Understanding the neural mechanisms underlying emotional processing is critical for advancements in emotional neuroscience. This study explores the relationship between emotion and motion perception using Event-Related Potentials (ERPs) in a structured experimental setup. We incorporate subjective intensity ratings to enrich the data by capturing the subjective experiences of participants in response to emotional stimuli with implied motion and no motion. Thirty university students participated in the study, where EEG data was collected and analyzed using threshold-free cluster enhancement (TFCE) for time-domain analysis and Repeated Measures ANOVA for frequency-domain analysis. Furthermore, we developed a multimodal deep learning model to predict subjective intensity levels from EEG-derived features. This model leverages statistical, spectral, and auto covariance features, integrated through a transformer encoder layer, to enhance predictive capability. Our findings contribute to a deeper understanding of emotional processing in the brain and highlight the importance of incorporating subjective measures in neuroscience research.
Despite the renal biopsy being the gold standard for diagnosing glomerulonephritis, this practice remains inaccessible for many patients worldwide. Nephropathologists typically combine microscopy, immunohistology, transmission electron microscopy, clinical information, and genetic studies for diagnosis. However, variability in nephropathology evaluation has hindered its integration with emerging technologies and personalized medicine. This study proposes the use of deep learning to extract significant features to distinguish glomerulonephritis from PAS sections without other modalities. To test this hypothesis, various AI methods were tested for classifying 12 common glomerulonephritis diagnoses. Finally, a sequential classification was implemented, initially characterizing sclerosed and non-sclerosed glomeruli using Swin-Transformers, followed by classifying the non-sclerosed glomeruli into 12 types of glomerulonephritis using ConvNeXt. The first step achieved an average Balanced Accuracy of 97% and an AUC of 0.96. In the second step, a Balanced Accuracy considering up to the top3 of 79.5% and an avarage AUCs of 0.76 were achieved. This study establishes a baseline for this challenging classification task, demonstrating promising results even on single PAS glomerular crops.
The Receiver Operating Characteristic (ROC) curve is a critical tool for binary classification analysis in medicine, with the Area Under the ROC Curve (AUROC) serving as a widely accepted metric to evaluate the performance of binary classifiers. This study conducts a comprehensive review of the ROC curve with a focus on its utility in outlier identification. We introduce a novel scoring method to rank actual positives and actual negatives within a test set, according to their impact on AUROC degradation. We bridge the scoring system with the ROC curve analysis to quantify each data point's contribution to AUROC loss. Furthermore, we introduce the IMICS ROC Analyzer, a graphical user interface-based software, embedded with our innovative algorithms. Through the use of an open-source prostate cancer dataset, we illustrate the application of our algorithms for practical outlier detection in binary classification tasks. The IMICS ROC Analyzer enhances the field of precision medicine by allowing for measuring an individual's contributions (be it patients, lesions, or samples) to the overall AUROC, thus facilitating confidence measurement of Machine Learning (ML) classifiers for individual cases of interest in a cohort.
The critical shortage of medical professionals in low-resource countries, notably in Africa, hinders adequate health-care delivery. AI, particularly Multimodal Large Language models (MLLMs), can enhance the efficiency of healthcare systems by assisting in medical image analysis and diagnosis. However, the deployment of state-of-the-art MLLMs is limited in these regions due to the high computational demands that exceed the capabilities of consumer-grade GPUs. This paper presents a framework for optimizing MLLMs for resource-constrained environments. We introduce optimized medical MLLMs including TinyLLaVA-Med-F, a medical fine-tuned MLLM, and quantized variants (TinyLLa VA - Med- FQ4, Tiny LLa VA - Med- FQ8, LLa VA - Med-Q4, and LLaVA-Med-Q8) that demonstrate substantial reductions in memory usage without significant loss in accuracy. Specifically, TinyLLaVA-Med-FQ4 achieves the greatest reductions, lowering dynamic memory by approximately 89% and static memory by 90% compared to LLaVA-Med. Similarly, LLaVA-Med-Q4 reduces dynamic memory by 65% and static memory by 67% compared to state-of-the-art LLaVA-Med. These memory reductions make these models feasible for deployment on consumer-grade GPUs such as RTX 3050. This research underscores the potential for deploying optimized MLLMs in low-resource settings, providing a foundation for future developments in accessible AI-driven healthcare solutions.
Early identification of individuals at risk for Alzheimer's disease is essential to improve treatment effectiveness. Cerebrospinal fluid analyses and positron emission tomography (PET) scans are commonly used to detect the presence of beta-amyloid and tau, which are associated with an increased risk of conversion to Alzheimer's disease. However, these biomarker tests are expensive and involve invasive procedures. Researchers are working towards discovering easily measurable biomarkers to detect individuals at risk, but only a few have been identified thus far. There is a need to discover biomarkers that are cost-efficient and non-invasive to test. We propose a machine learning approach for discovering potential risk biomarkers of Alzheimer's disease through the analysis of physiological responses to the cognitively complex task of driving by using decision tree ensemble techniques. Though driving patterns in early Alzheimer's have been previously studied, physiological responses of cognitively normal seniors during driving remain unexplored. As a first step, we measure heart rate, electrodermal activity, and temperature responses to several driving events, such as right turns and roundabouts, of seniors with and without elevated PET beta-amyloid levels to explore the relationship between these physiological responses and amyloid level. Data were collected from 26 participants with elevated beta-amyloid and 28 without. We used four machine learning algorithms for classification: Random Forest, Extra Trees, AdaBoost, and XGBoost, and developed a novel methodology to extract significant features from these models. By doing so, we successfully identified five risk biomarkers most influential in differentiating the two groups with and without elevated beta-amyloid.
