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machine learning based user friendly software platform  (Biomark Inc)

 
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    Biomark Inc machine learning based user friendly software platform
    Machine Learning Based User Friendly Software Platform, supplied by Biomark Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/machine-learning+software/based+friendly+learning+machine+platform+software+user/pm41872897-411-19-0
    Average 86 stars, based on 1 article reviews
    machine learning based user friendly software platform - by Bioz Stars, 2026-09
    86/100 stars

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    Software:

    Article Title: Multicenter peritumoral radiomics in oncology: advances, challenges, and future directions.
    Article Snippet: .. Biomark Res 11:102 93 Zhou Z, Qian X, Hu J, Zhu J, Geng C, Dai Y (2021) A new machine learning based user-friendly software platform for automatic radiomics modeling and analysis. ..



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    Flowchart of study design. DM, diabetes mellitus; CCTA, coronary computed tomography angiography; CAD, coronary artery disease; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; MACE, major adverse cardiovascular events

    Journal: BMC Medical Imaging

    Article Title: Incremental prognostic value of pericoronary fat attenuation index in diabetic patients with non-obstructive coronary artery disease

    doi: 10.1186/s12880-025-02146-6

    Figure Lengend Snippet: Flowchart of study design. DM, diabetes mellitus; CCTA, coronary computed tomography angiography; CAD, coronary artery disease; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; MACE, major adverse cardiovascular events

    Article Snippet: CT-FFR analysis was performed using a machine learning-based CT-FFR software (version 3.5, Siemens Healthineers, Germany).

    Techniques: Computed Tomography, Derivative Assay

    Kaplan–Meier curves for cumulative MACE rates ( A , B , C ) and cumulative MACCE rates ( D , E , F ) for stratified groups based on HRP, CT-FFR, and pericoronary FAI. MACE, major adverse cardiovascular events; MACCE, major adverse cardiovascular and cerebrovascular events; HRP, high-risk plaque; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; HU, Hounsfield units

    Journal: BMC Medical Imaging

    Article Title: Incremental prognostic value of pericoronary fat attenuation index in diabetic patients with non-obstructive coronary artery disease

    doi: 10.1186/s12880-025-02146-6

    Figure Lengend Snippet: Kaplan–Meier curves for cumulative MACE rates ( A , B , C ) and cumulative MACCE rates ( D , E , F ) for stratified groups based on HRP, CT-FFR, and pericoronary FAI. MACE, major adverse cardiovascular events; MACCE, major adverse cardiovascular and cerebrovascular events; HRP, high-risk plaque; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; HU, Hounsfield units

    Article Snippet: CT-FFR analysis was performed using a machine learning-based CT-FFR software (version 3.5, Siemens Healthineers, Germany).

    Techniques: Derivative Assay

    ROC curves of all models in predicting MACE ( A ) and MACCE ( B ). Model 1: HRP; Model 2: CT-FFR; Model 3: pericoronary FAI; Model 4: HRP + CT-FFR; Model 5: Model 4 + pericoronary FAI. ROC, receiver operating characteristic; MACE, major adverse cardiovascular events; MACCE, major adverse cardiovascular and cerebrovascular events; HRP, high-risk plaque; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; AUC, area under the curve; CI, confidence interval

    Journal: BMC Medical Imaging

    Article Title: Incremental prognostic value of pericoronary fat attenuation index in diabetic patients with non-obstructive coronary artery disease

    doi: 10.1186/s12880-025-02146-6

    Figure Lengend Snippet: ROC curves of all models in predicting MACE ( A ) and MACCE ( B ). Model 1: HRP; Model 2: CT-FFR; Model 3: pericoronary FAI; Model 4: HRP + CT-FFR; Model 5: Model 4 + pericoronary FAI. ROC, receiver operating characteristic; MACE, major adverse cardiovascular events; MACCE, major adverse cardiovascular and cerebrovascular events; HRP, high-risk plaque; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; AUC, area under the curve; CI, confidence interval

    Article Snippet: CT-FFR analysis was performed using a machine learning-based CT-FFR software (version 3.5, Siemens Healthineers, Germany).

    Techniques: Derivative Assay

    A representative case of DM patients with non-obstructive CAD. CCTA showed CAD-RADS 2, with 25–49% stenosis in LAD as well as 1–24% stenosis in RCA, and there was a HRP characterized by low attenuation plaque and spotty calcification in LAD; CT-FFR was 0.88; pericoronary FAI was − 60.97 HU. This patient underwent acute non-ST-segment elevation myocardial infarction 40 months after CCTA. DM, diabetes mellitus; CAD, coronary artery disease; CCTA, coronary computed tomography angiography; CAD-RADS, Coronary Artery Disease-Reporting and Data System; LAD, left anterior descending; LCX, left circumflex; RCA, right coronary artery; HRP, high-risk plaque; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; HU, Hounsfield units

    Journal: BMC Medical Imaging

    Article Title: Incremental prognostic value of pericoronary fat attenuation index in diabetic patients with non-obstructive coronary artery disease

    doi: 10.1186/s12880-025-02146-6

    Figure Lengend Snippet: A representative case of DM patients with non-obstructive CAD. CCTA showed CAD-RADS 2, with 25–49% stenosis in LAD as well as 1–24% stenosis in RCA, and there was a HRP characterized by low attenuation plaque and spotty calcification in LAD; CT-FFR was 0.88; pericoronary FAI was − 60.97 HU. This patient underwent acute non-ST-segment elevation myocardial infarction 40 months after CCTA. DM, diabetes mellitus; CAD, coronary artery disease; CCTA, coronary computed tomography angiography; CAD-RADS, Coronary Artery Disease-Reporting and Data System; LAD, left anterior descending; LCX, left circumflex; RCA, right coronary artery; HRP, high-risk plaque; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; HU, Hounsfield units

    Article Snippet: CT-FFR analysis was performed using a machine learning-based CT-FFR software (version 3.5, Siemens Healthineers, Germany).

    Techniques: Computed Tomography, Derivative Assay