In recent years, convolutional neural networks and deep learning have revolutionized computer vision (CV) applications and they are positively impacting medical practice, following success in many other trades. Automatic interpretation of radiologic images improves patient-specific accuracy and reduces radiologist burnout, either as support to reach accurate and consistent expert-level decisions, or as a second opinion about a potential pathology. Applications also include image interpretation during procedures such as endoscopy, AI-assisted surgery, automatic interpretation of digital pathology.
AI systems can analyse thousands of hours of surgical videos to develop patient-specific surgical procedures, by, for instance, detecting anatomical structures such as major nerves and helping with preventing nerve damage during the procedure. Feeds from classical or infrared cameras can be used in conjunction with computer vision methods for patient safety, logistical optimization, monitoring the spread of infections, health assessment, mobility, and similar. Image-based information can also be combined with other types of medically relevant information to provide synergistic decision support based on visual and non-visual cues. The interrelations between the phenotype, determined on the basis of medical imaging, molecular biology, and other data are also of high relevance to precision medicine.
Mostly focused on neuroimaging and related topic to neuroscience. Particularly fascinated by diffusion MRI, functional MRI, connectivity and microscopy. The focus of the use of these technologies mostly aiming at finding biomarkers for neurodegenerative diseases, but also at fundamental level of neural circuitry.
Extensive knowledge and experience in image analysis and machine learning.
Strong supporter of neurotech devices (as EEG, EMG…), as well as translation of research into companies, and implementation in global health and development by education.
Author of several peer-reviewed pubblications on neuroimaging, medical imaging and global health.
Editor and organizer of the MICCAI (Medical Image Computing and Computer Assisted Interventions) BrainLesion Workshop, published by Springer Verlag.
Supervised several healthcare projects in low- and middle-income countries in Africa, related to diabetes, HIV and prenatal care.
Visiting lecturer at the African Institute for Mathematical Sciences in Ghana and South Africa, mentor for the NextEinstein initiative.
Serial entrepreneur and science communicator.
Joan studied a BSc in physics at the University of Barcelona. Then he moved to Madrid to study a MSc in systems biology at the Universidad Autonoma de Madrid (UAM). His thesis was about computational neuroscience, specifically in " the learning role of dopamine ". After that Joan spent one year collaborating (as a research assistant) with the Theoretical Physics Department in Madrid studying the same things. In Sano he will be working on neuroplasticity after tumor and stroke, and surgical planning. From his personal side, he plays the piano, he loves any kind of sports (specially tennis and football) and he falls in love with mountains.
Sylwia holds a BSc from AGH University of Science and Technology. Her bachelor thesis focused on obtaining 3D-printed pelvis directly from a patient's CT scan. She gained practical experience as a software engineer and data scieSylwia holds a BSc from AGH University of Science and Technology. Her Bachelor’s thesis focused on obtaining 3D printed pelvis directly from a patient’s CT scan. She gained practical experience as a software engineer and data scientist in the industry, where she was focusing on Computer Vision solutions for public safety. In Sano, she works on challenges associated with medical imaging of brain tumors in collaboration with the University of Pennsylvania and the University of Zurich. Privately, she calls herself a renaissance soul due to having lots of interests and hobbies - she is a photomodel, traveler, yoga practitioner, just to name a few.
Monika obtained a BSc in Electroradiology from Collegium Medicum of the Jagiellonian University and an MSc in Bioinformatics from JU. During her master's studies, she was involved in the development of an algorithm for the automatic segmentation of a high-resolution ultrasound image. She has experience working with clinical patients in the hospital Diagnostic Imaging Department as a radiographer. In Sano, she investigates the use of generative adversarial networks to generate data in the context of microscopy. In her free time, she dances pole art, enjoys alternative music concerts, and plays analog RPGs.
Agus obtained his Ph.D. in computational neuroscience from Swinburne University of Technology, Australia, with a dissertation addressing the inference of physiological mechanisms in the human cortex by fitting a neural population model to EEG data. His Ph.D. dissertation was examined by two experts in computational neuroscience, one of whom graded the thesis with the highest grade: “Excellent - Summa cum Laude”. Before doing the Ph.D., he received his M.Sc. in Computer Science from TU Kaiserslautern, Germany. After his Ph.D., he joined the School of Computing, Telkom University, Indonesia, as an academic. His research interests include computational neuroscience, statistical inference, machine learning, logic, and formal methods. He is currently a postdoctoral researcher at Sano, investigating the comorbidity of PTSD and other disorders via machine learning analysis of EEG.
Deep learning and AI enthusiast, holds BSc in Electronics obtained at AGH UST. Currently pursuing a masters degree in Computer Science and Intelligent Systems at the same university. His BSc thesis was about application of deep learning based vision models to assess the aging process in veterinary subjects, a part of CyfroVet project run by ACC Cyfronet AGH in which he still actively participates. Currently working on his masters thesis at Sano in the project that aims to predict the seizure events with deep learning based on the brain EEG signals. Interested in applications of AI in the field of medicine. Privately active brazilian jiu-jitsu competitor, non-fiction books and RPG games lover.
Rosmary is a biotechnologist with experience in data recordings and analysis in the neurological, neurosurgical, and neurorehabilitation fields. She specializes in clinical network neuroscience and has skills in complex electrophysiological signal processing, particularly EEG source functional connectivity and network analysis. Her main interest is studying the large-scale network principles governing neuronal communication, cognition, and human behavior through computational approaches, personalized medicine, and clinical decision support.
Adrian’s background is in clinical psychology (MSc) and computational neuroscience (Ph.D.). His research interests cover the replicability of neuroimaging methods and applications of advanced neuroimaging techniques (MRI, fMRI, and DTI) in neurological disorders. He has experience with the analysis of large neuroimaging datasets and has taken part in some significant projects in the field, including the Neuroimaging Analysis Replication and Prediction Study (NARPS) and the Advancing Concussion Assessment in Pediatrics (A-CAP) study. Previous work has focused on non-linear methods for signal processing, replicability of neuroimaging analysis pipelines, multisite data harmonization, and brain connectivity alterations following traumatic brain injury.
Cemal completed his PhD in computational neuroscience at IMT Lucca, Italy. For his thesis, he examined structural brain plasticity in the case of early and acquired blindness. He is interested in methods for processing and analyzing neuroimaging data. In Sano, he will work on brain connectivity differences due to ischemia, and in collaboration with EPFL Zurich, he will examine stress-related brain circuits in both animal and human fMRI.
Coming soon
Relating Global and Local Connectome Changes to Dementia and Targeted Gene Expression in Alzheimer's Disease Journal Article
In: Front Hum Neurosci, 2021.
An integrated genomic approach to dissect the genetic landscape regulating the cell-to-cell transfer of α-synuclein Journal Article
In: 2021.
Genome-Wide Association Study of Brain Connectivity Changes for Alzheimer’s Disease Journal Article
In: 2020.
Boosting antenatal care attendance and number of hospital deliveries among pregnant women in rural communities: a community initiative in Ghana based on mobile phones applications and portable ultrasound scans Journal Article
In: 2016.
Predictive Value of Imaging Markers at Multiple Sclerosis Disease Onset Based on Gadolinium- and USPIO-Enhanced MRI and Machine Learning Journal Article
In: 2014.
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