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Guidance on selecting and evaluating AI auto-segmentation systems in clinical radiotherapy: insights from a six-vendor analysis.
Phys Eng Sci Med
January 2025
School of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia.
Artificial Intelligence (AI) based auto-segmentation has demonstrated numerous benefits to clinical radiotherapy workflows. However, the rapidly changing regulatory, research, and market environment presents challenges around selecting and evaluating the most suitable solution. To support the clinical adoption of AI auto-segmentation systems, Selection Criteria recommendations were developed to enable a holistic evaluation of vendors, considering not only raw performance but associated risks uniquely related to the clinical deployment of AI.
View Article and Find Full Text PDFDyadic working alliance, therapist insight skills, and client outcomes: Longitudinal mediation analyses.
Psychotherapy (Chic)
January 2025
Department of Psychology, Arizona State University.
A number of studies have shown an association between therapist skills (particularly insight skills) and the working alliance, but few studies have examined the directionality of this relationship. In addition, studies have used either the client or therapist report of the working alliance rather than a perspective. Thus, we examined whether (a) dyadic insight skills are indirectly related to client outcome through the working alliance and (b) the dyadic working alliance is indirectly related to client outcome through insight skills.
View Article and Find Full Text PDFExploring the potential of compound-protein complex structure-free models in virtual screening using BlendNet.
Brief Bioinform
November 2024
Department of Computer Science, Yonsei University, Yonsei-ro 50, Seodaemun-gu, 03722, Seoul, Republic of Korea.
Identifying new compounds that interact with a target is a crucial time-limiting step in the initial phases of drug discovery. Compound-protein complex structure-based affinity prediction models can expedite this process; however, their dependence on high-quality three-dimensional (3D) complex structures limits their practical application. Prediction models that do not require 3D complex structures for binding-affinity estimation offer a theoretically attractive alternative; however, accurately predicting affinity without interaction information presents significant challenges.
View Article and Find Full Text PDFCognitive stimulation to improve cognitive functioning in people with dementia.
Cochrane Database Syst Rev
January 2023
Research Department of Clinical, Educational and Health Psychology, University College London, London, UK.
Background: Cognitive stimulation (CS) is an intervention for people with dementia offering a range of enjoyable activities providing general stimulation for thinking, concentration and memory, usually in a social setting, such as a small group. CS is distinguished from other approaches such as cognitive training and cognitive rehabilitation by its broad focus and social elements, aiming to improve domains such as quality of life (QoL) and mood as well as cognitive function. Recommended in various guidelines and widely implemented internationally, questions remain regarding different modes of delivery and the clinical significance of any benefits.
View Article and Find Full Text PDFEnhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.
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Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Cancer is a complex disease driven by mutations in the genes that play critical roles in cellular processes. The identification of cancer driver genes is crucial for understanding tumorigenesis, developing targeted therapies and identifying rational drug targets. Experimental identification and validation of cancer driver genes are time-consuming and costly.
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