Purpose To develop a deep learning algorithm to predict 2-year neurodevelopmental outcomes in neonates with hypoxic-ischemic encephalopathy using MRI and basic clinical data. Materials and Methods In this study, MRI data of term neonates with encephalopathy in the High-dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) trial (ClinicalTrials.gov: NCT02811263), who were enrolled from 17 institutions between January 25, 2017, and October 9, 2019, were retrospectively analyzed.
View Article and Find Full Text PDFPurpose: (I) Characterize the demographics and clinical features of patients with meniscal root tears (MRT); (II) analyze the morphology, extent, and grade of MRT on MRI; (III) evaluate associated abnormalities on imaging; and (IV) evaluate the associations between imaging findings, demographics, clinical features, and joint structural abnormalities.
Material And Methods: A search was performed to identify meniscal root tears. Age, sex, BMI, and pain were recorded.
Brain extraction, or skull-stripping, is an essential data preprocessing step for machine learning approaches to brain MRI analysis. Currently, there are limited extraction algorithms for the neonatal brain. We aim to adapt an established deep learning algorithm for the automatic segmentation of neonatal brains from MRI, trained on a large multi-institutional dataset for improved generalizability across image acquisition parameters.
View Article and Find Full Text PDFRationale And Objectives: In pediatric imaging, sedation is often necessary to obtain diagnostic quality imaging. We aim to quantify patient and imaging-specific factors associated with successful pediatric scans without anesthesia and to evaluate labor cost savings associated with our institutional Scan Without Anesthesia Program (SWAP).
Materials And Methods: Patients who participated in SWAP between 2019-2022 were identified.
Background Assessment of appropriate brain myelination on T1- and T2-weighted MRI scans is based on gestationally corrected age (GCA) and requires subjective visual inspection of the brain with knowledge of normal myelination milestones. Purpose To develop a convolutional neural network (CNN) capable of estimating neonatal and infant GCA based on brain myelination on MRI scans. Materials and methods In this retrospective study from one academic medical center, brain MRI scans of patients aged 0-25 months with reported normal myelination were consecutively collected between January 1995 and June 2019.
View Article and Find Full Text PDFIn this case report, dual-energy CT was critical in the diagnosis of acute mesenteric ischemia by differentiating normal contrast-enhanced bowel and hemorrhagic necrosis. Iodine map showed a segment of small bowel with minimal contrast enhancement, and virtual non-contrast imaging revealed hyperattenuating bowel. This finding changed management for the patient and prevented complications from impending bowel perforation.
View Article and Find Full Text PDFThe objective is to determine patients' utilization rate of radiology image viewing through an online patient portal and to understand its impact on radiologists. IRB approval was waived. In this two-part, multi-institutional study, patients' image viewing rate was retrospectively assessed, and radiologists were anonymously surveyed for the impact of patient imaging access on their workflow.
View Article and Find Full Text PDFBackground: Ascorbic acid is involved in collagen biosynthesis and upregulates alkaline phosphatase, potentially alleviating cell senescence and stimulating mesenchymal stem cell proliferation and differentiation into osteoblasts. We hypothesized locally delivered ascorbic acid and β-glycerophosphate act as a bone graft extender to increase the volume of new bone formed in a murine model of posterior lumbar fusion.
Methods: Collagen sponges were used as delivery vehicles.
Purpose: To determine the rate of cytologic and diagnostic adequacy and identify features associated with suboptimal tissue sampling in ultrasound-guided fine-needle aspiration (US-FNA) of suspected nodal disease in thyroid cancer patients.
Methods: A single-institution pathology database was queried for lymph node FNA reports in thyroid cancer patients from 2014 to 2019. Charts were reviewed for demographics, body mass index (BMI), prior thyroidectomy, cancer type, and subsequent surgery.
Background: 3D printed patient-specific anatomical models have been applied clinically to orthopaedic care for surgical planning and patient education. The estimated cost and print time per model for 3D printers have not yet been compared with clinically representative models across multiple printing technologies. This study investigates six commercially-available 3D printers: Prusa i3 MK3S, Formlabs Form 2, Formlabs Form 3, LulzBot TAZ 6, Stratasys F370, and Stratasys J750 Digital Anatomy.
View Article and Find Full Text PDFBackground: Fused deposition modeling 3D printing is used in medicine for diverse purposes such as creating patient-specific anatomical models and surgical instruments. For use in the sterile surgical field, it is necessary to understand the mechanical behavior of these prints across 3D printing materials and after autoclaving. It has been previously understood that steam sterilization weakens polylactic acid, however, annealing heat treatment of polylactic acid increases its crystallinity and mechanical strength.
View Article and Find Full Text PDFBackground: Modern low-cost 3D printing technologies offer the promise of access to surgical tools in resource scarce areas, however optimal designs for manufacturing have not yet been established. We explore how the optimization of 3D printing parameters when manufacturing polylactic acid filament based Army-Navy retractors vastly increases the strength of retractors, and investigate sources of variability in retractor strength, material cost, printing time, and parameter limitations.
Methods: Standard retractors were printed from various polylactic acid filament spools intra-manufacturer and inter-manufacturer to measure variability in retractor strength.