Seyedmehdi Payabvash

Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging

2025 · 23 Zit. · BioMedInformatics

Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports

2022 · 22 Zit. · JAMA Network Open

Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis

2022 · 16 Zit. · Frontiers in Oncology

Value Proposition of FDA-Approved Artificial Intelligence Algorithms for Neuroimaging

2023 · 9 Zit. · Journal of the American College of Radiology

Artificial Intelligence for Neuroimaging and Musculoskeletal Radiology: Overview of Current Commercial Algorithms

2023 · 5 Zit. · Seminars in Roentgenology

Editorial: Radiomics Advances Precision Medicine

2022 · 4 Zit. · Frontiers in Oncology

OTHR-15. Assessment of TRIPOD adherence in articles developing machine learning models for differentiation of glioma from brain metastasis

2021 · 4 Zit. · Neuro-Oncology Advances

NIMG-35. MACHINE LEARNING GLIOMA GRADE PREDICTION LITERATURE: A TRIPOD ANALYSIS OF REPORTING QUALITY

2021 · 2 Zit. · Neuro-Oncology

Artificial intelligence in radiology: A comparative analysis of reimbursement and regulatory developments in the US and EU

2025 · 1 Zit. · European Journal of Radiology Artificial Intelligence

Bias assessment of Artificial Intelligence papers in Glioma segmentation using TRIPOD (P14-9.005)

2022 · 0 Zit. · Neurology

Abstract A053: Development and multicentric validation of a publicly-available head CT deidentification tool to facilitate inter-institutional stroke image sharing

2026 · 0 Zit. · Stroke

Abstract DP042: Explainable natural language processing (NLP) models to predict 90-day mortality of different stroke types from clinical note

2026 · 0 Zit. · Stroke

NIMG-38. MEASURING ADHERENCE TO TRIPOD OF ARTIFICIAL INTELLIGENCE PAPERS IN THE GLIOMA SEGMENTATION

2021 · 0 Zit. · Neuro-Oncology