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deep-learning algorithms  (PathAI Inc)

 
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    PathAI Inc deep-learning algorithms
    Deep Learning Algorithms, supplied by PathAI 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/machine+learning+software/pmc06833180-5016-16-20
    Average 90 stars, based on 1 article reviews
    deep-learning algorithms - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    other:

    Article Title: Validation of a whole slide image management system for metabolic-associated steatohepatitis for clinical trials.
    Article Snippet: Finally, with the crucial foundation of this validation evidence, which established glass-to-WSI equivalence specific to the MASH trial use case, assistive AI-based tools can now be offered to pathologists as a part of the trial IMS to help solve the challenge of © 2024 PathAI.

    Article Title: Pathology Visions 2023 Overview
    Article Snippet: Our group conducted a comparison of standard of care (SOC) PD-L1 quantification performed by a pathologist with an automated quantification performed by an experimental artificial intelligence (AI) algorithm (PathAI).

    Article Title: Use of Artificial Intelligence for Liver Diseases: A Survey from the EASL Congress 2024
    Article Snippet: For example, a proprietary AI-based tool (AIM-MASH AI Assist*, PathAI, US) was used to assess liver biopsies of 1,451 cirrhotic and non-cirrhotic patients for MASH clinical trial enrollment and endpoint assessment and showed positive results38.

    Article Title: Use of Artificial Intelligence for Liver Diseases: A Survey from the EASL Congress 2024
    Article Snippet: In a related study, a proprietary AI-based tool (AIM-MASH, PathAI) and additionally developed ML models (Liver Explore*, PathAI) were applied to WSIs from 3,577 patients enrolled in MASH clinical trials to characterize fibrosis composition by identifying different cell types and tissue regions, showing the biological support for the tool39.

    Article Title: AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis
    Article Snippet: The authors declare the following competing interests: J.A., S.J., D.R., H.P., K.L., A.P., J.C., M.N., C.K., S.A.J., R.B., N.H., D.Sh., N.I., D.Sa., R.E., B.T., Y.G., J.A.B., A.D., M.C.M., C.P., I.W., A.K., M.G.D., L.Y., and A.T.W. are currently, or were formerly, employed by and receive stock options from PathAI, Inc., a company that builds artificial intelligence tools for pathology.

    Article Title: Use of artificial intelligence for liver diseases: A survey from the EASL congress 2024
    Article Snippet: In a related study, a proprietary AI-based tool (AIM-MASH, PathAI) and additionally developed ML models (Liver Explore∗, PathAI) were applied to WSIs from 3,577 patients enrolled in MASH clinical trials to characterise fibrosis composition by identifying different cell types and tissue regions .

    Article Title: Use of artificial intelligence for liver diseases: A survey from the EASL congress 2024
    Article Snippet: For example, positive results were reported when using a proprietary AI-based tool (AIM-MASH AI Assist∗, PathAI, US) to assess liver biopsies of 1,451 patients with/without cirrhosis for MASH clinical trial enrolment and endpoint assessment.

    Software:

    Article Title: Spatial mapping of gene signatures in H&E-stained images: a proof of concept for interpretable predictions using additive multiple instance learning.
    Article Snippet: .. The authors would like to thank the software engineering and machine learning operations teams at PathAI for developing the systems and pipelines used for model development and feature extraction. ..

    Extraction:

    Article Title: Spatial mapping of gene signatures in H&E-stained images: a proof of concept for interpretable predictions using additive multiple instance learning.
    Article Snippet: .. The authors would like to thank the software engineering and machine learning operations teams at PathAI for developing the systems and pipelines used for model development and feature extraction. ..



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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