ArticleComputational intelligence and neuroscience2021
An Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07461805 (Caracterización de Subgrupos de Personas Con Diabetes Tipo 1), which is not on this map. Cited by 34 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Caracterización de Subgrupos de Personas Con Diabetes Tipo 1: análisis de características clínicas y glucométricas Utilizando Una aproximación de Inteligencia Artificial
Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 96 citations in OpenAlex.
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- Olfactory Science and Technology in Prostate Cancer Diagnosis: From Invertebrate Models to Artificial Intelligence.Life (Basel, Switzerland) · 2026Review
- Neuropsychiatric symptoms in preclinical and clinically manifest dementia: clusters and their health determinants.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- A multicenter study on preoperative WHO/ISUP grading of clear cell renal cell carcinoma using triphasic contrast-enhanced CT-based habitat imaging.BMC medical imaging · 2026Article
- Construction of a classification model for dementia among Brazilian adults aged 50 and over.Frontiers in aging neuroscience · 2026Article
- The association between cerebral small vessel disease and unfavorable hematoma morphology in primary intracerebral hemorrhage.Annals of medicine · 2025Article
- Explainable artificial intelligence for prediction of refractory ulcerative colitis: analysis of a Japanese Nationwide Registry.Annals of medicine · 2025Article
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- Out-of-distribution generalization for segmentation of lymph node metastasis in breast cancer.Scientific reports · 2025Article
- A multi-biomarker approach to risk stratification and detection of early cardiac disease in systemic sclerosis.PloS one · 2025Observational
- Identification of severe acute pediatric asthma phenotypes using unsupervised machine learning.Pediatric pulmonology · 2024Article
- Modeling and prediction of set‑up errors in breast cancer image‑guided radiotherapy using the Gaussian mixture model.Oncology letters · 2024Article
- Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies.Diagnostics (Basel, Switzerland) · 2024Review
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer disease is one of the common types that cause men's prostate damage all over the world. Prostate-specific membrane antigen (PSMA) expressed by type-II is an extremely attractive style for imaging-based diagnosis of prostate cancer. Clinically, photodynamic therapy (PDT) is used as noninvasive therapy in treatment of several cancers and some other diseases. This paper aims to segment or cluster and analyze pixels of histological and near-infrared (NIR) prostate cancer images acquired by PSMA-targeting PDT low weight molecular agents. Such agents can provide image guidance to resection of the prostate tumors and permit for the subsequent PDT in order to remove remaining or noneradicable cancer cells. The color prostate image segmentation is accomplished using an optimized image segmentation approach. The optimized approach combines the k-means clustering algorithm with elbow method that can give better clustering of pixels through automatically determining the best number of clusters. Clusters' statistics and ratio results of pixels in the segmented images show the applicability of the proposed approach for giving the optimum number of clusters for prostate cancer analysis and diagnosis.
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Registered trials
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.