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Effectiveness and cost-effectiveness of interventions to increase knowledge and awareness of attention deficit hyperactivity disorder: a systematic review.
Eur Child Adolesc Psychiatry
January 2025
Deakin Health Economics, School of Health and Social Development, Faculty of Health, Institute for Health Transformation, Deakin University, Geelong, VIC, Australia.
Various interventions, including caregiver education, psychoeducation, teacher and clinician training and behavioral management embedded with education, are available to enhance awareness and knowledge among caregivers, teachers, and clinicians. This review synthesizes evidence on the effectiveness and cost-effectiveness of interventions to increase ADHD awareness and knowledge for caregivers, clinicians, and teachers. Peer-reviewed literature was identified through the systematic searches of six databases: MEDLINE Complete, APA PsycInfo, CINAHL Complete, ERIC, Global Health and EconLit.
View Article and Find Full Text PDFEnhancing waste classification accuracy with Channel and Spatial Attention-Based Multiblock Convolutional Network.
Environ Monit Assess
January 2025
Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Shollinganallur, Chennai, India.
Municipal waste classification is significant for effective recycling and waste management processes that involve the classification of diverse municipal waste materials such as paper, glass, plastic, and organic matter using diverse techniques. Yet, this municipal waste classification process faces several challenges, such as high computational complexity, more time consumption, and high variability in the appearance of waste caused by variations in color, type, and degradation level, which makes an inaccurate waste classification process. To overcome these challenges, this research proposes a novel Channel and Spatial Attention-Based Multiblock Convolutional Network for accurately classifying municipal waste that utilizes a unique attention mechanism for enhancing feature learning and waste classification accuracy.
View Article and Find Full Text PDFA platform combining automatic segmentation and automatic measurement of the maxillary sinus and adjacent structures.
Clin Oral Investig
January 2025
State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China.
Objectives: To develop a platform including a deep convolutional neural network (DCNN) for automatic segmentation of the maxillary sinus (MS) and adjacent structures, and automatic algorithms for measuring 3-dimensional (3D) clinical parameters.
Materials And Methods: 175 CBCTs containing 242 MS were used as the training, validating and testing datasets at the ratio of 7:1:2. The datasets contained healthy MS and MS with mild (2-4 mm), moderate (4-10 mm) and severe (10- mm) mucosal thickening.
Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It.
Diagnostics (Basel)
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Aerospace Engineering Department and Interdisciplinary Research Center for Smart Mobility and Logistics, and Interdisciplinary Research Center Aviation and Space Exploration, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
Artificial intelligence (AI) has recently made unprecedented contributions in every walk of life, but it has not been able to work its way into diagnostic medicine and standard clinical practice yet. Although data scientists, researchers, and medical experts have been working in the direction of designing and developing computer aided diagnosis (CAD) tools to serve as assistants to doctors, their large-scale adoption and integration into the healthcare system still seems far-fetched. Diagnostic radiology is no exception.
View Article and Find Full Text PDFPerformance analysis of Leica Biosystems p16 monoclonal antibody in oropharyngeal squamous cell carcinoma.
Diagn Pathol
January 2025
Medical and Scientific Affairs, Leica Biosystems Richmond Inc. 5205 US, Highway 12, Richmond, IL, 60071, US.
Background: Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cause of cancer death globally, with newly diagnosed oropharyngeal squamous cell carcinoma (OPSCC) cases rising to 54,000 in the US alone in the year 2022. Recently, human papilloma virus (HPV) infection was more prevalent in OPSCC patients than the traditionally known carcinogens such as tobacco or alcohol. HPV 16 is the most common causative HPV strain, which is found in 5-10% of HNSCC patients.
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