: Accurate and rapid detection of COVID-19 remains critical for clinical management, especially in resource-limited settings. Current diagnostic methods face challenges in terms of speed and reliability, creating a need for complementary AI-based models that integrate diverse data sources. : This study aimed to develop and evaluate a multi-modal machine learning model that combines clinical biomarkers and chest X-ray images to enhance diagnostic accuracy and provide interpretable insights. : We used a dataset of 250 patients (180 COVID-19 positive and 70 negative cases) collected from clinical settings. Biomarkers such as CRP, ferritin, NLR, and albumin were included alongside chest X-ray images. Random Forest and Gradient Boosting models were used for biomarkers, and ResNet and VGG CNN architectures were applied to imaging data. A late-fusion strategy integrated predictions from these modalities. Stratified k-fold cross-validation ensured robust evaluation while preventing data leakage. Model performance was assessed using AUC-ROC, F1-score, Specificity, Negative Predictive Value (NPV), and Matthews Correlation Coefficient (MCC), with confidence intervals calculated via bootstrap resampling. : The Gradient Boosting + VGG fusion model achieved the highest performance, with an AUC-ROC of 0.94, F1-score of 0.93, Specificity of 93%, NPV of 96%, and MCC of 0.91. SHAP and LIME interpretability analyses identified CRP, ferritin, and specific lung regions as key contributors to predictions. : The proposed multi-modal approach significantly enhances diagnostic accuracy compared to single-modality models. Its interpretability aligns with clinical understanding, supporting its potential for real-world application.
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Sensors (Basel)
December 2024
CeMOS Research and Transfer Center, Mannheim University of Applied Sciences, 68163 Mannheim, Germany.
Advancements in Raman light sheet microscopy have provided a powerful, non-invasive, marker-free method for imaging complex 3D biological structures, such as cell cultures and spheroids. By combining 3D tomograms made by Rayleigh scattering, Raman scattering, and fluorescence detection, this modality captures complementary spatial and molecular data, critical for biomedical research, histology, and drug discovery. Despite its capabilities, Raman light sheet microscopy faces inherent limitations, including low signal intensity, high noise levels, and restricted spatial resolution, which impede the visualization of fine subcellular structures.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Internal Medicine Department, Faculty of Medicine, Ahi Evran University, Kirsehir 40200, Turkey.
: Accurate and rapid detection of COVID-19 remains critical for clinical management, especially in resource-limited settings. Current diagnostic methods face challenges in terms of speed and reliability, creating a need for complementary AI-based models that integrate diverse data sources. : This study aimed to develop and evaluate a multi-modal machine learning model that combines clinical biomarkers and chest X-ray images to enhance diagnostic accuracy and provide interpretable insights.
View Article and Find Full Text PDFJAMIA Open
February 2025
Artificial Intelligence (AI) for Health Institute (AIHealth), Washington University in St Louis, St Louis, MO 63130, United States.
Objective: Extracorporeal membrane oxygenation (ECMO) is among the most resource-intensive therapies in critical care. The COVID-19 pandemic highlighted the lack of ECMO resource allocation tools. We aimed to develop a continuous ECMO risk prediction model to enhance patient triage and resource allocation.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, 2000, South Africa.
The grassland ecosystem forms a critical part of the natural ecosystem, covering up to 15-26% of the Earth's land surface. Grassland significantly impacts the carbon cycle and climate regulation by storing carbon dioxide. The organic matter found in grassland biomass, which acts as a carbon source, greatly expands the carbon stock in terrestrial ecosystems.
View Article and Find Full Text PDFNat Med
January 2025
Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Predicting whether a patient with cancer will benefit from immune checkpoint inhibitors (ICIs) without resorting to advanced genomic or immunologic assays is an important clinical need. To address this, we developed and evaluated SCORPIO, a machine learning system that utilizes routine blood tests (complete blood count and comprehensive metabolic profile) alongside clinical characteristics from 9,745 ICI-treated patients across 21 cancer types. SCORPIO was trained on data from 1,628 patients across 17 cancer types from Memorial Sloan Kettering Cancer Center.
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