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proprietary machine-learning algorithm software ciphersensor  (Flashback Technologies Inc)

 
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    Structured Review

    Flashback Technologies Inc proprietary machine-learning algorithm software ciphersensor
    Proprietary Machine Learning Algorithm Software Ciphersensor, supplied by Flashback Technologies 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/machine+learning+software/proprietary+machine+learning+algorithm+software+ciphersensor/pm37948269-46-13-17
    Average 90 stars, based on 1 article reviews
    proprietary machine-learning algorithm software ciphersensor - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    Battery:

    Article Title: Intraoperative Use of Compensatory Reserve Measurement in Orthotopic Liver Transplant: Improved Sensitivity for the Prediction of Hypovolemic Events.
    Article Snippet: The oximeter was attached to a battery-powered computer unit that included a proprietary machine-learning algorithm software (CipherSensor, Flashback Technologies Inc.) designed to continuously interrogate hundreds of features of each arterial waveform and interpret them based on a reference library consisting of >650,000 waveforms generated from 101 human subjects who underwent progressive reductions in central blood volume until the point of hemodynamic decompensation.18 The framework of the CRM output is modeled after a fuel tank concept, where 100% is considered a “full tank” and 0% is considered “empty” (i.e., decompensated shock).2,6,7,11 Validation of this algorithm in various clinical settings has been shown in several previous studies.8–17 Research staff monitoring the outputs were instructed to periodically assess the device for proper placement and functioning and were trained in basic troubleshooting.

    Software:

    Article Title: Intraoperative Use of Compensatory Reserve Measurement in Orthotopic Liver Transplant: Improved Sensitivity for the Prediction of Hypovolemic Events.
    Article Snippet: The oximeter was attached to a battery-powered computer unit that included a proprietary machine-learning algorithm software (CipherSensor, Flashback Technologies Inc.) designed to continuously interrogate hundreds of features of each arterial waveform and interpret them based on a reference library consisting of >650,000 waveforms generated from 101 human subjects who underwent progressive reductions in central blood volume until the point of hemodynamic decompensation.18 The framework of the CRM output is modeled after a fuel tank concept, where 100% is considered a “full tank” and 0% is considered “empty” (i.e., decompensated shock).2,6,7,11 Validation of this algorithm in various clinical settings has been shown in several previous studies.8–17 Research staff monitoring the outputs were instructed to periodically assess the device for proper placement and functioning and were trained in basic troubleshooting.

    Generated:

    Article Title: Intraoperative Use of Compensatory Reserve Measurement in Orthotopic Liver Transplant: Improved Sensitivity for the Prediction of Hypovolemic Events.
    Article Snippet: The oximeter was attached to a battery-powered computer unit that included a proprietary machine-learning algorithm software (CipherSensor, Flashback Technologies Inc.) designed to continuously interrogate hundreds of features of each arterial waveform and interpret them based on a reference library consisting of >650,000 waveforms generated from 101 human subjects who underwent progressive reductions in central blood volume until the point of hemodynamic decompensation.18 The framework of the CRM output is modeled after a fuel tank concept, where 100% is considered a “full tank” and 0% is considered “empty” (i.e., decompensated shock).2,6,7,11 Validation of this algorithm in various clinical settings has been shown in several previous studies.8–17 Research staff monitoring the outputs were instructed to periodically assess the device for proper placement and functioning and were trained in basic troubleshooting.

    Biomarker Discovery:

    Article Title: Intraoperative Use of Compensatory Reserve Measurement in Orthotopic Liver Transplant: Improved Sensitivity for the Prediction of Hypovolemic Events.
    Article Snippet: The oximeter was attached to a battery-powered computer unit that included a proprietary machine-learning algorithm software (CipherSensor, Flashback Technologies Inc.) designed to continuously interrogate hundreds of features of each arterial waveform and interpret them based on a reference library consisting of >650,000 waveforms generated from 101 human subjects who underwent progressive reductions in central blood volume until the point of hemodynamic decompensation.18 The framework of the CRM output is modeled after a fuel tank concept, where 100% is considered a “full tank” and 0% is considered “empty” (i.e., decompensated shock).2,6,7,11 Validation of this algorithm in various clinical settings has been shown in several previous studies.8–17 Research staff monitoring the outputs were instructed to periodically assess the device for proper placement and functioning and were trained in basic troubleshooting.



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    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