E. Somasundaram

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

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 ET...

Automated fetal magnetic resonance imaging lung segmentation for rapid lung volume estimation across diverse pulmonary hypoplasia phenotypes.

BACKGROUND Fetal magnetic resonance imaging (MRI)-derived lung volume measurements are used for prenatal risk stratification in conditions associated with pulmonary hypoplasia, but manual segmentation is time-intensive. OBJECTIVE To evaluate whether automated fetal MRI lung segmentation enables rapid and reliable vol...

Clinical Implementation and Validation of Automated Slice-to-Volume Reconstruction for Fetal MRI Using a Vendor-Neutral MONAI-Based Imaging Informatics Pipeline.

In this single-center, single-vendor cohort, the pipeline returned radiologist-acceptable reconstructions within a clinically practical timeframe and Automated fetal MRI SVR was operationally feasible using a DICOM-based, vendor-neutral deployment architecture integrated with routine PACS workflow.

Evaluating Clinical NLP Services for Chest Radiograph Report Labeling: A Comparative Study on an Independent Pediatric Dataset.