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Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals
The goal of this project is to develop and validate machine learning-based software tools to identify key brain imaging measurements in acute stroke patients using brain imaging modalities available in the majority of the hospitals such as CT angiography. This would enable better management of stroke patients and better delivery of endovascular stroke therapy

  • S A Sheth, V Lopez-Rivera, A Barman, J C Grotta, A J Yoo, S Lee, M E Inam, S I Savitz, and L Giancardo, “Machine Learning-Enabled Automated Determination of Acute Ischemic Core From Computed Tomography Angiography,” Stroke, vol. 50, no. 11, pp. 3093–3100, (2019).
  • A Barman, M Inam, S Lee, S Savitz, S Sheth*, L Giancardo*. Determining Ischemic Stroke from CT-Angiography Imaging using Symmetry-Sensitive Convolutional Networks. IEEE International Symposium on Biomedical Imaging (ISBI) (2019).
  • R Abdelkhaleq, Y Kim, S Khose, P Kan, S Salazar-Marioni, L Giancardo*, and S A Sheth*, “Automated prediction of final infarct volume in patients with large-vessel occlusion acute ischemic stroke,” Neurosurgical Focus, vol. 51, no. 1, p. E13, (2021)
  • U M L-T Estrada, G Meeks, S Salazar-Marioni, F Scalzo, M Farooqui, J Vivanco-Suarez, S O Gutierrez*, S A Sheth*, and L Giancardo*, “Quantification of infarct core signal using CT imaging in acute ischemic stroke,” NeuroImage: Clinical, vol. 34, p. 102998, (2022).
  • Giancardo L, Niktabe A, Ocasio L, Abdelkhaleq R, Salazar-Marioni S, Sheth SA. Segmentation of acute stroke infarct core using image-level labels on CT-angiography. NeuroImage: Clinical, vol 37, p. 103362, (2023)
  • U. M. Lal-Trehan Estrada, A. Oliver, S. A. Sheth, X. Lladó, and L. Giancardo, “Strategies to combine 3D vasculature and brain CTA with deep neural networks: Application to LVO,” iScience, vol. 27, no. 2, p. 108881, (2024)
  • Y. Dong, S. Pachade, X. Liang, S. A. Sheth, and L. Giancardo, “A self-supervised learning approach for registration agnostic imaging models with 3D brain CTA,” iScience, vol. 27, no. 3, p. 109004, Mar. (2024)
Actionable Stroke Detection with Deep Learning and Retinal Imaging
This project aims to develop systems and data for identifying acute strokes using retina imaging. This would enable the deployment of life saving drugs in situations where brain scans are typically not available such as ambulances or even space missions.
More information
Stroke care from Bench to Bedside
We are integrating our machine learning algorithms for stroke care into the acute stroke care pathway in a manner that sends automated alerts of its findings to physicians on the care team. This allows us to evaluate their real world impact.

  • A L Czap, M Bahr-Hosseini, N Singh, J-M Yamal, M Nour, S Parker, Y Kim, L Restrepo, R Abdelkhaleq, S Salazar-Marioni, K Phan, R Bowry, S S Rajan, J C Grotta, J L Saver, L Giancardo*, and S A Sheth*, “Machine Learning Automated Detection of Large Vessel Occlusion From Mobile Stroke Unit Computed Tomography Angiography,” Stroke, (online) (2021), p. STROKEAHA.121.036091
  • I Fyfe, “Intelligent, mobile stroke imaging,” Nature Reviews Neurology, vol. 18, no. 2, Art. no. 2, (2022).
Neurodegeneration Markers with Digital Device Interaction and Machine Learning
We are developing new computational methods to detect subtle motor impairments based on timing of finger interactions on a standard personal computer or mobile devices without predefined tests. We created and tested software tools to remotely collect months’ worth of data and computational algorithms able to repeatably detect signs of neurodegenerative diseases.
  • L Giancardo, A Sanchez-Ferro, I Butterworth, C Sanchez-Mendoza and J M Hooker, “Psychomotor Impairment Detection via Finger Interactions with a Computer Keyboard”, Scientific Reports, 5(9678), (2015).
  • L Giancardo, A Sanchez-Ferro, T Arroyo-Gallego, I Butterworth, C Sanchez-Mendoza, P Montero, M Matarazzo, J A Obeso, M L Gray and R San José “Computer keyboard interaction as an indicator of early Parkinson’s disease”, Scientific Reports, 6(34468), (2016)
  • T Arroyo-Gallego, M Ledesma-Carbayo, I Butterworth, C Mendoza, M Matarazzo, P Montero, R Lopez-Blanco, V Puertas-Martin, M Gray, L Giancardo*, and A Sanchez-Ferro*. Detecting Motor Impairment in Early Parkinson’s Disease via Natural Typing Interaction With Keyboards: Validation of the neuroQWERTY Approach in an Uncontrolled At-Home Setting. J. Med. Internet Res. 20, e89 (2018)
  • M Matarazzo, T Arroyo-Gallego, P Montero , et al., L Giancardo*, A S Ferro*. Remote Monitoring of Treatment Response in Parkinson’s Disease: The Habit of Typing on a Computer. Movement Disorders, vol. 34, no. 10, pp. 1488–1495 (2019)
  • S Tripathi, T Arroyo-Gallego, L Giancardo. Keystroke-Dynamics for Parkinson’s Disease Signs Detection in An At-Home Uncontrolled Population: A New Benchmark and Method. IEEE Transactions on Biomedical Engineering, vol. 70, no. 1, pp. 182–192 (2023).
