DSNN: A DenseNet-Based SNN for Explainable Brain Disease Classification

Construction of the preoperative staging prediction model for cervical cancer based on deep learning and MRI: a retrospective study

Background

Cervical cancer remains a significant global health concern, particularly for women. Accurate preoperative staging is crucial for treatment planning and long-term prognosis. Traditional staging methods rely on manual imaging analysis, which is subjective and time-consuming. Deep learning-based automated staging models offer a promising approach to enhance both accuracy and efficiency.

Methods

This study retrospectively analyzed preoperative MRI scans (T1 and T2 stages) from 112 cervical cancer patients. Seven deep learning models-DenseNet, FBNet, HRNet, RegNet, ResNet50, ShuffleNet, and ViT-were trained and validated using standardized preprocessing, data augmentation, and manual annotation techniques. Convolutional neural networks were employed to extract multidimensional imaging features, forming the basis of an automated staging prediction model.

Results

Among all tested models, HRNet demonstrated the best performance, achieving an accuracy of 69.70%, recall of 68.89%, F1-score of 68.98%, and precision of 69.62%. ShuffleNet ranked second, with slightly lower performance, while ViT exhibited the weakest predictive ability. The ROC curve analysis confirmed HRNet's superior classification capability, with an AUC of 0.7778, highlighting its effectiveness in small-sample datasets.

Conclusion

This study confirms that deep learning models utilizing MRI images can enable automated cervical cancer staging with improved accuracy and efficiency. HRNet, in particular, demonstrates strong potential as a clinical decision-support tool, contributing to the advancement of precision medicine and personalized treatment strategies for cervical cancer.

Electroencephalogram (EEG) Based Fuzzy Logic and Spiking Neural Networks (FLSNN) for Advanced Multiple Neurological Disorder Diagnosis

Neurological disorders are a major global health concern that have a substantial impact on death rates and quality of life. accurately identifying a number of diseases Due to inherent data uncertainties and Electroencephalogram (EEG) pattern overlap, conventional EEG diagnosis methods frequently encounter difficulties. This paper proposes a novel framework that integrates FLSNN to enhance the accuracy and robustness of multiple neurological disorder disease detection from EEG signals. In multiple neurological disorders, the primary motivation is to overcome the limitations of existing methods that are unable to handle the complex and overlapping nature of EEG signals. The key aim is to provide a unified, automated solution for detecting multiple neurological disorders such as epilepsy, Parkinson's, Alzheimer's, schizophrenia, and stroke in a single framework. In the Fuzzy Logic and Spiking Neural Networks (FLSNN) framework, EEG data is preprocessed to eliminate noise and artifacts, while a fuzzy logic model is applied to handling uncertainties prior to applying spike neural networking to analyze the temporal and dynamics of the signals. Processes EEG data three times faster than traditional techniques. This framework achieves 97.46% accuracy in binary classification and 98.87% accuracy in multi-class classification, indicating increased efficiency. This research provides a significant advancement in the diagnosis of multiple neurological disorders using EEG and enhances both the quality and speed of diagnostics from the EEG signal and the advancement of AI-based medical diagnostics. at https://github.com/jainshraddha12/FLSNN , the source code will be available to the public.

Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review

In recent years, Artificial Intelligence (AI) methods, specifically Machine Learning (ML) models, have been providing outstanding results in different areas of knowledge, with the health area being one of its most impactful fields of application. However, to be applied reliably, these models must provide users with clear, simple, and transparent explanations about the medical decision-making process. This systematic review aims to investigate the use and application of explainability in ML models used in brain disease studies. A systematic search was conducted in three major bibliographic databases, Web of Science, Scopus, and PubMed, from January 2014 to December 2023. A total of 133 relevant studies were identified and analyzed out of a total of 682 found in the initial search, in which the explainability of ML models in the medical context was studied, identifying 11 ML models and 12 explainability techniques applied in the study of 20 brain diseases.

A feature-aware multimodal framework with auto-fusion for Alzheimer's disease diagnosis

Alzheimer's disease (AD), one of the most common dementias, has about 4.6 million new cases yearly worldwide. Due to the significant amount of suspected AD patients, early screening for the disease has become particularly important. There are diversified types of AD diagnosis data, such as cognitive tests, images, and risk factors, many prior investigations have primarily concentrated on integrating only high-dimensional features and simple fusion concatenation, resulting in less-than-optimal outcomes for AD diagnosis. Therefore, We propose an enhanced multimodal AD diagnostic framework comprising a feature-aware module and an automatic model fusion strategy (AMFS). To preserve the correlation and significance features within a low-dimensional space, the feature-aware module employs a low-dimensional SHapley Additive exPlanation (SHAP) boosting feature selection as the initial step, following this analysis, diverse tiers of low-dimensional features are extracted from patients' biological data. Besides, in the high-dimensional stage, the feature-aware module integrates cross-modal attention mechanisms to capture subtle relationships among different cognitive domains, neuroimaging modalities, and risk factors. Subsequently, we integrate the aforementioned feature-aware module with graph convolutional networks (GCN) to address heterogeneous data in multimodal AD, while also possessing the capability to perceive relationships between different modalities. Lastly, our proposed AMFS autonomously learns optimal parameters for aligning two sub-models. The validation tests using two ADNI datasets show the high accuracies of 95.9% and 91.9% respectively, in AD diagnosis. The methods efficiently select features from multimodal AD data, optimizing model fusion for potential clinical assistance in diagnostics.

