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Evaluating the accuracy of impaired skin integrity in critically ill patients: Key characteristics and clinical implications.
Int J Nurs Knowl
January 2025
Graduate Program in Nursing, Federal University of Ceará, Fortaleza, Brazil.
Objective: To evaluate the accuracy of the defining characteristics of the nursing diagnosis Impaired skin integrity (00046) in patients admitted to intensive care units (ICUs).
Methods: A cross-sectional diagnostic accuracy study was conducted with 105 adult patients admitted to an ICU. A latent class model with random effects was used to test the sensitivity and specificity of the defining characteristics investigated.
Family Connections: The Impact of an Education Program for Carers of Individuals With Borderline Personality Disorder in Italian Mental Health Services.
Fam Process
March 2025
Unit of Psychiatry, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
Borderline personality disorder (BPD) has a strong impact not only on patients' lives but also on their families. The presence of an invalidating environment is one of the key factors in the etiology of BPD. This study evaluated the impact of the Family connections (FC) program on burden, grief, and other clinical variables in 202 caregivers and identified the profiles of participants who improved/deteriorated their levels of burden and grief.
View Article and Find Full Text PDFDeep learning system for the differential diagnosis of oral mucosal lesions through clinical photographic imaging.
J Dent Sci
January 2025
School of Dentistry, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Background/purpose: Oral mucosal lesions are associated with a variety of pathological conditions. Most deep-learning-based convolutional neural network (CNN) systems for computer-aided diagnosis of oral lesions have typically concentrated on determining limited aspects of differential diagnosis. This study aimed to develop a CNN-based diagnostic model capable of classifying clinical photographs of oral ulcerative and associated lesions into five different diagnoses, thereby assisting clinicians in making accurate differential diagnoses.
View Article and Find Full Text PDFEstablishment and evaluation of a deep learning-based tooth wear severity grading system using intraoral photographs.
J Dent Sci
January 2025
First Clinical Division, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, Beijing, China.
Background/purpose: Artificial intelligence (AI) can assist in medical diagnosis owing to its high accuracy and efficiency. This study aimed to develop a diagnostic system for automatically determining the degree of tooth wear (TW) using intraoral photographs with deep learning.
Materials And Methods: The study included 388 intraoral photographs.
Influence of operator experience on the complete-arch accuracy and time-based efficiency of three intraoral scanners.
J Dent Sci
January 2025
School of Dental Technology, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan.
Background/purpose: The performance of intraoral scanners (IOSs) relies on the operator's skills. However, whether operator experience influences IOS accuracy remains unclear. This study investigated the effect of operator experience on the trueness accuracy and time-based efficiency of IOSs.
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