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deep and machine learning software algorithm notal oct analyzer [noa]  (Notal Vision Ltd)

 
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    Notal Vision Ltd deep and machine learning software algorithm notal oct analyzer [noa]
    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

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    Related Articles

    Tomography:

    Article Title: Characterizing and Predicting Response to Intravitreal Anti-Vascular Endothelial Growth Factor Treatment in Eyes with Retinal Vein Occlusion in Routine Clinical Practice
    Article Snippet: .. Retinal uid was quanti ed using Notal Optical Coherence Tomography Analyzer (Notal Vision Ltd., Tel Aviv, Israel). ..

    Article Title: Characterizing and Predicting Response to Intravitreal Anti-Vascular Endothelial Growth Factor Treatment in Eyes with Retinal Vein Occlusion in Routine Clinical Practice
    Article Snippet: .. Retinal fluid was quantified using Notal Optical Coherence Tomography Analyzer (Notal Vision Ltd., Tel Aviv, Israel). ..

    other:

    Article Title: The British-Israeli Project for Algorithm-Based Management of Age-related Macular Degeneration: Deep Learning Integration for Real- World Data Management and Analysis.
    Article Snippet: Deep learning quantitative OCT analysis All anonymized OCT volume scans were analyzed using a deep and machine learning software algorithm (Notal OCT Analyzer [NOATM], Notal Vision Ltd., Tel Aviv, Israel), which segments OCT B-scans in an automated manner.

    Article Title: Predictors of limited early response to anti-vascular endothelial growth factor therapy in neovascular age-related macular degeneration with machine learning feature importance
    Article Snippet: OCTs taken at baseline and 3 months by Cirrus High-Definition Spectral Domain OCT (V.9.5.1, Carl Zeiss Meditech, Dublin CA) were analyzed by Notal OCT Analyzer (Notal Vision Ltd., Tel Aviv, Israel), a validated ML algorithm that automatically quantifies IRF and SRF in nAMD.

    Article Title: Retinal Specialist versus Artificial Intelligence Detection of Retinal Fluid from OCT
    Article Snippet: The Notal OCT Analyzer (NOA, Notal Vision Ltd, Tel Aviv, Israel) is one such AI machine learning-based software tool.

    Article Title: [Digital remote monitoring of chronic retinal conditions-A clinical future tool? : Remote monitoring of chronic retinal conditions].
    Article Snippet: Das Notal-Home-OCT-System (NHOCT Scanly®, Notal Vision Ltd., Tel Aviv, Israel) kombiniert die OCT-Akquisition über „spectral domain technology“ mit einer KI(künstliche Intelligenz)-gestützten Auswertung des Bildmaterials auf einem cloudbasierten Server und wurde von der US-amerikanischen Zulassungsbehör- 828 Die Ophthalmologie 10 · 2024 de FDA positiv bewertet und rezent auf dem US-Markt zugelassen [34, 42, 49].

    Software:

    Article Title: Performance of a Machine-Learning Computational Image Analysis Algorithm in Retinal Fluid Quantification for Patients With Diabetic Macular Edema and Retinal Vein Occlusions.
    Article Snippet: Optical coherence tomography (OCT) has become the mainstay in the diagnosis and management of vitreoretinal disorders, including neovascular age-related macular degeneration (nAMD), diabetic macular edema (DME), and retinal vein occlusion (RVO).1-3 Anti–vascular endothelial growth factor injections are the first-line treatment of these pathologies, which are characterized by accumulation of fluid in the macula leading to significant impairment in visual acuity.. Treatment decisions are based on OCT imaging and entail evaluating individual B-scans within a macular cube for the presence of fluid.. This can be a timeconsuming process in a busy routine retina practice even when each macular scan is evaluated, leading to suboptimal outcomes and treatment paradigms.4 As patient volumes increase in retina clinics, there is a need to develop automated algorithms that can

    Article Title: The British-Israeli Project for Algorithm-Based Management of Age-related Macular Degeneration: Deep Learning Integration for Real- World Data Management and Analysis.
    Article Snippet: .. Deep learning quantitative OCT analysis All anonymized OCT volume scans were analyzed using a deep and machine learning software algorithm (Notal OCT Analyzer [NOA], Notal Vision Ltd., Tel Aviv, Israel), which segments OCT B-scans in an automated manner. .. The NOA algorithm, its application in nAMD and its diagnostic accuracy have been previously validated on Cirrus and Spectralis systems [22] and its output contains 115 variables, as detailed in Supplementary Table, including fluid parameters [SRF, IRF, and total retinal fluid (TRF) volumes (nl); average IRF, SRF, and TRF heights (μm)], analyzed en-face area (mm); retinal volume (nl); average retinal height (μm), vitreomacular interface abnormalities; volume of RPE irregularities (mm), and average RPE irregularities height (μm).



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    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
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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