This article evaluates the performance of a protocol to monitor riparian forests in western Oregon, United States based on the quality of the data obtained from a field survey. Precision is the criteria used to determine the quality of 19 field and 6 derived metrics. The derived metrics were calculated from the field data. The survey consisted of 110 riparian sites on public and private lands that were sampled during the summers of 1996 and 1997. In order to calculate metric precision, some of the field plots were re-measured. Metric precision was defined in terms of the coefficient of variability (CV) and standard deviation and then compared with a pre-defined data quality objective (DQO). A metric was considered precise if the CV met or exceeded the DQO. The geomorphology metrics were not precise while the forest stand inventory metrics and forest cover metrics, with some exceptions, were precise. The precision for many of the field and derived metrics compared favorably with the level of precision for similar metrics reported in the literature. Recommendations are made to improve the precision for some metrics and they include changing the way precision is calculated, re-defining the field protocol, or improving field training.
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http://dx.doi.org/10.1023/a:1014259902449 | DOI Listing |
Med Biol Eng Comput
January 2025
Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, (C.G.), India.
This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. A multi-dataset approach was employed, including data from the UK Biobank, MICCAI Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, and clinical datasets of congenital heart disease. Preprocessing steps involved segmentation, intensity normalization, and mesh generation, while the reconstruction was performed using a blend of statistical shape modeling (SSM), graph convolutional networks (GCNs), and progressive GANs.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Guangzhou Cadre and Talent Health Management Center, Guangzhou, China.
Background: Large language models have shown remarkable efficacy in various medical research and clinical applications. However, their skills in medical image recognition and subsequent report generation or question answering (QA) remain limited.
Objective: We aim to finetune a multimodal, transformer-based model for generating medical reports from slit lamp images and develop a QA system using Llama2.
Sci Rep
January 2025
Graduate Program in Electrical and Computer Engineering, Universidade Tecnológica Federal do Paraná (UTFPR), Curitiba, 80230-901, Brazil.
Modeling the Digital Twin (DT) is an important resource for accurately representing the physical entity, enabling it to deliver functional services, meet application requirements, and address the disturbances between the physical and digital realms. This article introduces the Log Mean Kinematics Difference Synchronization (SyncLMKD) to measure the kinematic variations distributed among Digital Twin elements to ensure symmetric values relative to a reference. The proposed method employs abductive reasoning and draws inspiration from the Log Mean Temperature Difference (LMTD).
View Article and Find Full Text PDFDue to the low contrast of abdominal CT (Computer Tomography) images and the similar color and shape of the liver to other organs such as the spleen, stomach, and kidneys, liver segmentation presents significant challenges. Additionally, 2D CT images obtained from different angles (such as sagittal, coronal, and transverse planes) increase the diversity of liver morphology and the complexity of segmentation. To address these issues, this paper proposes a Detail Enhanced Convolution (DE Conv) to improve liver feature learning and thereby enhance liver segmentation performance.
View Article and Find Full Text PDFInt J Obstet Anesth
December 2024
Department of Biomedical Engineering and the School of Brain Sciences and Cognition, Ben Gurion University of the Negev, Beer Sheva, Israel.
Background: Correct identification of the epidural space requires extensive training for technical proficiency. This study explores a novel bimanual haptic simulator designed for the precise insertion of an epidural needle based on loss-of-resistance (LOR) detection, providing realistic dual-hand force feedback.
Methods: The simulator, equipped with two haptic devices connected to a Tuohy needle and an LOR syringe, was designed to simulate the tissues' resistive forces felt by the user during the procedure, offer anatomical variability and record detailed performance metrics for personalized feedback.
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