Machine learning based analysis of stroke lesions on mouse tissue sections

Abstract

An unbiased, automated and reliable method for analysis of brain lesions in tissue after ischemic stroke is missing. Manual infarct volumetry or by threshold-based semi-automated approaches is laborious, and biased to human error or biased by many false -positive and -negative data, respectively. Thereby, we developed a novel machine learning, atlas-based method for fully automated stroke analysis in mouse brain slices stained with 2% Triphenyltetrazolium-chloride (2% TTC), named “StrokeAnalyst”, which runs on a user-friendly graphical interface. StrokeAnalyst registers subject images on a common spatial domain (a novel mouse TTC- brain atlas of 80 average mathematical images), calculates pixel-based, tissue-intensity statistics (z-scores), applies outlier-detection and machine learning (Random-Forest) models to increase accuracy of lesion detection, and produces volumetry data and detailed neuroanatomical information per lesion. We validated StrokeAnalyst in two separate experimental sets using the filament stroke model. StrokeAnalyst detects stroke lesions in a rater-independent and reproducible way, correctly detects hemispheric volumes even in presence of post-stroke edema and significantly minimizes false-positive errors compared to threshold-based approaches (false-positive rate 1.2–2.3%, p < 0.05). It can process scanner-acquired, and even smartphone-captured or pdf-retrieved images. Overall, StrokeAnalyst surpasses all previous TTC-volumetry approaches and increases quality, reproducibility and reliability of stroke detection in relevant preclinical models.

Prediction of in‐hospital hypokalemia using machine learning and first hospitalization day records in patients with traumatic brain injury

“…Like other studies combing machine learning and disease models, 27,55 it is hoped the findings of this study would be valuable for…”

“…Like other studies combing machine learning and disease models, 27,55 it is hoped the findings of this study would be valuable for clinical practice and to improve patient outcomes. A key point making future translational research promising is that the optimal algorithms and predictive models showed good and stable performance in other external datasets.…”

Investigating the Influence of Heavy Metals and Environmental Factors on Metabolic Syndrome Risk Based on Nutrient Intake: Machine Learning Analysis of Data from the Eighth Korea National Health and Nutrition Examination Survey (KNHANES)

“…Recognizing the pitfalls of missing data, we employed advanced imputation techniques grounded in probabilistic frameworks to ensure coherent value replacement [ 24 ]. Outliers, which can jeopardize model accuracy, were identified and rectified using robust statistical methodologies such as the IQR method and Z-score method [ 25 , 26 , 27 , 28 , 29 , 30 ]. Furthermore, given the sensitivity of machine learning algorithms to feature scales, normalization processes like Min–Max scaling and Z-score normalization were utilized, ensuring consistent interpretability and optimization across all variables [ 31 , 32 ].…”