Tumor volume features predict survival outcomes for patients diagnosed with diffuse intrinsic pontine glioma.

Background: Diffuse intrinsic pontine glioma (DIPG) is a fatal childhood central nervous system tumor. Diagnosis and monitoring of tumor response to therapy is based on magnetic resonance imaging (MRI). MRI-based analyses of tumor volume and appearance may aid in the prediction of patient overall survival (OS).

Comparative analysis of personal protective equipment nonadherence detection: computer vision versus human observers.

Objectives: Human monitoring of personal protective equipment (PPE) adherence among healthcare providers has several limitations, including the need for additional personnel during staff shortages and decreased vigilance during prolonged tasks. To address these challenges, we developed an automated computer vision system for monitoring PPE adherence in healthcare settings. We assessed the system performance against human observers detecting nonadherence in a video surveillance experiment.

Accuracy of Cranial Shape Measurements Using Smartphones: A Prospective Study.

Objective: Prospectively validate the accuracy of smartphone-based digital cranial measurements for the diagnosis and treatment of deformational plagiocephaly and/or brachycephaly (DPB), compared with calipers used in the standard of care.

Design/methods: Bird's-eye-view head photos were captured via smartphone, and their heads were measured with hand calipers by an expert user. CI/CVAI/CVA were calculated from photos and caliper measurements, and from 3D photogrammetry of the head as ground truth.

UNITY: A low-field magnetic resonance neuroimaging initiative to characterize neurodevelopment in low and middle-income settings.

Measures of physical growth, such as weight and height have long been the predominant outcomes for monitoring child health and evaluating interventional outcomes in public health studies, including those that may impact neurodevelopment. While physical growth generally reflects overall health and nutritional status, it lacks sensitivity and specificity to brain growth and developing cognitive skills and abilities. Psychometric tools, e.

Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: A two-center study.

Background: Diffuse midline gliomas (DMG) are aggressive pediatric brain tumors that are diagnosed and monitored through MRI. We developed an automatic pipeline to segment subregions of DMG and select radiomic features that predict patient overall survival (OS).

Methods: We acquired diagnostic and post-radiation therapy (RT) multisequence MRI (T1, T1ce, T2, and T2 FLAIR) and manual segmentations from 2 centers: 53 from 1 center formed the internal cohort and 16 from the other center formed the external cohort.