Background: The World Health Organization (WHO) has recommended the development of a cleft-specific database for collecting and analyzing data on patients with cleft from birth to adulthood. However, such a database currently does not exist in Malaysia. The objective of this study was to develop a cleft lip and/or palate (CL/P) database software for Malaysia to streamline data collection and support comprehensive research to enhance outcomes of care.
Methods: The development of the database software involves several key stages, including determining the requirements, designing the software interface, implementing the system, conducting thorough testing, and completing comprehensive documentation. The database software was mainly developed internally within the research institution. The team involved in developing the clinical database includes cleft clinicians, software developers, software designers, members of the Cleft Lip and Palate Association Malaysia (CLAPAM), and experts in database development.
Results: An online and offline database software has been developed to store information on patients with CL/P in Malaysia. It is designed to be user-friendly, accommodating multiple specialties and capable of storing photographs, radiology, and three-dimensional files. Various methods have been implemented to ensure data security. Additionally, documentation including video tutorials, consent forms, and hard copy versions has been developed to complement the database.
Conclusion: A specialized cleft-specific database software has been successfully developed for use in Malaysia to improve data management and support CL/P patient care.
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http://dx.doi.org/10.1186/s12903-025-05583-5 | DOI Listing |
Eur J Haematol
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Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
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Actas Esp Psiquiatr
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Department of Internal Medicine, Dermatology and Psychiatry and Instituto Universitario de Neurociencia (IUNE), Universidad de La Laguna, 38071 San Cristobal de La Laguna, Spain.
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March 2025
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This study presents a novel deep learning approach for surface electromyography (sEMG) gesture recognition using stacked autoencoder neural network (SAE)s. The method leverages hierarchical representation learning to extract meaningful features from raw sEMG signals, enhancing the precision and robustness of gesture classification.•Feature Extraction and Classification MODWT Decomposition: The sEMG signals were decomposed using the MODWT DECOMPOSITION(Maximal Overlap Discrete Wavelet Transform) to capture various frequency components.
View Article and Find Full Text PDFFront Oncol
February 2025
Department of Genetics, School of Life Sciences, Bengbu Medical University, Bengbu, China.
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