P-hydroxybenzaldehyde protects Caenorhabditis elegans from oxidative stress and β-amyloid toxicity

Introduction

Gastrodia elata is the dried tuber of the orchid Gastrodia elata Bl. It is considered a food consisting of a source of precious medicinal herbs, whose chemical composition is relatively rich. Gastrodia elata and its extracted fractions have been shown to have neuroprotective effects. P-hydroxybenzaldehyde (p-HBA), as one of the main active components of Gastrodia elata, has anti-inflammatory, antioxidative stress, and cerebral protective effects, which has potential for the treatment of Alzheimer's disease (AD). The aim of this study was to verify the role of p-HBA in AD treatment and to investigate its mechanism of action in depth based using the Caenorhabditis elegans (C. elegans) model.

Methods

In this study, we used paralysis, lifespan, behavioral and antistress experiments to investigate the effects of p-HBA on AD and aging. Furthermore, we performed reactive oxygen species (ROS) assay, thioflavin S staining, RNA-seq analysis, qPCR validation, PCR Array, and GFP reporter gene worm experiment to determine the anti-AD effects of p-HBA, as well as in-depth studies on its mechanisms.

Results

p-HBA was able to delay paralysis, improve mobility and resistance to stress, and delay aging in the AD nematode model. Further mechanistic studies showed that ROS and lipofuscin levels, Aβ aggregation, and toxicity were reduced after p-HBA treatment, suggesting that p-HBA ameliorated Aβ-induced toxicity by enhancing antioxidant and anti-aging activity and inhibiting Aβ aggregation. p-HBA had a therapeutic effect on AD by improving stress resistance, as indicated by the down-regulation of NLP-29 and UCR-11 expression and up-regulation of PQN-75 and LYS-3 expression. In addition, the gene microarray showed that p-HBA treatment played a positive role in genes related to AD, anti-aging, ribosomal protein pathway, and glucose metabolism, which were collectively involved in the anti-AD mechanism of p-HBA. Finally, we also found that p-HBA promoted nuclear localization of DAF-16 and increased the expression of SKN-1, SOD-3, and GST-4, which contributed significantly to inhibition of Aβ toxicity and enhancement of antioxidative stress.

Conclusion

Our work suggests that p-HBA has some antioxidant and anti-aging activities. It may be a viable candidate for the treatment and prevention of Alzheimer's disease.

Adaptive Tensor-Based Feature Extraction for Pupil Segmentation in Cataract Surgery

Cataract surgery remains the only definitive treatment for visually significant cataracts, which are a major cause of preventable blindness worldwide. Successful performance of cataract surgery relies on stable dilation of the pupil. Automated pupil segmentation from surgical videos can assist surgeons in detecting risk factors for pupillary instability prior to the development of surgical complications. However, surgical illumination variations, surgical instrument obstruction, and lens material hydration during cataract surgery can limit pupil segmentation accuracy. To address these problems, we propose a novel method named adaptive wavelet tensor feature extraction (AWTFE). AWTFE is designed to enhance the accuracy of deep learning-powered pupil recognition systems. First, we represent the correlations among spatial information, color channels, and wavelet subbands by constructing a third-order tensor. We then utilize higher-order singular value decomposition to eliminate redundant information adaptively and estimate pupil feature information. We evaluated the proposed method by conducting experiments with state-of-the-art deep learning segmentation models on our BigCat dataset consisting of 5,700 annotated intraoperative images from 190 cataract surgeries and a public CaDIS dataset. The experimental results reveal that the AWTFE method effectively identifies features relevant to the pupil region and improved the overall performance of segmentation models by up to 2.26% (BigCat) and 3.31% (CaDIS). Incorporation of the AWTFE method led to statistically significant improvements in segmentation performance (P < 1.29  ×  10-10 for each model) and yielded the highest-performing model overall (Dice coefficients of 94.74% and 96.71% for the BigCat and CaDIS datasets, respectively). In performance comparisons, the AWTFE consistently outperformed other feature extraction methods in enhancing model performance. In addition, the proposed AWTFE method significantly improved pupil recognition performance by up to 2.87% in particularly challenging phases of cataract surgery.

