Alexandros Sigaras

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Assistant Professor of Research in Physiology and Biophysics

Weill Cornell Medical College

United States

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About

Dr. Alexandros Sigaras is an Assistant Professor of Research in Physiology and Biophysics at Weill Cornell Medical College, USA. His research focuses on the intersection of voice AI technology and medical applications, as evidenced by his recent articles on voice data acquisition and AI-driven predictions in reproductive health. He is actively involved in studies related to mobile health and advanced imaging techniques for assessing human blastocysts.

Recent Grants

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Bridge2AI: Voice as a Biomarker of Health - Building an ethically sourced, bioaccoustic database to understand disease like never before

Open Date: 2022-09-01

Close Date: 2026-08-31

Articles (19)

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Collaborators (6)

Maryou Ballo Kosti Lambros

Institute of Cancer Research

UNITED KINGDOM

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Pegah Khosravi

Assistant Professor

New York City College of Technology

UNITED STATES

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Alexander Gelbard

Associate Professor

Vanderbilt University School of Medicine

UNITED STATES

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Jasmine Gunkel

Assistant Professor, beginning 2025

Western University

CANADA

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Jonas Malmsten

Assistant Professor

Weill Cornell Medicine

UNITED STATES

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Johan Rung

Head of Data Centre, SciLifeLab

Uppsala University

SWEDEN

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Alexandros Sigaras

Research Interests

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Recent Grants

Articles (19)

The Bridge2AI-voice application: initial feasibility study of voice data acquisition through mobile health

Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey

Automatic ploidy prediction and quality assessment of human blastocysts using time-lapse imaging

Summary of Keynote Speeches from the 2024 Voice AI Symposium, presented by the Bridge2AI-Voice Consortium.

The doctor will polygraph you now.

High-fidelity pose estimation for real-time extended reality (XR) visualization for cardiac catheterization

Workshop summaries from the 2024 voice AI symposium, presented by the Bridge2AI-voice consortium

Automatic Ploidy Prediction and Quality Assessment of Human Blastocysts Using Time-Lapse Imaging

A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study.

A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study

The genomic landscape of metastatic clear cell renal cell carcinoma after systemic therapy

Containers in Bioinformatics Applications, Practical Considerations, and Best Practices in Molecular Pathology

Weakly-supervised tumor purity prediction from frozen H&E stained slides

Molecular Evaluation of Low-grade Low-stage Endometrial Cancer With and Without Recurrence.

A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology–Radiology Fusion

A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology-Radiology Fusion

CD38 in Advanced Prostate Cancers.

Building an international consortium for tracking coronavirus health status.

Precision medicine at Weill Cornell Medicine/New York Presbyterian: Breaking silos, integrating resources, being inclusive

Collaborators (6)

Maryou Ballo Kosti Lambros

Pegah Khosravi

Alexander Gelbard

Jasmine Gunkel

Jonas Malmsten

Johan Rung

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