Intelligence-based medicine最新文献

Alzheimer's disease is a complex neurodegenerative disorder that profoundly impacts millions of individuals worldwide, presenting significant challenges in both diagnosis and treatment. Recent advances in deep learning-based methods have shown promising potential for predicting disease progression using multimodal data. However, the majority of studies in this domain have predominantly focused on cross-sectional data, neglecting the crucial temporal dimension of the disease's progression. In this study, we propose a novel approach to predict the progression of Alzheimer's disease by leveraging a multimodal time-series forecasting system based on graph representation learning. Our approach incorporates a Temporal Graph Network encoder, employing k-nearest neighbors and Cumulative Bayesian Ridge with high correlation imputation to generate graph node embeddings at each time step. Furthermore, we employ an Encoder-Decoder architecture, where a Graph Attention Network translates a dynamic graph into node embeddings, and a decoder estimates future edge probabilities. When utilizing all available patient features in the ADNI dataset, our proposed method achieved an Area Under the Curve (AUC) of 0.8090 for dynamic edge prediction. Furthermore, for neuroimaging data, the AUC improved significantly to 0.8807.

Autism Spectrum Disorders (ASD) are one of the most serious health problems that our generation is facing [1]. It affects around one out of every 54 children and causes issues with social interaction, communication [2] and repetitive behaviors [3]. The development of full biomarkers for neuroimaging is a crucial step in diagnosing and tailoring medical care for autism spectrum disorder [4]. Volumetric studies focused on 3D MRI texture features have shown a high capacity for detecting abnormalities and characterizing variations caused by tissue heterogeneity. Recently, it has been the interest of comprehensive studies. However, only a few studies have aimed to investigate the link between object texture and ASD. This paper suggests a framework based on geometric texture features analyzing the variations between ASD and development control (DC) subjects. Our study uses 1114 T1-weighted MRI scans from two groups of subjects: 521 individuals with ASD and 593 controls (age range: 6–64 years) [5], divided into three broad age groups. We then computed the features from automatically labeled subcortical and cortical regions and encoded them as texture features by applying seven global Riemannian geometry descriptors and eight local features of standard Harlicks quantifier functions. Significant tests were used to identify texture volumetric differences between ASD and DC subjects. The most discriminative features are selected by applying the Correlation Matrix, and these features are used to classify the two classes using an Artificial Neural Network analysis. Preliminary results indicate that in ASD subjects, all 15 structure-derived features and subcortical regions tested have significantly different distributions from DC subjects.

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The Gram staining method is one of the most effective morphological identification procedures for detecting bacteria from direct smear microscopy. This staining process is inexpensive. It aids in diagnosing bacterial infections quickly as it is used for direct clinical sample specimens such as pus, urine, and sputum. The computer-aided diagnostic system aids the clinician by avoiding tedious manual evaluation procedures. However, images captured often suffer from contrast, illumination, and stain variations due to various camera settings and situations. These differences are due to image acquisition conditions, sample quality, and poor staining procedures. These variations affect the diagnosis process, lowering the image analysis performance of the computer-aided diagnosis system. In this context, the present work proposes a novel color normalization approach based on a Cycle Generative Adversarial Network(GAN). We introduce a novel normalization loss function, L_cycm, which is integrated into our dedicated normalization loss, L_N, within the framework of Cycle GAN(CGAN). The proposed method is compared with the state-of-the-art normalization algorithms qualitatively and quantitatively using the KMC dataset. In addition, the study demonstrates the impact of normalization on the Convolutional Neural Network (CNN) -based segmentation and classification process. Furthermore, a bacteria detection framework is proposed based on the U2Net segmentation model and a CNN classifier. The proposed normalization achieved an SSIM score of 0.93 ± 0.07 and PSNR of 29 ± 3.7. The accuracy of the CNN-based classifier improved by 40 % after normalization.