Machine Learning-based Tools for Longitudinal Brain Imaging Analysis and Connectomes
We develop techniques to analyze the changes in structural connectivity and voxel-based changes of the brain longitudinally. These techniques allow for data-driven processing able to discover imaging biomarkers candidates from whole brain or atlas specific diffusion-MRI data. These have application for neurodegenerative diseases such as Parkinson's Disease or Alzheimer. We have also developed approaches for combining imaging and clinical data and sparse machine learning-based method to automatically explore inter-group differences in Diffusion-MRI connectomes in an unbiased-fashion.
  • O Peña-Nogales, T Ellmore, R de Luis-García, J Suescun, M Schiess & L Giancardo. Longitudinal Connectomes as a Candidate Progression Marker for Prodromal Parkinson’s Disease. Frontiers in Neuroscience 12, 1–13 (2019)
  • A Crimi*, L Giancardo*, F Sambataro, A Gozzi, V Murino & D Sona. MultiLink Analysis: Brain Network Comparison via Sparse Connectivity Analysis. Scientific Reports 9, 65 (2019).
  • Pena D, Barman A, Suescun J, Jiang X, Schiess MC, Giancardo L. Quantifying Neurodegenerative Progression With DeepSymNet, an End-to-End Data-Driven Approach. Front Neurosci. 13:1053 (2019)
  • D Pena, J Suescun, M Schiess, T M Ellmore, and L Giancardo, “Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer’s Disease Conversion,” Front Neurosci, vol. 15, p. 744190, (2022).
Machine Learning-based Tools for Retina Image Analysis
General purpose tools for retina image analysis and imaging biomarkers extraction using machine learning-based models with fundus images OCT and OCT-A images
  • L Giancardo, F Meriaudeau, T P Karnowski, K W Tobin, P Favaro, E Grisan, A Ruggeri and E Chaum, “Texture-less Macula Swelling Detection with Multiple Retinal Fundus Images”, IEEE Transactions on Biomedical Engineering, (2011)
  • L Giancardo, F Meriaudeau, T P Karnowski, Y Li, S Gaarg, K W Tobin and E Chaum, “Exudate-based Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets”, Medical Image Analysis 16(1), 216—226, (2012)
  • L Giancardo, K Roberts and Z Zhao, “Representation Learning for Retinal Vasculature Embeddings”. Fetal, Infant and Ophthalmic Medical Image Analysis. FIFI 2017, OMIA 2017. Lecture Notes in Computer Science, vol 10554. Springer, Cham, 2017
  • I Coronado, R Abdelkhaleq, J Yan, S Salazar Marioni, A Jagolino-Cole, R Channa, S Pachade, S A Sheth*, L Giancardo*. “Towards Stroke Biomarkers on Fundus Retinal Imaging: A Comparison Between Vasculature Embeddings and General Purpose Convolutional Neural Network”. Conf Proc IEEE Eng Med Biol Soc. (2021)
  • S Pachade, P Porwal, M Kokare, L Giancardo, and F Mériaudeau, “NENet: Nested EfficientNet and adversarial learning for joint optic disc and cup segmentation,” Med Image Anal, vol. 74, p. 102253, (2021)
Deep Learning-based Tools for MRI Safety
MRI is an imaging modality essential for the diagnosis, prognosis and tracking of several medical condition. However, people with implantable devices are likely to lack accurate information about the specifics of their device. Systems to allow for a quick and safe MRI safety clearance from X-ray images could be tightly integrated with the current radiologist workflow and leverage modern machine learning (ML) algorithms able to identify and quantify image patterns and being continuously updated with newer devices entering the market.
  • L Giancardo, O Arevalo, A Tenreiro, R Riascos, and E Bonfante, “MRI Compatibility: Automatic Brain Shunt Valve Recognition using Feature Engineering and Deep Convolutional Neural Networks,” Scientific reports, vol. 8, no. 1, p. 16052, (2018).
  • S J Sujit, E Bonfante, A Aein, I Coronado, R Riascos-Castaneda, and L Giancardo, “Deep Learning Enabled Brain Shunt Valve Identification Using Mobile Phones,” Computer Methods and Programs in Biomedicine, p. 106356, (2021).