The aim of the presented studies was to investigate whether classifications of neglect patients into perceptual (i.e. identifying a patient as suffering from mainly attentional/space representation deficits) and premotor (judging the main impairment to be related towards actions into contralesional space) categories are consistent across similar Landmark techniques that have, in the past, been designed to tease these potentially overlapping aspects of hemispatial neglect apart. Thirteen patients with hemispatial neglect were tested both with the Landmark Test, adapted from Milner et al. (1992; 1993) in which they had to manually point to the half of a centrally pre-bisected line that, to them, appeared shorter and the motor version of the Bisiach Landmark Test (Bisiach et al., 1998) in which, rather than just judging a centrally prebisected line, they had to judge asymmetrically bisected lines as well. The specific question was whether these two tasks, which are very similar, would categorise the same set of patients in the same way. Most patients could be classified into either the premotor or perceptual category in each task, but no consistent categorisation emerged across the two tests. Just three out of the thirteen patients were consistently classified across both tests. Despite the apparent similarity of the two tests the Milner Landmark Test proved to be much more sensitive to identifying even a slight perceptual bias and seems therefore the test of choice if identification of perceptual bias is the major interest.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/s0010-9452(08)70162-x | DOI Listing |
J Clin Virol
January 2025
Center for Immunotherapy and Precision Immuno-Oncology, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA; Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA. Electronic address:
Background: Plasma cell-free Human Papillomavirus DNA (cfHPVDNA) is a biomarker for oropharyngeal carcinoma. Existing diagnostics may be limited by inadequate sensitivity or high cost/complexity for longitudinal monitoring.
Objectives: We hypothesized that sensitive and specific plasma cfHPVDNA detection may be achieved via a highly-multiplex qPCR method.
Oral Maxillofac Surg
January 2025
Department of Oral and Maxillofacial Surgery, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany.
Purpose: This study aimed to clarify the applicability of smartphone-based three-dimensional (3D) surface imaging for clinical use in oral and maxillofacial surgery, comparing two smartphone-based approaches to the gold standard.
Methods: Facial surface models (SMs) were generated for 30 volunteers (15 men, 15 women) using the Vectra M5 (Canfield Scientific, USA), the TrueDepth camera of the iPhone 14 Pro (Apple Inc., USA), and the iPhone 14 Pro with photogrammetry.
J Pediatr Orthop B
January 2025
Orthopedic and Traumatology Department, IRCCS Istituto Giannina Gaslini.
Pediatricians and general practitioners are involved in the newborn screening for developmental dysplasia of the hip. They often rely on the quality of the ultrasound (US) examination to make diagnostic and therapeutic decisions. Therefore, the professional must be able to assess its quality.
View Article and Find Full Text PDFBiomed Eng Lett
January 2025
Department of Orthopedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505 South Korea.
Unlabelled: Accurate assessment of shoulder range of motion (ROM) is crucial for evaluating patient progress. Traditional manual goniometry often lacks precision and is subject to inter-observer variability, especially in measuring shoulder internal rotation (IR). This study introduces an artificial intelligence (AI)-based approach that uses clinical photography to improve the accuracy of ROM quantification.
View Article and Find Full Text PDFDentomaxillofac Radiol
January 2025
Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego, La Jolla, California 92093, USA.
Objectives: To identify landmarks in ultrasound periodontal images and automate the image-based measurements of gingival recession (iGR), gingival height (iGH), and alveolar bone level (iABL) using machine learning.
Methods: We imaged 184 teeth from 29 human subjects. The dataset included 1580 frames for training and validating the U-Net CNN machine learning model, and 250 frames from new teeth that were not used in training for testing the generalization performance.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!