Survival and grade of the glioma prediction using transfer learning

Glioblastoma is a highly malignant brain tumor with a life expectancy of only 3-6 months without treatment. Detecting and predicting its survival and grade accurately are crucial. This study introduces a novel approach using transfer learning techniques. Various pre-trained networks, including EfficientNet, ResNet, VGG16, and Inception, were tested through exhaustive optimization to identify the most suitable architecture. Transfer learning was applied to fine-tune these models on a glioblastoma image dataset, aiming to achieve two objectives: survival and tumor grade prediction.The experimental results show 65% accuracy in survival prediction, classifying patients into short, medium, or long survival categories. Additionally, the prediction of tumor grade achieved an accuracy of 97%, accurately differentiating low-grade gliomas (LGG) and high-grade gliomas (HGG). The success of the approach is attributed to the effectiveness of transfer learning, surpassing the current state-of-the-art methods. In conclusion, this study presents a promising method for predicting the survival and grade of glioblastoma. Transfer learning demonstrates its potential in enhancing prediction models, particularly in scenarios with limited large datasets. These findings hold promise for improving diagnostic and treatment approaches for glioblastoma patients.

Research progress of natural products and their derivatives against Alzheimer's disease

Alzheimer's disease (AD), a persistent neurological dysfunction, has an increasing prevalence with the aging of the world and seriously threatens the health of the elderly. Although there is currently no effective treatment for AD, researchers have not given up, and are committed to exploring the pathogenesis of AD and possible therapeutic drugs. Natural products have attracted considerable attention owing to their unique advantages. One molecule can interact with multiple AD-related targets, thus having the potential to be developed in a multi-target drug. In addition, they are amenable to structural modifications to increase interaction and decrease toxicity. Therefore, natural products and their derivatives that ameliorate pathological changes in AD should be intensively and extensively studied. This review mainly presents research on natural products and their derivatives for the treatment of AD.

Deep learning-based radiomic nomograms for predicting Ki67 expression in prostate cancer

BACKGROUND

To explore the value of a multiparametric magnetic resonance imaging (MRI)-based deep learning model for the preoperative prediction of Ki67 expression in prostate cancer (PCa).

MATERIALS

The data of 229 patients with PCa from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. Deep learning features were extracted and selected from each patient's prostate multiparametric MRI (diffusion-weighted imaging, T2-weighted imaging, and contrast-enhanced T1-weighted imaging sequences) data to establish a deep radiomic signature and construct models for the preoperative prediction of Ki67 expression. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a joint model. The predictive performance of multiple deep-learning models was then evaluated.

RESULTS

Seven prediction models were constructed: one clinical model, three deep learning models (the DLRS-Resnet, DLRS-Inception, and DLRS-Densenet models), and three joint models (the Nomogram-Resnet, Nomogram-Inception, and Nomogram-Densenet models). The areas under the curve (AUCs) of the clinical model in the testing, internal validation, and external validation sets were 0.794, 0.711, and 0.75, respectively. The AUCs of the deep models and joint models ranged from 0.939 to 0.993. The DeLong test revealed that the predictive performance of the deep learning models and the joint models was superior to that of the clinical model (p < 0.01). The predictive performance of the DLRS-Resnet model was inferior to that of the Nomogram-Resnet model (p < 0.01), whereas the predictive performance of the remaining deep learning models and joint models did not differ significantly.

CONCLUSION

The multiple easy-to-use deep learning-based models for predicting Ki67 expression in PCa developed in this study can help physicians obtain more detailed prognostic data before a patient undergoes surgery.

Variational Autoencoders-BasedSelf-Learning Model for Tumor Identification and Impact Analysis from 2-D MRI Images

Over the past few years, a tremendous change has occurred in computer-aided diagnosis (CAD) technology. The evolution of numerous medical imaging techniques has enhanced the accuracy of the preliminary analysis of several diseases. Magnetic resonance imaging (MRI) is a prevalent technology extensively used in evaluating the progress of the spread of malignant tissues or abnormalities in the human body. This article aims to automate a computationally efficient mechanism that can accurately identify the tumor from MRI images and can analyze the impact of the tumor. The proposed model is robust enough to classify the tumors with minimal training data. The generative variational autoencoder models are efficient in reconstructing the images identical to the original images, which are used in adequately training the model. The proposed self-learning algorithm can learn from the insights from the autogenerated images and the original images. Incorporating long short-term memory (LSTM) is faster processing of the high dimensional imaging data, making the radiologist's task and the practitioners more comfortable assessing the tumor's progress. Self-learning models need comparatively less data for the training, and the models are more resource efficient than the various state-of-art models. The efficiency of the proposed model has been assessed using various benchmark metrics, and the obtained results have exhibited an accuracy of 89.7%. The analysis of the progress of tumor growth is presented in the current study. The obtained accuracy is not pleasing in the healthcare domain, yet the model is reasonably fair in dealing with a smaller size dataset by making use of an image generation mechanism. The study would outline the role of an autoencoder in self-learning models. Future technologies may include sturdy feature engineering models and optimized activation functions that would yield a better result.