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Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing.
Parasit Vectors
January 2025
Faculty of Information Technology, Mutah University, Mutah, Jordan.
Background: Amebiasis represents a significant global health concern. This is especially evident in developing countries, where infections are more common. The primary diagnostic method in laboratories involves the microscopy of stool samples.
View Article and Find Full Text PDFEnhancing furcation involvement classification on panoramic radiographs with vision transformers.
BMC Oral Health
January 2025
Department of Periodontics, Affiliated Hospital of Medical School, Nanjing Stomatological Hospital, Research Institute of Stomatology, Nanjing University, Nanjing, China.
Background: The severity of furcation involvement (FI) directly affected tooth prognosis and influenced treatment approaches. However, assessing, diagnosing, and treating molars with FI was complicated by anatomical and morphological variations. Cone-beam computed tomography (CBCT) enhanced diagnostic accuracy for detecting FI and measuring furcation defects.
View Article and Find Full Text PDFFully automated segmentation and classification of renal tumors on CT scans via machine learning.
BMC Cancer
January 2025
Department of Urology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Background: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
Materials And Methods: The model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas.
CODE-ACCORD: A Corpus of building regulatory data for rule generation towards automatic compliance checking.
Sci Data
January 2025
Faculty of Computing, Engineering and Built Environment, Birmingham City University, Birmingham, B4 7XG, UK.
Automatic Compliance Checking (ACC) within the Architecture, Engineering, and Construction (AEC) sector necessitates automating the interpretation of building regulations to achieve its full potential. Converting textual rules into machine-readable formats is challenging due to the complexities of natural language and the scarcity of resources for advanced Machine Learning (ML). Addressing these challenges, we introduce CODE-ACCORD, a dataset of 862 sentences from the building regulations of England and Finland.
View Article and Find Full Text PDFAutomatic segmentation of the midfacial bone surface from ultrasound images using deep learning methods.
Int J Oral Maxillofac Surg
January 2025
Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China; National Center for Stomatology, Beijing, China; National Clinical Research Center for Oral Diseases, Beijing, China; National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China. Electronic address:
With developments in computer science and technology, great progress has been made in three-dimensional (3D) ultrasound. Recently, ultrasound-based 3D bone modelling has attracted much attention, and its accuracy has been studied for the femur, tibia, and spine. The use of ultrasound allows data for bone surface to be acquired non-invasively and without radiation.
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