According to the feelings-as-information account, a person's mood state signals to him or her the valence of the current environment (N. Schwarz & G. Clore, 1983). However, the ways in which the environment automatically influences mood in the first place remain to be explored. The authors propose that one mechanism by which the environment influences affect is automatic evaluation, the nonconscious evaluation of environmental stimuli as good or bad. A first experiment demonstrated that repeated brief exposure to positive or negative stimuli (which leads to automatic evaluation) induces a corresponding mood in participants. In 3 additional studies, the authors showed that automatic evaluation affects information processing style. Experiment 4 showed that participants' mood mediates the effect of valenced brief primes on information processing.
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http://dx.doi.org/10.1037/0096-3445.135.1.70 | DOI Listing |
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 PDFInsights Imaging
January 2025
Medical Research Department, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, P. R. China.
Objective: To develop an automatic segmentation model to delineate the adnexal masses and construct a machine learning model to differentiate between low malignant risk and intermediate-high malignant risk of adnexal masses based on ovarian-adnexal reporting and data system (O-RADS).
Methods: A total of 663 ultrasound images of adnexal mass were collected and divided into two sets according to experienced radiologists: a low malignant risk set (n = 446) and an intermediate-high malignant risk set (n = 217). Deep learning segmentation models were trained and selected to automatically segment adnexal masses.
Neurourol Urodyn
January 2025
Department of Urology, Renmin Hospital of Wuhan University, Wuhan, China.
Objectives: To automatically identify and diagnose bladder outflow obstruction (BOO) and detrusor underactivity (DUA) in male patients with lower urinary tract symptoms through urodynamics exam.
Patients And Methods: We performed a retrospective review of 1949 male patients who underwent a urodynamic study at two institutions. Deep Convolutional Neural Networks scheme combined with a short-time Fourier transform algorithm was trained to perform an accurate diagnosis of BOO and DUA, utilizing five-channel urodynamic data (consisting of uroflowmetry, urine volume, intravesical pressure, abdominal pressure, and detrusor pressure).
Eur J Radiol Open
June 2025
Department of Nuclear Medicine, Medical Faculty and University Hospital Duesseldorf, Heinrich Heine University Duesseldorf, Düsseldorf 40225, Germany.
Objective: [F]FDG imaging is an integral part of patient management in CAR-T-cell therapy for recurrent or therapy-refractory DLBCL. The calculation methods of predictive power of specific imaging parameters still remains elusive. With this retrospective study, we sought to evaluate the predictive power of the baseline metabolic parameters and tumor burden calculated with automated segmentation via different thresholding methods for early therapy failure and mortality risk in DLBCL patients.
View Article and Find Full Text PDFPhys Imaging Radiat Oncol
October 2024
Université Paris-Saclay, Gustave Roussy, Inserm, Molecular Radiotherapy and Therapeutic Innovation, U1030, 94800 Villejuif, France.
Background And Purpose: Deep-learning-based automatic segmentation is widely used in radiation oncology to delineate organs-at-risk. Dual-energy CT (DECT) allows the reconstruction of enhanced contrast images that could help with manual and auto-delineation. This paper presents a performance evaluation of a commercial auto-segmentation software on image series generated by a DECT.
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