Background: Automated digital morphology systems are utilized for blood cell morphological examination. The aim of this study is to evaluate the accuracy and efficacy of RBC morphological anomaly screening using the CellaVision DM96 (DM96) automated image analysis system.
Methods: The automated analysis of RBC shape, size, and chromasia abnormalities was conducted on the DM96 using 478 blood samples. A manual microscopic review was independently performed.
Results: The DM96 preclassified samples as poikilocytosis-positive for 98% of cases with schistocytosis or echinocytosis, 97% of elliptocytosis, and 92% or 65% of cases that were positive for teardrop cells or for target cells, respectively. The accuracy of the DM96 in the detection of RBC size and chromasia abnormalities of iron deficiency anemia cases was higher than direct microscopic observation.
Conclusions: Automated morphological analysis with the DM96 has potential utility in the morphological screening of RBC anomalies that are associated with disease.
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http://dx.doi.org/10.7754/clin.lab.2013.120912 | DOI Listing |
J Shoulder Elbow Surg
January 2025
Department of Orthopedics and Trauma, Peking University People's Hospital, Beijing 100044, China; Key Laboratory of Trauma and Neural Regeneration (Peking University), Ministry of Education, Beijing 100044, China; National Center for Trauma Medicine, Peking University People's Hospital, Beijing 100044, China. Electronic address:
Objective: The bare area is defined as a transverse region within the trochlear notch, serving as an optimal entry point for olecranon osteotomy due to the absence of articular cartilage coverage. However, there is limited research on the morphology and location of the bare area, and there is a lack of intuitive visual description. Thus, the purpose of this study is to delineate anatomical features of the bare area and visualize its morphology and refine the olecranon osteotomy approach.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing Institute of Agricultural Mechanization, Nanjing 210014, China.
To address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of , in this study, a machine vision-based intelligent harvesting device was designed according to its agronomic characteristics and morphological features. This device mainly comprised a frame, camera, truss-type robotic arm, flexible manipulator, and control system. The FES-YOLOv5s deep learning target detection model was used to accurately identify and locate .
View Article and Find Full Text PDFMaterials (Basel)
January 2025
Laboratory of Materials Technology, Department of Materials Engineering, Federal University of Campina Grande, Campina Grande 58400-850, Brazil.
Over the past 15 years, there has been a significant increase in the search for environmentally friendly energy sources, and transition-metal-based energy storage devices are leading the way in these new technologies. Supercapacitors are attractive in this regard due to their superior energy storage capabilities. Electrode materials, which are crucial components of supercapacitors, such as cobalt-oxide-based electrodes, have great qualities for achieving high supercapacitor performance.
View Article and Find Full Text PDFBiomedicines
January 2025
Embrapa Genetic Resources and Biotechnology, Laboratory of Nanobiotechnology (LNANO), Brasília 70770-917, DF, Brazil.
Machine learning is used to analyze images by training algorithms on data to recognize patterns and identify objects, with applications in various fields, such as medicine, security, and automation. Meanwhile, histological cross-sections, whether longitudinal or transverse, expose layers of tissues or tissue mimetics, which provide crucial information for microscopic analysis. : This study aimed to employ the Google platform "Teachable Machine" to apply artificial intelligence (AI) in the interpretation of histological cross-section images of hydrogel filaments.
View Article and Find Full Text PDFInt J Med Inform
January 2025
Department of Computer Science and Artificial Intelligence, University of Udine, 33100, Italy.
Background: Segmentation models for clinical data experience severe performance degradation when trained on a single client from one domain and distributed to other clients from different domain. Federated Learning (FL) provides a solution by enabling multi-party collaborative learning without compromising the confidentiality of clients' private data.
Methods: In this paper, we propose a cross-domain FL method for Weakly Supervised Semantic Segmentation (FL-W3S) of white blood cells in microscopic images.
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