Publications by authors named "Sina Ghandian"
Article Synopsis
- Abnormal tau protein accumulation into neurofibrillary tangles (NFTs) is a key feature of Alzheimer's disease, and accurately detecting these tangles in tissue samples is important for understanding their relationship with various clinical factors.
- The study introduces a scalable, open-source deep-learning method that can efficiently quantify NFT burden in digital images of post-mortem human brain tissue, overcoming the limitations of manual analysis, like time consumption and variability.
- The trained segmentation model demonstrated strong performance in identifying NFTs at a high level of detail, correlating well with expert scores and significantly improving the speed and accuracy of analysis compared to traditional methods.
Background: Non-alcoholic fatty liver (NAFL) can progress to the severe subtype non-alcoholic steatohepatitis (NASH) and/or fibrosis, which are associated with increased morbidity, mortality, and healthcare costs. Current machine learning studies detect NASH; however, this study is unique in predicting the progression of NAFL patients to NASH or fibrosis.
Aim: To utilize clinical information from NAFL-diagnosed patients to predict the likelihood of progression to NASH or fibrosis.
Background: Acute heart failure (AHF) is associated with significant morbidity and mortality. Effective patient risk stratification is essential to guiding hospitalization decisions and the clinical management of AHF. Clinical decision support systems can be used to improve predictions of mortality made in emergency care settings for the purpose of AHF risk stratification.
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