Ertl-Wagner Paediatric Neuroradiology Research Group<\/h1><\/div>We are a multi-disciplinary team focused on paediatric neuroradiological research.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
A Multidisciplinary Approach<\/h2><\/div><\/div><\/div>Our multidisciplinary team of neuroradiologists, physicists, statisticians, computer science & data scientists, AI scientists, learners and administrators is committed to the advancement of paediatric radiology research, in order to optimize clinical pathways for practitioners and improve patient outcomes. Our work is:<\/p>\n<\/div>
- <\/i><\/span>\n
\n- Data Driven <\/strong>\u2013 analysis of large imaging repositories using artificial intelligence (AI) methodology is the basis of our work.<\/li>\n<\/ul>\n<\/div><\/li>
- <\/i><\/span>\n
\n- Results Orientated<\/strong> \u2013 translating research results into clinical practice and improved patient outcomes is an important facet of our work.<\/li>\n<\/ul>\n<\/div><\/li>
- <\/i><\/span>\n
\n- Research Led <\/strong>\u2013 ethical, REB-approved procedures guide our research processes.<\/li>\n<\/ul>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div><\/div>
Data-Driven Analysis<\/h2><\/div><\/div><\/div>Our main areas of interest in research are translational imaging-based research bridging the gap between basic science innovations and clinical medicine and the field of machine learning and artificial intelligence in medical imaging. Our research utilizes four major approaches:<\/p>\n<\/div>
- <\/i><\/span>\n
\n- Using methods of Artificial Intelligence (AI) including Radiomics and DL for imaging-based characterization of neuropaediatric disorders.<\/li>\n<\/ul>\n<\/div><\/li>
- <\/i><\/span>\n
\n- Imaging based characterization and prognostication of paediatric brain tumours.<\/li>\n<\/ul>\n<\/div><\/li>
- <\/i><\/span>\n
\n- Volumetric trajectories of normal\/abnormal development.<\/li>\n<\/ul>\n<\/div><\/li>
- <\/i><\/span>\n
\n- Imaging based characterization of neuroinflammatory disorders of childhood.<\/li>\n<\/ul>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div><\/div><\/div><\/div>
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<\/span><\/div><\/div>Research<\/h2><\/div>Our core research aims to generate novel and more efficient pathways to improve the outcomes of paediatric neuroradiology patients.<\/p>\n<\/div>
<\/div>Browse our publications<\/span><\/a><\/div><\/div><\/div><\/span><\/div><\/div>Methods<\/h2><\/div>We combine human and artificial intelligence, adopting methods like machine learning (DL) and deep learning (DL)\u00a0 to support our data-driven work.<\/p>\n<\/div>
We are a multi-disciplinary team focused on paediatric neuroradiological research.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
A Multidisciplinary Approach<\/h2><\/div><\/div><\/div>Our multidisciplinary team of neuroradiologists, physicists, statisticians, computer science & data scientists, AI scientists, learners and administrators is committed to the advancement of paediatric radiology research, in order to optimize clinical pathways for practitioners and improve patient outcomes. Our work is:<\/p>\n<\/div>
- <\/i><\/span>\n
\n- Data Driven <\/strong>\u2013 analysis of large imaging repositories using artificial intelligence (AI) methodology is the basis of our work.<\/li>\n<\/ul>\n<\/div><\/li>
- <\/i><\/span>\n
\n- Results Orientated<\/strong> \u2013 translating research results into clinical practice and improved patient outcomes is an important facet of our work.<\/li>\n<\/ul>\n<\/div><\/li>
- <\/i><\/span>\n
\n- Research Led <\/strong>\u2013 ethical, REB-approved procedures guide our research processes.<\/li>\n<\/ul>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div><\/div>
