A5050719623- Lung Cancer Diagnosis and Treatment
- COVID-19 diagnosis using AI
- Neurological disorders and treatments
- Radiomics and Machine Learning in Medical Imaging
- Advanced MRI Techniques and Applications
- Biomedical and Engineering Education
- Educational Leadership and Innovation
- Advanced Neuroimaging Techniques and Applications
- Problem and Project Based Learning
- Neurological and metabolic disorders
- E-Learning and Knowledge Management
- Functional Brain Connectivity Studies
- Parkinson's Disease Mechanisms and Treatments
- Infectious Encephalopathies and Encephalitis
- Neuroscience and Neural Engineering
- Epilepsy research and treatment
- Transcranial Magnetic Stimulation Studies
Summa Health System
2022-2025
Sanford USD Medical Center
2025
Tecnológico de Monterrey
2021-2023
Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán
2021-2023
Illinois Institute of Technology
2018-2019
Rush University Medical Center
2016-2018
University of Illinois Chicago
2018
The annual Deep Brain Stimulation (DBS) Think Tank aims to create an opportunity for a multidisciplinary discussion in the field of neuromodulation examine developments, opportunities and challenges field. proceedings Sixth Annual recapitulate progress applications neurotechnology, neurophysiology emerging techniques treatment range psychiatric neurological conditions including Parkinson's disease, essential tremor, Tourette syndrome, epilepsy, cognitive disorders, addiction. Each section...
Continuous EEG (cEEG) is a non-invasive bedside tool used to detect causative or contributory conditions of the encephalopathic state. By continuously recording electrical brain activity, it provides insights into background patterns, seizures, and dynamic cerebral thereby aiding in management critically ill patients with acute injury. The term 'cyclic alternating pattern encephalopathy' (CAPE) was recently introduced describe changes activity observed on patients. CAPE characterized by...
Biomedical engineering (BME) is one of the fastest-growing fields worldwide. BME professionals are extensively employed in health technology and healthcare industries. Hence, their education must prepare them to face challenge a rapidly evolving technological environment. signals systems analysis essential undergraduate education. Unfortunately, students often underestimate importance courses as they do not perceive these courses’ practical applications future professional practice. In this...
Introduction: The objective of this work was to predict preoperatively the maximum extent which direct stimulation therapy can propagate through an epileptic circuit for stabilizing refractory focal-onset epilepsy. A pre-surgical workflow is presented comprises a computationally intensive process calculating volume cortical activation (VOCA) surrounding cylindrical depth contacts virtually placed in white matter. employs function (AF) derived from cable modeling axon. AF extrapolated...
A two-step method for obtaining a volumetric estimation of COVID-19 related lesion from CT images is proposed. The first step consists in applying U-NET convolutional neural network to provide segmentation the lung-parenchyma. This architecture trained and validated using Thoracic Volume Pleural Effusion Segmentations Diseased Lungs Benchmarking Chest Processing Pipelines (PleThora) dataset, which publicly available. second an automatic algorithm based on probabilistic active contour (PACO)...
Abstract Parametric subtracted post‐ictal diffusion tensor imaging (pspiDTI) is a novel technique developed at our center to visualize transient, patient‐specific, ictal‐associated water abnormalities in hippocampal‐associated axonal tissue. PspiDTI can elucidate putative connectivity patterns, tracing ictal propagation following partial‐onset seizure without generalization secondarily. compares two DTI volumes acquired during the early period (<4 hr), and baseline inter‐ictal interval...
Biomedical engineering undergraduate students often underestimate the relevance of their courses as they hardly perceive practical application in future professional development, resulting a lack interest, motivation, and engagement. This issue may be addressed by implementing or "hands-on" learning experiences that allow to construct knowledge apply skills real-world scenarios. work proposes create supported blended spaces, including traditional classrooms for direct instruction, daily-life...
Quantifying the affected lung areas in a COVID-19 patient is important to assess disease progression and treatment response. It also helps with phenotype differentiation prognosis prediction. In clinical setting, this quantification performed manually by an expert radiologist, based on their experience knowledge, however it time-consuming task inter-observer variability, creating thus need develop automatic algorithms delimitate diseased areas. Convolutional neural networks (CNNs) have been...
Purpose: A semiautomatic two-step methodology is proposed to obtain a volumetric estimation of the COVID-19 related lesions on CT images.Methods: The first step consists lesion segmentation using non-supervised approach based probabilistic active contours, making it interesting because does not need large dataset train model. second uses CNN segment lung parenchyma and then whole-lung volume estimation. Finally, resultant masks are used compute percentage in lungs. validated publicly...