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Behnam Javanmardi

Research Group: Artificial Intelligence in Medical Image Analysis (AIMIA)

Behnam
 

Dr. rer. nat. Behnam Javanmardi
 
Institute for Genomic Statistics and Bioinformatics
Universitätsklinikum Bonn
Rheinische Friedrich-Wilhelms-Universität Bonn
Venusberg-Campus 1
53127 Bonn
 
E-mail: bjav   [at]   uni-bonn.de
Phone: +49 228 287-16546

 
 
 Publications          LinkedIn          Twitter          GitHub

Research 

The rapid developments in Artificial Intelligence (AI) and, at the same time, the emergence of Big Data in medical fields have provided researchers with unprecedented opportunities for advancement of Precision and Personalized Medicine. In our research group, we exploit state-of-the-art AI techniques in Computer-Vision and Automated Image Analysis to develop tools and methods for computer-assisted diagnosis of Genetic Disorders.

Projects

B2G
 
Diagnosis of Rare Skeletal Disorders

Although individually rare, skeletal dysplasias collectively constitute an important group of genetic disorders often resulting in short stature, altered movement biomechanics, pain, fatigue, and reduced functional performance. The clinical diagnosis of these disorders usually requires recognizing patterns on skeletal X-Ray images which is a challenging task since most of these disorders are extremely rare, and even experienced clinicians might have seen only some of these disorders. This is where AI and computer-vision can play an important role in providing assistive tools for prompt detection and identification of these genetic disorders.

In this project, we are collecting hand X-Ray images of patients diagnosed with skeletal dysplasias and we are using deep Convolutional Neural Networks (CNN) to build accurate AI models for the identification of phenotypic patterns and classification of different rare skeletal diseases.

 
 
 
 
DR1234
 
 
Diagnosis of Diabetic Retinopathy
 

The number of people with diabetes rose from 108 million in 1980 to 422 million in 2014 and is estimated to rise to 700 million by 2045. Up to 80% of patients with diabetes are affected by Diabetic Retinopathy (DR) also known as diabetic eye disease. While DR is one of the major causes of blindness, the risk of vision loss can be reduced with early detection and treatment. Therefore, frequent monitoring and screening of diabetic patients are crucial for preventing vision-threatening outcomes. AI-based methods have already proven to be effective in the identification of different stages of DR. 

In this project, we aim at improving the sensitivity of deep learning models in the recognition of early stages of DR using both fundus images and polygenic risk scores

Group Members

 Tzung-Chien Hsieh          Alexander Hustinx          Miguel Ibarra          Sebastian Rassmann          Ashly Sebastine
 

Scientific Vita

Mar. 2021 - Present          Group Leader at the Institute for Genomic Statistics and Bioinformatics, University of Bonn

Jan. 2019 - Mar. 2021       Postdoctoral Researcher at the French National Centre for Scientific Research (CNRS), Paris

May 2017 - Dec. 2018       Postdoctoral Researcher at the Institute for Research in Fundamental Sciences (IPM), Tehran

February 2017                   Dr. rer. nat. (PhD) in Astrophysics, Rheinische Friedrich ­Wilhelms­ Universität Bonn

Oct. 2012 - Feb. 2017       Member of the International Max Planck Research School for Astronomy and Astrophysics, Bonn & Cologne

                                          Researcher in the SPODYR group at the Argelander Institute for Astronomy, University of Bonn

                                          Researcher at the Max Planck Institute for Radio Astronomy, Bonn

2010 - 2012                       Master of Science in Physics & Astronomy, Shahid Beheshti University, Tehran

2006 - 2010                       Bachelor of Science in Physics, Shiraz University, Shiraz

 
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