Data-Driven Analysis<\/h2><\/div><\/div><\/div>Our main areas of interest in research are translational imaging-based research bridging the gap between basic science innovations and clinical medicine and the field of machine learning and artificial intelligence in medical imaging. Our research utilizes four major approaches:<\/p>\n<\/div>
- <\/i><\/span>\n
\n- Using methods of Artificial Intelligence (AI) including Radiomics and DL for imaging-based characterization of neuropaediatric disorders.<\/li>\n<\/ul>\n<\/div><\/li>
- <\/i><\/span>\n
\n- Imaging based characterization and prognostication of paediatric brain tumours.<\/li>\n<\/ul>\n<\/div><\/li>
- <\/i><\/span>\n
\n- Volumetric trajectories of normal\/abnormal development.<\/li>\n<\/ul>\n<\/div><\/li>
- <\/i><\/span>\n
\n- Imaging based characterization of neuroinflammatory disorders of childhood.<\/li>\n<\/ul>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/span><\/div><\/div>
Research<\/h2><\/div>Our core research aims to generate novel and more efficient pathways to improve the outcomes of paediatric neuroradiology patients.<\/p>\n<\/div>
<\/div>Browse our publications<\/span><\/a><\/div><\/div><\/div><\/span><\/div><\/div>Methods<\/h2><\/div>We combine human and artificial intelligence, adopting methods like machine learning (DL) and deep learning (DL)\u00a0 to support our data-driven work.<\/p>\n<\/div>
Our multidisciplinary team of neuroradiologists, physicists, statisticians, computer science & data scientists, AI scientists, learners and administrators is committed to the advancement of paediatric radiology research, in order to optimize clinical pathways for practitioners and improve patient outcomes. Our work is:<\/p>\n<\/div>
- <\/i><\/span>\n
- \n
- Data Driven <\/strong>\u2013 analysis of large imaging repositories using artificial intelligence (AI) methodology is the basis of our work.<\/li>\n<\/ul>\n<\/div><\/li>
- <\/i><\/span>
\n- \n
- Results Orientated<\/strong> \u2013 translating research results into clinical practice and improved patient outcomes is an important facet of our work.<\/li>\n<\/ul>\n<\/div><\/li>
- <\/i><\/span>
\n- \n
- Research Led <\/strong>\u2013 ethical, REB-approved procedures guide our research processes.<\/li>\n<\/ul>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div><\/div>
Data-Driven Analysis<\/h2><\/div>
<\/div><\/div>Our main areas of interest in research are translational imaging-based research bridging the gap between basic science innovations and clinical medicine and the field of machine learning and artificial intelligence in medical imaging. Our research utilizes four major approaches:<\/p>\n<\/div>
- <\/i><\/span>\n
- \n
- Using methods of Artificial Intelligence (AI) including Radiomics and DL for imaging-based characterization of neuropaediatric disorders.<\/li>\n<\/ul>\n<\/div><\/li>
- <\/i><\/span>\n
- \n
- Imaging based characterization and prognostication of paediatric brain tumours.<\/li>\n<\/ul>\n<\/div><\/li>
- <\/i><\/span>\n
- \n
- Volumetric trajectories of normal\/abnormal development.<\/li>\n<\/ul>\n<\/div><\/li>
- <\/i><\/span>\n
- \n
- Imaging based characterization of neuroinflammatory disorders of childhood.<\/li>\n<\/ul>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>
Research<\/h2><\/div>
Our core research aims to generate novel and more efficient pathways to improve the outcomes of paediatric neuroradiology patients.<\/p>\n<\/div>
<\/div>Browse our publications<\/span><\/a><\/div><\/div><\/div><\/div>Methods<\/h2><\/div>
We combine human and artificial intelligence, adopting methods like machine learning (DL) and deep learning (DL)\u00a0 to support our data-driven work.<\/p>\n<\/div>
- Imaging based characterization of neuroinflammatory disorders of childhood.<\/li>\n<\/ul>\n<\/div><\/li><\/ul>
- <\/i><\/span>
- <\/i><\/span>
- <\/i><\/span>
- Data Driven <\/strong>\u2013 analysis of large imaging repositories using artificial intelligence (AI) methodology is the basis of our work.<\/li>\n<\/ul>\n<\/div><\/li>
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