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The following publication list has been updated on January 2023 and uniquely contains publications associated with the Giancardo Lab at UTHealth. For the full publication list associated with each member of the team please check Google scholar or NCBI bibliography links in the Team page

Refereed Original Articles in Journals

  • T Arroyo-Gallego, M Ledesma-Carbayo, A Sanchez-Ferro, I Butterworth, C Sanchez-Mendoza, M Matarazzo, P Montero, R Lopez-Blanco V Puertas-Martin, R Trincado and L Giancardo “Detection of Motor Impairment in Parkinson's Disease via Mobile Touchscreen Typing”, IEEE Biomedical Engineering PP(99), (2017). 
  • A Sánchez-Ferro, M Matarazzo, P Martínez-Martín, C Martínez-Ávila , A G de la Cámara, L Giancardo L, T Arroyo-Gallego, P Montero , V Puertas-Martín, I Obeso, I Butterworth, C S Mendoza, N J Catalán, A J Molina, F Bermejo Pareja, J C Martínez Castrillo, L López Manzanares, A Alonso Cánovas, J H Rodríguez, M Gray. Minimal Clinically Important Difference for UPDRS-III in Daily Practice.  Movement Disorders Clinical Practice. 5(4), 448-450 (2018).
  • 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).
  • Porwal P, Pachade S, Kokare M, Giancardo L, Mériaudeau F. Retinal image analysis for disease screening through local tetra patterns. Computers in Biology and Medicine, 102:200–10 (2018)
  • L Giancardo, O Arevalo, A Terneiro, R Riascos, E Bonfante, “MRI Compatibility: Automatic Brain Shunt Valve Recognition using Feature Engineering and Deep Convolutional Neural Networks”, Scientific Reports, 8(16052), 2018.
  • Zafar S, McCormick J, Giancardo L, Saidha S, Abraham A, Channa R. "Retinal imaging for neurologic diseases: "a window into the brain” International Ophthalmology Clinics, 59 (1), 137-154 (2019).
  • 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).
  • 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 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). 
  • 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). 
  • P Porwal et al., L Giancardo, G Quellec, and F Mériaudeau, “IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge,” Medical Image Analysis, vol. 59, p. 101561, (2020).
  • S Pachade, P Porwal, M Kokare, L Giancardo, and F Meriaudeau, “Retinal vasculature segmentation and measurement framework for color fundus and SLO images,” Biocybernetics and Biomedical Engineering, vol. 40, no. 3, pp. 865–900, (2020).
  • K J Kiser, S Ahmed, S Stieb, A S R Mohamed, H Elhalawani, P Y S Park, N S Doyle, B J Wang, A Barman, Z Li, W J Zheng, C D Fuller, and L Giancardo, “PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines,” Medical Physics, vol. 47, no. 11, pp. 5941–5952, (2020). 
  • Y Kim, X Jiang, L Giancardo, D Pena, A S Bukhbinder, A Y Amran, and P E Schulz, “Multimodal Phenotyping of Alzheimer’s Disease with Longitudinal Magnetic Resonance Imaging and Cognitive Function Data,” Sci Rep, vol. 10, no. 1, p. 5527, (2020).
  • S Pachade, P Porwal, D Thulkar, M Kokare, G Deshmukh, V Sahasrabuddhe, L Giancardo, G Quellec, and F Mériaudeau, “Retinal Fundus Multi-Disease Image Dataset (RFMiD): A Dataset for Multi-Disease Detection Research,” Data, vol. 6, no. 2, Art. no. 2, (2021).
  • K J Kiser, A Barman, S Stieb, C D Fuller, and L Giancardo, “Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better Than Traditional Metrics in a Thoracic Cavity Segmentation Workflow,” J Digit Imaging, (2021).
  • Y Kim, S Lee, R Abdelkhaleq, V Lopez-Rivera, B Navi, H Kamel, S I Savitz, A L Czap, J C Grotta, L D McCullough, T M Krause, L Giancardo, F S Vahidy, and S A Sheth, “Utilization and Availability of Advanced Imaging in Patients With Acute Ischemic Stroke,” Circulation: Cardiovascular Quality and Outcomes, vol. 14, no. 4, p. e006989, (2021).
  • 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). 
  • 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). 
  • 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), p. STROKEAHA.121.036091. 
  • 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).
  • 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).
  • J Holcomb, L C Oliveira, L Highfield, K O Hwang, L Giancardo, and E V Bernstam, “Predicting health-related social needs in Medicaid and Medicare populations using machine learning,” Sci Rep, vol. 12 (2022).
  • 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).
  • Zhang X, Xie Z, Xiang Y, Baig I, Kozman M, Stender C, L Giancardo and C Tao. Issues in Melanoma Detection: Semisupervised Deep Learning Algorithm Development via a Combination of Human and Artificial Intelligence. JMIR Dermatology, 5:e39113 (2022).
  • A A Holmes, S Tripathi, E Katz, I Mondesire-Crump, R Mahajan, A Ritter, T Arroyo-Gallego, L Giancardo. "A novel framework to estimate cognitive impairment via finger interaction with digital devices". Brain Communication, vol. 4, no. 4 (2022).
  • S. Pachade, I. Coronado, R. Abdelkhaleq, J. Yan, S. Salazar-Marioni, A. Jagolino, M. Bahrainian, R. Channa, S. A. Sheth, L. Giancardo. "Detection of Stroke with Retinal Microvascular Density and Self-Supervised Learning Using OCT-A and Fundus Imaging " J. Clin. Med, 11(24), 7408 (2022).
  • S A Sheth, L Giancardo, M Colasurdo, V M Srinivasan, A Niktabe, P Kan, "Machine learning and acute stroke imaging". Journal of NeuroInterventional Surgery, 15:195–9 (2023).
  • 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).

