryngology, Head and Neck Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Ziqiang XuGraduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan.
Akira FuruiGraduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan.
Zu SohGraduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan.
Harutoyo HiranoFujita Health University, School of Medical Sciences, Toyoake, Japan.
Stephanie SutokoHealthcare Innovation Center, Research and Development Group, Hitachi, Ltd., Tokyo, Japan.
Tomohiko MizuguchiNew Business Producing Division, Business Development Department, Maxell, Ltd., Tokyo, Japan.
Akihiko KandoriNext Research, Research and Development Group, Hitachi, Ltd., Tokyo, Japan.
Takao HamamotoDepartment of Otorhinolaryngology, Head and Neck Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Sachio TakenoDepartment of Otorhinolaryngology, Head and Neck Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Shigeto YamawakiCenter for Brain, Mind and KANSEI Sciences Research, Hiroshima University, Hiroshima, Japan.
Toshio TsujiGraduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan. tsuji-c@bsys.hiroshima-u.ac.jp.

Funding

Adaptable and Seamless Technology Transfer Program through Target-Driven R and D JPMJTR22R8Japan Society for the Promotion of Science 23K08964
6 · The paper itself

Abstract

Stroke-related dysphagia is influenced by brain damage location and cognitive impairment, but its mechanisms remain unclear. In this study, we aimed to clarify the mechanisms by which brain damage causes dysphagia and cognitive function in 246 patients with stroke using a probabilistic neural network model. Dysphagia was classified as mild (oral intake with liquid and diet modifications) or severe (unable to take food orally, requiring tube feeding). Atlas-based segmentation applied to brain MRI data delineated 121 anatomically-defined regions, including 116 Automated Anatomical Labeling regions (AAL116), brainstem level 1, and white matter level 4 of the Automated Talairach Atlas Labels (ATAL), and the total score of five cognitive items on the Functional Independence Measure was used to evaluate cognitive function. Classifying dysphagia by severity and evaluating cognitive function resulted in improvements in prediction accuracy and reduced the number of predictor variables. In addition, after adding evaluations of cognitive function in both the severe and mild dysphagia groups, evaluations of the brainstem, which had remained a final predictor variable in the analysis of brain regions only, no longer remained. The results highlight the importance of integrating neurological imaging and cognitive assessment in the diagnosis and rehabilitation of dysphagia after stroke.

Indexed as

BrainCognitionDeglutitionDeglutition DisordersMachine LearningStrokeAgedFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedBrain injury siteCognitive functionDysphagiaLog-linearized Gaussian mixture networkMachine learning analysisStroke

Identifiers

PMID42135376
PMCPMC13365378

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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.