Evidence map›Paper›PMID 41158630›Full record

ArticleFrontiers in endocrinology2025

Exploring unsupervised learning techniques for early detection of myocardial ischemia in type 2 diabetes.

Bing Liu, Yan-Jie Hou, Ping Wu, Xiao Han, Hao Qi, Xiu-Yun Yang, Zhi-Fang Wu, Si-Jin Li

Abstract read
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Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Bing LiuDepartment of Nuclear Medicine, First Hospital of Shanxi Medicinal University, Shanxi Medical University, Taiyuan, Shanxi, China.
Yan-Jie HouDepartment of Nuclear Medicine, First Hospital of Shanxi Medicinal University, Shanxi Medical University, Taiyuan, Shanxi, China.
Ping WuDepartment of Nuclear Medicine, First Hospital of Shanxi Medicinal University, Shanxi Medical University, Taiyuan, Shanxi, China.
Xiao HanDepartment of Nuclear Medicine, First Hospital of Shanxi Medicinal University, Shanxi Medical University, Taiyuan, Shanxi, China.
Hao QiDepartment of Endocrinology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Xiu-Yun YangModern Educational Technology Center, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Zhi-Fang WuDepartment of Nuclear Medicine, First Hospital of Shanxi Medicinal University, Shanxi Medical University, Taiyuan, Shanxi, China.
Si-Jin LiDepartment of Nuclear Medicine, First Hospital of Shanxi Medicinal University, Shanxi Medical University, Taiyuan, Shanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Myocardial ischemia can result in severe cardiovascular complications. However, the impact of clinical factors on myocardial ischemia in individuals with T2DM remains unclear. we applied a clustering approach to identify the variability in myocardial ischemia evaluated through Single-Photon Emission Computed Tomography. Methods: Retrospective statistics derived from 637 T2DM patients with myocardial ischemia who participated in SPECT imaging at our hospital between January 2022 and September 2024 were gathered. Ischemia areas, cavity size, wall motion,ventricular contraction, cardiac systolic coordination, End-diastolic Volume, End-systolic Volume; Left ventricular injection fraction were assessed and analyzed. Clustering analysis of medical data in unsupervised learning, involving the elbow method and silhouette coefficient(cluster 1: 262; cluster 2: 375);. Results: The Healthcare information between two groups differed in multiple respects (1) Cluster 1 had the had the older patient(63.23 ± 12.31), longer average duration of diabetes(10.27 ± 8.77), higher Glycated Hemoglobin(HbA1c) values(7.69 ± 1.76), the higher level of serum creatinine (115.42 ± 106.18µmol/L);and a higher proportion of patients with insulin treatment(40.5%) (2).Cluster 1 had more males(68.8%),higher proportion of patients with smoking history(44.5%), the higher level of Cholesterol(3.96 ± 1.12mmol/L),serum uric acid (406.78 ± 135.24µmol/L),Low-density lipoprotein cholesterol(2.08 ± 0.32mmol/L),and was more prone to statin therapy (6.1%).The SPECT features differed across the various clusters (1):Cluster 1 had higher proportion of Hypokinesis(38.2%),poor ventricular contraction(57.6%),Impaired Cardiac systolic coordination(63.7%),and abnormal LVEF(81.3%) (2).Cluster 2 had a higher proportion of total ischemia(11.5%) and abnormal ESV(52.8%) (3).There was no significant difference in Ischemia areas, Cavity size, Involved segments, and EDV. Discussion: Although the unsupervised clustering approach revealed differences in various clinical and imaging characteristics, no significant differences were observed in ischemic burden, cavity size, involved segments, or EDV.

Indexed as

Diabetes Mellitus, Type 2Myocardial IschemiaUnsupervised Machine LearningAgedCluster AnalysisEarly DiagnosisFemaleHumansMaleMiddle AgedRetrospective StudiesTomography, Emission-Computed, Single-Photondiabetes mellituselbow methodmachine learningmyocardial ischemiasilhouette coefficientsingle-photon emission computed tomography

Identifiers

PMID41158630
PMCPMC12557336

What Socratic holds

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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 Socratic graph.