Fedaa Najdawi

Assistant Professor / Medical Director of Informatics and Digital Pathology

Vanderbilt University Medical Center

United States

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Dr. Fedaa Najdawi is an Assistant Professor and Medical Director of Informatics and Digital Pathology at Vanderbilt University Medical Center, USA. Her research focuses on applications of artificial intelligence in pathology, particularly in gastrointestinal diseases and medullary thyroid carcinoma. Recent publications include studies on histological analyses for celiac disease, advancements in AI for gastrointestinal tract biopsies, and prognostic models in medullary thyroid carcinoma.

Articles (10)

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2024

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2024

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2024

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2024

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2024

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2023

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2023

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2022

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2022

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2022

Collaborators (2)

Kartik Viswanathan

Assistant Professor

Emory University Hospital

UNITED STATES

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Anthony Glover

Associate Professor

University of Sydney

AUSTRALIA

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Fedaa Najdawi

Research Interests

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Articles (10)

Novel prognostic nomogram for predicting recurrence‐free survival in medullary thyroid carcinoma

Association of the Genomic Profile of Medullary Thyroid Carcinoma with Tumor Characteristics and Clinical Outcomes in an International Multicenter Study

A feasibility study using quantitative and interpretable histological analyses of celiac disease for automated cell type and tissue area classification

Advancing Artificial Intelligence Integration Into the Pathology Workflow: Exploring Opportunities in Gastrointestinal Tract Biopsies

Challenges in IBD Research 2024: Precision Medicine

Artificial Intelligence Enables Quantitative Assessment of Ulcerative Colitis Histology

QUANTITATIVE AND EXPLAINABLE ARTIFICIAL INTELLIGENCE (AI)-POWERED APPROACHES TO PREDICT ULCERATIVE COLITIS DISEASE ACTIVITY FROM HEMATOXYLIN AND EOSIN (H&E)-STAINED WHOLE SLIDE IMAGES (WSI)

Grading of Medullary Thyroid Carcinoma: an Interobserver Reproducibility Study

International Medullary Thyroid Carcinoma Grading System: A Validated Grading System for Medullary Thyroid Carcinoma

International Medullary Thyroid Carcinoma Grading System: A Validated Grading System for Medullary Thyroid Carcinoma

Collaborators (2)

Kartik Viswanathan

Anthony Glover

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Fedaa Najdawi

Research Interests

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Contact this professor

About

Articles (10)

Novel prognostic nomogram for predicting recurrence‐free survival in medullary thyroid carcinoma

Association of the Genomic Profile of Medullary Thyroid Carcinoma with Tumor Characteristics and Clinical Outcomes in an International Multicenter Study

A feasibility study using quantitative and interpretable histological analyses of celiac disease for automated cell type and tissue area classification

Advancing Artificial Intelligence Integration Into the Pathology Workflow: Exploring Opportunities in Gastrointestinal Tract Biopsies

Challenges in IBD Research 2024: Precision Medicine

Artificial Intelligence Enables Quantitative Assessment of Ulcerative Colitis Histology

QUANTITATIVE AND EXPLAINABLE ARTIFICIAL INTELLIGENCE (AI)-POWERED APPROACHES TO PREDICT ULCERATIVE COLITIS DISEASE ACTIVITY FROM HEMATOXYLIN AND EOSIN (H&amp;E)-STAINED WHOLE SLIDE IMAGES (WSI)

Grading of Medullary Thyroid Carcinoma: an Interobserver Reproducibility Study

International Medullary Thyroid Carcinoma Grading System: A Validated Grading System for Medullary Thyroid Carcinoma

International Medullary Thyroid Carcinoma Grading System: A Validated Grading System for Medullary Thyroid Carcinoma

Collaborators (2)

Kartik Viswanathan

Anthony Glover

Make your next move.

QUANTITATIVE AND EXPLAINABLE ARTIFICIAL INTELLIGENCE (AI)-POWERED APPROACHES TO PREDICT ULCERATIVE COLITIS DISEASE ACTIVITY FROM HEMATOXYLIN AND EOSIN (H&E)-STAINED WHOLE SLIDE IMAGES (WSI)