Objectives: To define indices of completeness and accuracy of clinical information in the skin biopsy requisition form (RF) and correlate them with health care delivery outcomes and pathology service utilization.
Methods: RFs in our pathology information system were reviewed and assessed for the presence of 10 clinical elements considered critical for dermatopathologic diagnosis. Accuracy was determined by reviewing corresponding clinical notes.
Results: In total, 249 RFs were reviewed. In inflammatory dermatoses, provision of a clinical impression, provision of more than two elements, and achieving more than 75% accuracy were associated with improved outcomes and decreased utilization. For all nonlymphoproliferative cases, higher quality clinical information was associated with decreased turnaround time (P < .001). More clinical information was associated with increased utilization and turnaround time (P = .0235) for lymphoproliferative cases and higher resampling rates for melanocytic lesions (P = .0066).
Conclusions: In inflammatory dermatoses, providing high-quality clinical information on the RF promotes optimal histopathologic diagnostic performance and appropriate pathology service utilization.
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http://dx.doi.org/10.1093/ajcp/aqw186 | DOI Listing |
Sensors (Basel)
December 2024
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Transformer is a powerful model widely used in artificial intelligence applications. It contains complex structures and has extremely high computational requirements that are not suitable for embedded intelligent sensors with limited computational resources. The binary quantization technology takes up less memory space and has a faster calculation speed; however, it is seldom studied for the lightweight transformer.
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December 2024
Shanghai Research Institute of Microelectronics, Peking University, Shanghai 201203, China.
Despite the accuracy and robustness attained in the field of object tracking, algorithms based on Siamese neural networks often over-rely on information from the initial frame, neglecting necessary updates to the template; furthermore, in prolonged tracking situations, such methodologies encounter challenges in efficiently addressing issues such as complete occlusion or instances where the target exits the frame. To tackle these issues, this study enhances the SiamRPN algorithm by integrating the convolutional block attention module (CBAM), which enhances spatial channel attention. Additionally, it integrates the kernelized correlation filters (KCFs) for enhanced feature template representation.
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December 2024
School of Engineering, Technology and Design, Canterbury Christ Church University, Canterbury CT1 1QU, UK.
The rapid integration of Internet of Things (IoT) systems in various sectors has escalated security risks due to sophisticated multilayer attacks that compromise multiple security layers and lead to significant data loss, personal information theft, financial losses etc. Existing research on multilayer IoT attacks exhibits gaps in real-world applicability, due to reliance on outdated datasets with a limited focus on adaptive, dynamic approaches to address multilayer vulnerabilities. Additionally, the complete reliance on automated processes without integrating human expertise in feature selection and weighting processes may affect the reliability of detection models.
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December 2024
Instituto de Estudios de Género, Universidad Carlos III de Madrid, Calle Madrid, 126, 28903 Getafe, Spain.
Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense, there are applications related to the safety and well-being of people (sexual assaults, gender-based violence, children and elderly abuse, mental health, etc.) that require even more improvements.
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December 2024
School of Mechanical Engineering and Automation, Foshan University, Foshan 528225, China.
Inspection robots, which improve hazard identification and enhance safety management, play a vital role in the examination of high-risk environments in many fields, such as power distribution, petrochemical, and new energy battery factories. Currently, the position precision of the robots is a major barrier to their broad application. Exact kinematic model and control system of the robots is required to improve their location accuracy during movement on the unstructured surfaces.
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