Publications by authors named "V Koponen"
Article Synopsis
- Histological assessment of autoimmune hepatitis (AIH) is difficult, particularly with nonclassical features like bile-duct injury being understudied.
- Researchers developed an AI tool, called AI(H), to analyze liver biopsy slides from patients with AIH, using 123 pre-treatment biopsies for training.
- The AI models demonstrated high accuracy in detecting various features related to AIH, including tissue structures and immune cells, offering a more detailed and reproducible assessment compared to manual analysis.
We have developed an artificial intelligence (AI)-based digital pathology model for the evaluation of histologic features related to eosinophilic esophagitis (EoE). In this study, we evaluated the performance of our AI model in a cohort of pediatric and adult patients for histologic features included in the Eosinophilic Esophagitis Histologic Scoring System (EoEHSS). We collected a total of 203 esophageal biopsy samples from patients with mucosal eosinophilia of any degree (91 adult and 112 pediatric patients) and 10 normal controls from a prospectively maintained database.
View Article and Find Full Text PDFBackground: In an attempt to provide quantitative, reproducible, and standardized analyses in cases of eosinophilic esophagitis (EoE), we have developed an artificial intelligence (AI) digital pathology model for the evaluation of histologic features in the EoE/esophageal eosinophilia spectrum. Here, we describe the development and technical validation of this novel AI tool.
Methods: A total of 10 726 objects and 56.
An artificial intelligence (AI) algorithm for prostate cancer detection and grading was developed for clinical diagnostics on biopsies. The study cohort included 4221 scanned slides from 872 biopsy sessions at the HUS Helsinki University Hospital during 2016-2017 and a subcohort of 126 patients treated by robot-assisted radical prostatectomy (RALP) during 2016-2019. In the validation cohort (n = 391), the model detected cancer with a sensitivity of 98% and specificity of 98% (weighted kappa 0.
View Article and Find Full Text PDFThe effect of building frame and moisture damage on microbial indoor air quality was characterized in 17 wooden and 15 concrete or brick school buildings. Technical investigations to detect visible moisture and mold damage were performed according to a standardized protocol. Viable airborne microbes were determined by using a six-stage impactor (Andersen 10-800).
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