Publications by authors named "E J Delp"

Objective: This pilot cross-sectional study explored differences in dietary intake and eating behaviors between healthy adults and a group of adults taking insulin to manage diabetes.

Methods: A characteristic questionnaire and up to four Automated Self-Administered 24-Hour dietary recalls were collected from 152 adults aged 18-65 years (96 healthy and 56 adults taking insulin) from Indiana and across the U.S.

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Purpose: To obtain high-resolution velocity fields of cerebrospinal fluid (CSF) and cerebral blood flow by applying a physics-guided neural network (div-mDCSRN-Flow) to 4D flow MRI.

Methods: The div-mDCSRN-Flow network was developed to improve spatial resolution and denoise 4D flow MRI. The network was trained with patches of paired high-resolution and low-resolution synthetic 4D flow MRI data derived from computational fluid dynamic simulations of CSF flow within the cerebral ventricles of five healthy cases and five Alzheimer's disease cases.

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Visual detection of stromata (brown-black, elevated fungal fruiting bodies) is the primary method for quantifying tar spot early in the season because these structures are definitive signs of the disease and essential for effective disease monitoring and management. Here, we present the Stromata Contour Detection Algorithm version 2 (SCDA v2), which addresses the limitations of the previously developed SCDA version 1 (SCDA v1), without the need to empirically search for optimal decision-making input parameters (DMIPs) while achieving higher and consistent accuracy in tar spot stromata detection. SCDA v2 operates in two components: (i) SCDA v1 producing tar spot-like region proposals for a given input corn leaf Red-Green-Blue (RGB) image and (ii) a pretrained convolutional neural network (CNN) classifier identifying true tar spot stromata from the region proposals.

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Article Synopsis
  • A study was conducted to evaluate the accuracy of four technology-assisted dietary assessment methods in estimating energy and nutrient intake during controlled meals across breakfast, lunch, and dinner.
  • 152 participants completed a 24-hour dietary recall after consuming meals with weights recorded, comparing results from different assessment tools, including ASA24 and Intake24.
  • Findings showed that Intake24, ASA24, and mFR-TA produced more accurate energy estimates than IA-24HR, which had a significant margin of error, indicating variability in the reliability of these assessment methods.
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The first step in any dietary monitoring system is the automatic detection of eating episodes. To detect eating episodes, either sensor data or images can be used, and either method can result in false-positive detection. This study aims to reduce the number of false positives in the detection of eating episodes by a wearable sensor, Automatic Ingestion Monitor v2 (AIM-2).

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