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| http://dx.doi.org/10.1016/j.lana.2022.100421 | DOI Listing |
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Psychological and Behavioral Insights From Social Media Users: Natural Language Processing-Based Quantitative Study on Mental Well-Being.
JMIR Form Res
January 2025
Smith School of Business, Queen's University, Kingston, ON, Canada.
Background: Depression significantly impacts an individual's thoughts, emotions, behaviors, and moods; this prevalent mental health condition affects millions globally. Traditional approaches to detecting and treating depression rely on questionnaires and personal interviews, which can be time consuming and potentially inefficient. As social media has permanently shifted the pattern of our daily communications, social media postings can offer new perspectives in understanding mental illness in individuals because they provide an unbiased exploration of their language use and behavioral patterns.
View Article and Find Full Text PDFUtility of the radiological report function of an artificial intelligence system in interpreting CBCT images: a technical report.
Dentomaxillofac Radiol
January 2025
Division of Oral Radiology, Faculdade São Leopoldo Mandic.
Objectives: The aim of this technical report was to assess whether the "Radiological Report" tool within the Artificial Intelligence (AI) software Diagnocat can achieve a satisfactory level of performance comparable to that of experienced dentomaxillofacial radiologists in interpreting cone-beam CT scans.
Methods: Ten cone-beam CT scans were carefully selected and analyzed using the AI tool, and they were also evaluated by two dentomaxillofacial radiologists. Observations related to tooth numeration, alterations in dental crowns, roots, and periodontal tissues were documented and subsequently compared to the AI findings.
Key Concepts in Machine Learning and Clinical Applications in the Cardiac Intensive Care Unit.
Curr Cardiol Rep
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Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Purpose Of Review: Artificial Intelligence (AI) technology will significantly alter critical care cardiology, from our understanding of diseases to the way in which we communicate with patients and colleagues. We summarize the potential applications of AI in the cardiac intensive care unit (CICU) by reviewing current evidence, future developments and possible challenges.
Recent Findings: Machine Learning (ML) methods have been leveraged to improve interpretation and discover novel uses for diagnostic tests such as the ECG and echocardiograms.
The blind spots on chest computed tomography: what do we miss.
J Thorac Dis
December 2024
Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Chest computed tomography (CT) is the most frequently performed imaging examination worldwide. Compared with chest radiography, chest CT greatly improves the detection rate and diagnostic accuracy of chest lesions because of the absence of overlapping structures and is the best imaging technique for the observation of chest lesions. However, there are still frequently missed diagnoses during the interpretation process, especially in certain areas or "blind spots", which may possibly be overlooked by radiologists.
View Article and Find Full Text PDFAn Integrative Approach to Precision Pre-implantation Genetic Diagnosis by Investigating Single-cell Sequencing, Polygenic Risk Assessment, Artificial Intelligence-guided Embryo Selection and Genome Editing in Embryos with COL4A1 c.1537G>A Mutation.
J Hum Reprod Sci
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Department of Genomics, Sandor Speciality Diagnostics, Hyderabad, Telangana, India.
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