Background: Semantic errors result from the disruption of access either to semantics or to lexical representations. One way to determine the origins of these errors is to evaluate comprehension of words that elicit semantic errors in naming. We hypothesized that in acute stroke there are different brain regions where dysfunction results in semantic errors in both naming and comprehension versus those with semantic errors in oral naming alone.
Methods: A consecutive series of 196 patients with acute left hemispheric stroke who met inclusion criteria were evaluated with oral naming and spoken word/picture verification tasks and magnetic resonance imaging within 48 h of stroke onset. We evaluated the relationship between tissue dysfunction in 10 pre-specified Brodmann's areas (BA) and the production of coordinate semantic errors resulting from (1) semantic deficits or (2) lexical access deficits.
Results: Semantic errors arising from semantic deficits were most associated with tissue dysfunction/infarct of left BA 22. Semantic errors resulting from lexical access deficits were associated with hypoperfusion/infarct of left BA 37.
Conclusion: Our study shows that semantic errors arising from damage to distinct cognitive processes reflect dysfunction of different brain regions.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2659726 | PMC |
http://dx.doi.org/10.1016/j.cortex.2008.05.013 | DOI Listing |
Background: Manual analysis of histopathological images is often not only time-consuming and painstaking but also prone to error from subjective evaluation criteria and human error. To address these issues, we created a fully automated workflow to enumerate jejunal crypts in a microcolony survival assay to quantify gastrointestinal damage from radiation.
Methods And Materials: After abdominal irradiation of mice, jejuna were obtained and prepared on histopathologic slides, and crypts were counted manually by trained individuals.
JMIR Med Inform
December 2024
Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Background: Traditional rule-based natural language processing approaches in electronic health record systems are effective but are often time-consuming and prone to errors when handling unstructured data. This is primarily due to the substantial manual effort required to parse and extract information from diverse types of documentation. Recent advancements in large language model (LLM) technology have made it possible to automatically interpret medical context and support pathologic staging.
View Article and Find Full Text PDFOral Surg Oral Med Oral Pathol Oral Radiol
October 2024
Department of Translational Medicine, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India.
Objective: Artificial intelligence (AI) has been increasingly utilized in diagnosis of skeletal deformities, while its role in treatment planning and outcome prediction of jaw corrective surgeries with 3-dimensional (3D) imaging remains underexplored.
Methods: The comprehensive search was done in PubMed, Google scholar, Semantic scholar and Cochrane Library between January 2000 and May 2024. Inclusion criteria encompassed studies on AI applications in treatment planning and outcome prediction for jaw corrective surgeries using 3D imaging.
Quant Imaging Med Surg
December 2024
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Background: Stroke is one of the leading causes of disability and death worldwide. Ischemic stroke accounts for 75-90% of all stroke incidents. Assessing the size and location of the stroke lesion is crucial for treatment decisions, especially those related to urgent vascular reconstruction surgery.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!