Antonio R. Porras, PhD is Associate Professor of Neurological Surgery at the University of Chicago, where he directs the Neural Image Computing (NIC) Lab and serves as Scientific Director of the Section of Computational Neurosurgery. His research focuses on developing artificial intelligence and medical image computing methods that transform biomedical images and multimodal health data into actionable insights for healthcare. Leading interdisciplinary teams of engineers, data scientists, and clinicians, his work advances precision diagnosis, treatment planning, and predictive modeling across neurosurgery, radiology, pediatrics, genetics, and other medical specialties.
Dr. Porras earned degrees in Computer Engineering and Biomedical Engineering in Spain before completing a PhD in Computational Imaging at Pompeu Fabra University in Barcelona. Following his doctoral training, he pursued extensive postdoctoral and research training at Children's National Hospital and within the NIH research environment, broadening his expertise in developmental disorders, human genetics, and pediatric neurosurgery. This interdisciplinary training helped shape his approach to combining methodological innovation with clinically meaningful applications.
Before joining the University of Chicago, Dr. Porras was a faculty member at the University of Colorado, where he established an internationally recognized research program in medical image computing and artificial intelligence with applications spanning craniofacial disorders, pediatrics, genetics, and surgery. His research has contributed to advances in AI-driven disease diagnosis, image-based phenotyping, surgical planning, and predictive modeling and has been supported by the National Institutes of Health. At Colorado, he was awarded tenure and developed educational and mentoring programs for students, trainees, and early-career investigators while teaching learners across disciplines spanning engineering, data science, and medicine.
Dr. Porras is an active leader in the international medical image computing community. He co-founded the MICCAI Mentorship Program in 2018, has led federal awards supporting participation in MICCAI by U.S.-based students and early-career investigators, serves on the MICCAI Board of Directors, and chairs the MICCAI Career Advancement Working Group. Through these and other initiatives, he has helped expand access to mentorship, professional development, entrepreneurship, and career-building opportunities across the MICCAI community.
Children's National Hospital
USA (DC)
Postdoctoral - Quantitative Imaging, Pediatric Neurosurgery
2020
Foundation for Advanced Education in the Sciences, National Institutes of Health
USA (MD)
Postdoctoral - Medical Genetics, Developmental Biology
2019
Pompeu Fabra University
Spain
PhD - Medical Image Computing
2015
University of Barcelona
Spain
MSc - Biomedical Engineering
2010
University of Cordoba
Spain
Eng - Computer Engineering
2008
University of Cordoba
Spain
BSc - Technical Engineering in Computer Systems
2006
Data-driven personalized surgical treatment selection for metopic and sagittal craniosynostosis.
Data-driven personalized surgical treatment selection for metopic and sagittal craniosynostosis. J Neurosurg Pediatr. 2026 May 29; 1-10.
PMID: 42214106
Population-Driven Synthesis of Personalized Cranial Development From Cross-Sectional Pediatric CT Images.
Population-Driven Synthesis of Personalized Cranial Development From Cross-Sectional Pediatric CT Images. IEEE Trans Biomed Eng. 2025 Sep; 72(9):2732-2741.
PMID: 40100672
Cranial bone thickness and density anomalies quantified from CT images can identify chronic increased intracranial pressure.
Cranial bone thickness and density anomalies quantified from CT images can identify chronic increased intracranial pressure. Neuroradiology. 2024 Oct; 66(10):1817-1828.
PMID: 38871879
Joint Cranial Bone Labeling and Landmark Detection in Pediatric CT Images Using Context Encoding.
Joint Cranial Bone Labeling and Landmark Detection in Pediatric CT Images Using Context Encoding. IEEE Trans Med Imaging. 2023 10; 42(10):3117-3126.
PMID: 37216247
Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry.
Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry. Comput Methods Programs Biomed. 2023 Oct; 240:107689.
PMID: 37393741
Data-driven Normative Reference of Pediatric Cranial Bone Development.
Data-driven Normative Reference of Pediatric Cranial Bone Development. Plast Reconstr Surg Glob Open. 2022 Aug; 10(8):e4457.
PMID: 35983543
Development and evaluation of a machine learning-based point-of-care screening tool for genetic syndromes in children: a multinational retrospective study.
Development and evaluation of a machine learning-based point-of-care screening tool for genetic syndromes in children: a multinational retrospective study. Lancet Digit Health. 2021 10; 3(10):e635-e643.
PMID: 34481768
Quantification of Head Shape from Three-Dimensional Photography for Presurgical and Postsurgical Evaluation of Craniosynostosis.
Quantification of Head Shape from Three-Dimensional Photography for Presurgical and Postsurgical Evaluation of Craniosynostosis. Plast Reconstr Surg. 2019 12; 144(6):1051e-1060e.
PMID: 31764657