Mahdieh Shabanian

A two-stage deep learning pipeline for automated detection and localization of endotracheal tubes on pediatric chest radiographs: a pilot study

Hailong Li, Bohan Zhang, Elanchezhian Somasundaram, Mahdieh Shabanian et autres

BACKGROUND: Endotracheal tubes (ETTs) are critical life-support devices for mechanically ventilated pediatric patients, yet automated ETT assessment on pediatric chest radiographs (CXRs) remains limited. OBJECTIVE: To develop and evaluate a two-stage deep learning pipeline for automated detection and localization of ETTs on …

us (code pays fourni par la source)

Enhancing validation of case-control omics signatures through “minimalist” single-subject analysis (N-of-1 trials): proof of concept in sepsis

Liam S. Wilson, Nima Pouladi, Rachel F Nelson, Elizabeth A. Middleton et autres

OBJECTIVE: To evaluate if a single-subject study (S3) design, utilizing paired transcriptome samples from the same patient (eg, "sepsis" vs "recovered"), can replicate transcriptomic signatures from small case-control studies, addressing challenges in patient accrual for rare or sub-stratified diseases. METHODS: We generated …

us (code pays fourni par la source)

Paired-Sample and Pathway-Anchored MLOps Framework for Robust Transcriptomic Machine Learning in Small Cohorts: Model Classification Study

Mahdieh Shabanian, Nima Pouladi, Liam S. Wilson, Mattia Prosperi et autres

Background: Approximately 90% of the 65,000 human diseases are infrequent, collectively affecting ~400 million people, substantially limiting cohort accrual. This low prevalence constrains the development of robust transcriptome-based machine learning (ML) classifiers. Standard data-driven classifiers typically require cohorts of more than 100 …

us (code pays fourni par la source)

Paired-Sample and Pathway-Anchored MLOps Framework for Robust Transcriptomic Machine Learning in Small Cohorts (Preprint)

Yves A. Lussier, Mahdieh Shabanian, Nima Pouladi, Liam S. Wilson et autres

BACKGROUND Ninety percent of the 65,000 human diseases are infrequent, collectively affecting ~ 400 million people, substantially limiting cohort accrual. This low prevalence constrains the development of robust transcriptome-based machine learning (ML) classifiers. Standard data-driven classifiers typically require cohorts of over 100 …

Paired-Sample and Pathway-Anchored MLOps Framework for Robust Transcriptomic Machine Learning in Small Cohorts

Mahdieh Shabanian, Nima Pouladi, Liam S. Wilson, Mattia Prosperi et autres

Abstract Background Ninety percent of the 65,000 human diseases are infrequent, collectively affecting ∼ 400 million people, substantially limiting cohort accrual. This low prevalence constrains the development of robust transcriptome-based machine learning (ML) classifiers. Standard data-driven classifiers typically require cohorts of over …

us (code pays fourni par la source)

Enabling Transcriptome Classification in Micro-cohorts with Pathway-Anchoring and Single-Subject Studies

Mahdieh Shabanian, Nima Pouladi, Liam S. Wilson, Mattia Prosperi et autres

us (code pays fourni par la source)

Liver fibrosis classification on trichrome histology slides using weakly supervised learning in children and young adults

Mahdieh Shabanian, Zachary L. Taylor, Christopher Woods, Anas Bernieh et autres

Background: Traditional liver fibrosis staging via percutaneous biopsy suffers from sampling bias and variable inter-pathologist agreement, highlighting the need for more objective techniques. Deep learning models for disease staging from medical images have shown potential to decrease diagnostic variability, with recent weakly …

us (code pays fourni par la source)

Infant brain age classification: 2D CNN outperforms 3D CNN in small dataset

Mahdieh Shabanian, Markus T. Wenzel, John P. DeVincenzo

Determining if the brain is developing normally is a key component of pediatric neuroradiology and neurology. Brain magnetic resonance imaging (MRI) of infants demonstrates a specific pattern of development beyond simply myelination. While radiologists have used myelination patterns, brain morphology and size …

us, de (code pays fourni par la source)

3D deep neural network to automatically identify TSC structural brain pathology based on MRI

Mahdieh Shabanian, Abdullah-Al-Zubaer Imran, Adeel Ahmed Siddiqui, Robert Lowell Davis et autres

During childhood, neurological involvement in tuberous sclerosis complex (TSC) is a leading cause of death. Neurological involvement, including epilepsy, can cause significant long-term sequelae in children. Brain involvement in TSC can be detected by magnetic resonance imaging (MRI). Still, neuroimaging analysis is …

us (code pays fourni par la source)

Infant Brain Age Classification: 2D CNN Outperforms 3D CNN in Small Dataset

Mahdieh Shabanian, Markus T. Wenzel, John P. DeVincenzo

Determining if the brain is developing normally is a key component of pediatric neuroradiology and neurology. Brain magnetic resonance imaging (MRI) of infants demonstrates a specific pattern of development beyond simply myelination. While radiologists have used myelination patterns, brain morphology and size …

Infant Brain Age Classification: 2D CNN Outperforms 3D CNN in Small\n Dataset

Mahdieh Shabanian, M Wenzel, John P. DeVincenzo

Determining if the brain is developing normally is a key component of\npediatric neuroradiology and neurology. Brain magnetic resonance imaging (MRI)\nof infants demonstrates a specific pattern of development beyond simply\nmyelination. While radiologists have used myelination patterns, brain\nmorphology and size characteristics to determine age-adequate …

Developing Deep-Learning Methods for Diagnosis and Prognosis of Pediatric Progressive Diseases Using Modern Imaging Techniques

Mahdieh Shabanian

Purpose and Rationale. Central nervous system manifestations form a significant burden of disease in young children. There have been efforts to correlate the neurological disease state in tuberous sclerosis complex (TSC) neurological disease state with imaging findings is a standard part of …