Review



machine-learning software fasteranalytics tm  (DecisionQ Corp)

 
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    DecisionQ Corp machine-learning software fasteranalytics tm
    Machine Learning Software Fasteranalytics Tm, supplied by DecisionQ Corp, 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/machine-learning+software/machine+learning+software+fasteranalytics+tm/pmc08832594-66-3-8
    Average 90 stars, based on 1 article reviews
    machine-learning software fasteranalytics tm - by Bioz Stars, 2026-09
    90/100 stars

    Images

    Related Articles

    Software:

    Article Title: IgG Responses to Tissue-Associated Antigens as Biomarkers of Immunological Treatment Efficacy
    Article Snippet: Our statistical analysis consisted of using a commercially available machine-learning software package (FasterAnalytics, DecisionQ Corporation, Washington, DC).

    Article Title: Development and comparison of 1-year survival models in patients with primary bone sarcomas: External validation of a Bayesian belief network model and creation and external validation of a new gradient boosting machine model
    Article Snippet: Using commercially available machine-learning software (FasterAnalytics TM ; DecisionQ, Washington, DC, USA), which was originally developed to analyze video cassette sales, Nandra et al. generated a Bayesian belief network (BBN) model for 1-year survival of patients with bone sarcoma and demonstrated five factors with conditional dependencies for survival 1 year after surgery.

    Generated:

    Article Title: IgG Responses to Tissue-Associated Antigens as Biomarkers of Immunological Treatment Efficacy
    Article Snippet: Our statistical analysis consisted of using a commercially available machine-learning software package (FasterAnalytics, DecisionQ Corporation, Washington, DC).

    Article Title: Development and comparison of 1-year survival models in patients with primary bone sarcomas: External validation of a Bayesian belief network model and creation and external validation of a new gradient boosting machine model
    Article Snippet: Using commercially available machine-learning software (FasterAnalytics TM ; DecisionQ, Washington, DC, USA), which was originally developed to analyze video cassette sales, Nandra et al. generated a Bayesian belief network (BBN) model for 1-year survival of patients with bone sarcoma and demonstrated five factors with conditional dependencies for survival 1 year after surgery.



    Similar Products

    machine learning based user friendly software platform  (Biomark Inc)
    86
    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
    		  Buy from Supplier
    machine learning software  (Lunit Inc)
    86
    Lunit Inc machine learning software
    Machine Learning Software, supplied by Lunit 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/algorithms+learning+machine/pm41594208-124-17-19
    Average 86 stars, based on 1 article reviews
    machine learning software - by Bioz Stars, 2026-09
    86/100 stars
    		  Buy from Supplier
    machine learning based ct ffr software  (Siemens Healthineers)
    86
    Siemens Healthineers machine learning based ct ffr software
    Flowchart of study design. DM, diabetes mellitus; CCTA, coronary computed tomography angiography; CAD, coronary artery disease; <t>CT-FFR,</t> CCTA-derived fractional flow reserve; FAI, fat attenuation index; MACE, major adverse cardiovascular events
    Machine Learning Based Ct Ffr Software, supplied by Siemens Healthineers, 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/ct+ffr/pmc12870152-141-6-12
    Average 86 stars, based on 1 article reviews
    machine learning based ct ffr software - by Bioz Stars, 2026-09
    86/100 stars
    		  Buy from Supplier
    aivia machine learning software  (Danaher Inc)
    96
    Danaher Inc aivia machine learning software
    Flowchart of study design. DM, diabetes mellitus; CCTA, coronary computed tomography angiography; CAD, coronary artery disease; <t>CT-FFR,</t> CCTA-derived fractional flow reserve; FAI, fat attenuation index; MACE, major adverse cardiovascular events
    Aivia Machine Learning Software, supplied by Danaher Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/machine-learning+software/Aivia+AI+Image+Analysis+Software/bio_rxiv__2025__11__21__689759-66-16-19
    Average 96 stars, based on 1 article reviews
    aivia machine learning software - by Bioz Stars, 2026-09
    96/100 stars
    		  Buy from Supplier
    deep and machine learning software algorithm notal oct analyzer [noa]  (Notal Vision Ltd)
    90
    Notal Vision Ltd deep and machine learning software algorithm notal oct analyzer [noa]
    Flowchart of study design. DM, diabetes mellitus; CCTA, coronary computed tomography angiography; CAD, coronary artery disease; <t>CT-FFR,</t> CCTA-derived fractional flow reserve; FAI, fat attenuation index; MACE, major adverse cardiovascular events
    Deep And Machine Learning Software Algorithm Notal Oct Analyzer [Noa], supplied by Notal Vision Ltd, 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/machine-learning+software/notal+optical+coherence+tomography+analyzer/pm40582342-75-16-24
    Average 90 stars, based on 1 article reviews
    deep and machine learning software algorithm notal oct analyzer [noa] - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    machine learning software  (Oxford Instruments)
    99
    Oxford Instruments machine learning software
    Flowchart of study design. DM, diabetes mellitus; CCTA, coronary computed tomography angiography; CAD, coronary artery disease; <t>CT-FFR,</t> CCTA-derived fractional flow reserve; FAI, fat attenuation index; MACE, major adverse cardiovascular events
    Machine Learning Software, supplied by Oxford Instruments, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/machine-learning+software/Imaris/pm40299956-254-1-0
    Average 99 stars, based on 1 article reviews
    machine learning software - by Bioz Stars, 2026-09
    99/100 stars
    		  Buy from Supplier

    Image Search Results


    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