 Refereed Original Articles in International Conferences

  • L Giancardo, K Roberts and Z Zhao, “Representation Learning for Retinal Vasculature Embeddings”. in Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 10554 LNCS, 243–250 (2017).
  • L Giancardo, T Ellmore, J Suescun, L Ocasio, A Kamali, R Riascos-Castaneda and M Schiess. Longitudinal Connectome-based Predictive Modeling for REM Sleep Behavior Disorder from Structural Brain Connectivity. Proceeding SPIE Med. Imaging, Medical Imaging 2018: Computer-Aided Diagnosis in Med. Imaging 2018 Comput. Diagnosis (eds. Mori, K. & Petrick, N.) 18 (2018).
  • 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). 
  • S Capoglu, J Savarraj, S A Sheth, C H Choi, L Giancardo. Representation Learning of 3D Brain Angiograms, an Application for Cerebral Vasospasm Prediction. Conf Proc IEEE Eng Med Biol Soc. (2019). 
  • R Zhang, L Giancardo, D A Pena, Y Kim, H Tong, X Jiang. From Brain Imaging to Graph Analysis: a study on ADNI's patient cohort. AMIA 2020 Informatics summit (2020).
  • A Barman, V Lopez-Rivera, S Lee, F Vahidy, J Fan, S Savitz, S Sheth and L Giancardo. Combining Symmetric and Standard Deep Convolutional Representations for Detecting Acute Hemorrhagic Stroke. Proceeding SPIE Medical Imaging (2020). 
  • Z Li, R Li, K Kiser, L Giancardo*, W J Zheng*. “Segmenting Thoracic Cavities with Neoplastic Lesions: A Head-to-head Benchmark with Fully Convolutional Neural Networks”. ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. (2021).
  • S Fan, M E Inam, D Peña, P R Chen*, and L Giancardo*, “X-Ray Based Automatic Detection Of Brain Coil Compaction Using Unsupervised Learning,” IEEE 18th International Symposium on Biomedical Imaging (ISBI) (2021). 
  • 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).