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ml-based ct texture analysis software  (Canon inc)

 
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    Canon inc ml-based ct texture analysis software
    Ml Based Ct Texture Analysis Software, supplied by Canon inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/texture+analysis-based+software/ml+based+ct+texture+analysis+software/pm34636644-87-9-51
    Average 90 stars, based on 1 article reviews
    ml-based ct texture analysis software - by Bioz Stars, 2026-11
    90/100 stars

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    Related Articles

    Software:

    Article Title: Machine learning for lung texture analysis on thin-section CT: Capability for assessments of disease severity and therapeutic effect for connective tissue disease patients in comparison with expert panel evaluations.
    Article Snippet: Background: The need for quantitative assessment of interstitial lung involvement on thin-section computed tomography (CT) has arisen in interstitial lung diseases including connective tissue disease (CTD).. Purpose: To evaluate the capability of machine learning (ML)-based CT texture analysis for disease severity and treatment response assessments in comparison with qualitatively assessed thin-section CT for patients with CTD.. Material and Methods: A total of 149 patients with CTD-related ILD (CTD-ILD) underwent initial and follow-up CT scans (total 364 paired serial CTexaminations), pulmonary function tests, and serum KL-6 level tests.



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    Image Search Results


    Lung texture analysis report: The Dicom representative image of each category of pattern. These were chosen based on the predominance of lung parenchymal patterns including hyperlucent, ground glass [GG], reticular, and honeycombing. Imbio LTA provided their regional distribution within three different lung zones [upper, middle, lower] in each lung. The platform provided an image in which each lung pattern was colored differently, allowing the evaluation of disease extent, composition, and location at a glance. Specific images were chosen because of the predominance of one of the patterns in each of them; at the same time, they had other patterns, also showing that all pathologies could coexist in a single case of bronchiectasis, yet have a predominance of a single pattern that may have a bearing on their clinical symptoms.

    Journal: Diagnostics

    Article Title: Artificial Intelligence Unveils the Unseen: Mapping Novel Lung Patterns in Bronchiectasis via Texture Analysis

    doi: 10.3390/diagnostics14242883

    Figure Lengend Snippet: Lung texture analysis report: The Dicom representative image of each category of pattern. These were chosen based on the predominance of lung parenchymal patterns including hyperlucent, ground glass [GG], reticular, and honeycombing. Imbio LTA provided their regional distribution within three different lung zones [upper, middle, lower] in each lung. The platform provided an image in which each lung pattern was colored differently, allowing the evaluation of disease extent, composition, and location at a glance. Specific images were chosen because of the predominance of one of the patterns in each of them; at the same time, they had other patterns, also showing that all pathologies could coexist in a single case of bronchiectasis, yet have a predominance of a single pattern that may have a bearing on their clinical symptoms.

    Article Snippet: This study aimed to assess lung involvement in patients with bronchiectasis using the Bronchiectasis Radiologically Indexed CT Score (BRICS) and AI-based quantitative lung texture analysis software (IMBIO, Version 2.2.0).

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