Ren Fail. 2024 Dec;46(2):2417740. doi: 10.1080/0886022X.2024.2417740. Epub 2024 Oct 22.

ABSTRACT

We developed a multimodal ultrasound (US) deep learning (DL) fusion model to automatically classify early fibrosis in patients with chronic kidney disease (CKD). This prospective study included patients with CKD who underwent continuous gray-scale US, superb microvascular imaging, and strain elastography from May to November 2022. According to the pathological tubular atrophy and interstitial fibrosis score, patients were divided into minimal and mild groups (affected area ≤10% and 11 - 25% of the total cortical volume, respectively). The dataset was divided into training (70%) and test (30%) sets. A DL model combining the features of the three US modes was developed to predict early fibrosis in patients with CKD. We compared these findings with the area under the receiver operating characteristic curve (AUC) of the clinical model by analyzing the receiver operating characteristic curve in the test set. The AUC of single-mode DL based on gray-scale US, superb microvascular imaging, and strain elastography was 0.682, 0.745, and 0.648, respectively, while that of the multimodal US DL model was 0.86. The accuracy, specificity, and sensitivity of the multimodal US DL model were 0.779, 0.767, and 0.796, respectively, and the negative and positive predictive values were 0.842 and 0.706, respectively. The AUC of the multimodal US DL model was significantly better than that of the single-mode DL and clinical models. The DL algorithm developed using multimodal US images can effectively predict early fibrosis in patients with CKD with significantly greater accuracy than single-mode DL or clinical models.

PMID:39435700 | DOI:10.1080/0886022X.2024.2417740

Big Data. 2024 Oct 22. doi: 10.1089/big.2023.0117. Online ahead of print.

ABSTRACT

The influence maximization problem has several issues, including low infection rates and high time complexity. Many proposed methods are not suitable for large-scale networks due to their time complexity or free parameter usage. To address these challenges, this article proposes a local heuristic called Embedding Technique for Influence Maximization (ETIM) that uses shell decomposition, graph embedding, and reduction, as well as combined local structural features. The algorithm selects candidate nodes based on their connections among network shells and topological features, reducing the search space and computational overhead. It uses a deep learning-based node embedding technique to create a multidimensional vector of candidate nodes and calculates the dependency on spreading for each node based on local topological features. Finally, influential nodes are identified using the results of the previous phases and newly defined local features. The proposed algorithm is evaluated using the independent cascade model, showing its competitiveness and ability to achieve the best performance in terms of solution quality. Compared with the collective influence global algorithm, ETIM is significantly faster and improves the infection rate by an average of 12%.

PMID:39435527 | DOI:10.1089/big.2023.0117

Acta Radiol. 2024 Oct 22:2841851241288507. doi: 10.1177/02841851241288507. Online ahead of print.

ABSTRACT

BACKGROUND: Low-keV virtual monoenergetic images (VMIs) of dual-energy computed tomography (CT) enhances iodine contrast for detecting small arteries like the Adamkiewicz artery (AKA), but image noise can be problematic. Deep-learning image reconstruction (DLIR) effectively reduces noise without sacrificing image quality.

PURPOSE: To evaluate whether DLIR on low-keV VMIs of dual-energy CT scans improves the visualization of the AKA.

MATERIAL AND METHODS: We enrolled 29 patients who underwent CT angiography before aortic repair. VMIs obtained at 70 and 40 keV were reconstructed using hybrid iterative reconstruction (HIR), and 40 keV VMIs were reconstructed using DLIR. The image noise of the spinal cord, the maximum CT values of the anterior spinal artery (ASA), and the contrast-to-noise ratio (CNR) of the ASA were compared. The overall image quality and the delineation of the AKA were evaluated on a 4-point score (1 = poor, 4 = excellent).

RESULTS: The mean image noise of the spinal cord was significantly lower on 40-keV DLIR than on 40-keV HIR scans; they were significantly higher than on 70-keV HIR images. The CNR of the ASA was highest on the 40-keV DLIR images among the three reconstruction images. The mean image quality scores for 40-keV DLIR and 70-keV HIR scans were comparable, and higher than of 40-keV HIR images. The mean delineation scores for 40-keV HIR and 40-keV DLIR scans were significantly higher than for 70-keV HIR images.

CONCLUSION: Visualization of the AKA was significantly better on low-keV VMIs subjected to DLIR than conventional HIR images.

PMID:39435504 | DOI:10.1177/02841851241288507

Comput Struct Biotechnol J. 2024 Oct 5;23:3498-3505. doi: 10.1016/j.csbj.2024.09.031. eCollection 2024 Dec.

ABSTRACT

Recent advancements in Natural Language Processing (NLP) have been significantly driven by the development of Large Language Models (LLMs), representing a substantial leap in language-based technology capabilities. These models, built on sophisticated deep learning architectures, typically transformers, are characterized by billions of parameters and extensive training data, enabling them to achieve high accuracy across various tasks. The transformer architecture of LLMs allows them to effectively handle context and sequential information, which is crucial for understanding and generating human language. Beyond traditional NLP applications, LLMs have shown significant promise in bioinformatics, transforming the field by addressing challenges associated with large and complex biological datasets. In genomics, proteomics, and personalized medicine, LLMs facilitate identifying patterns, predicting protein structures, or understanding genetic variations. This capability is crucial, e.g., for advancing drug discovery, where accurate prediction of molecular interactions is essential. This review discusses the current trends in LLMs research and their potential to revolutionize the field of bioinformatics and accelerate novel discoveries in the life sciences.

PMID:39435343 | PMC:PMC11493188 | DOI:10.1016/j.csbj.2024.09.031

Comput Struct Biotechnol J. 2024 Oct 3;23:3481-3488. doi: 10.1016/j.csbj.2024.09.030. eCollection 2024 Dec.

ABSTRACT

Digital twins in biomedical research, i.e. virtual replicas of biological entities such as cells, organs, or entire organisms, hold great potential to advance personalized healthcare. As all biological processes happen in space, there is a growing interest in modeling biological entities within their native context. Leveraging generative artificial intelligence (AI) and high-volume biomedical data profiled with spatial technologies, researchers can recreate spatially-resolved digital representations of a physical entity with high fidelity. In application to biomedical fields such as computational pathology, oncology, and cardiology, these generative digital twins (GDT) thus enable compelling in silico modeling for simulated interventions, facilitating the exploration of 'what if' causal scenarios for clinical diagnostics and treatments tailored to individual patients. Here, we outline recent advancements in this novel field and discuss the challenges and future research directions.

PMID:39435342 | PMC:PMC11491725 | DOI:10.1016/j.csbj.2024.09.030

Open Forum Infect Dis. 2024 Oct 5;11(10):ofae581. doi: 10.1093/ofid/ofae581. eCollection 2024 Oct.

ABSTRACT

BACKGROUND: This study investigated the abilities of radiomics and clinical feature models to distinguish kidney stone-associated urinary tract infections (KS-UTIs) using computed tomography.

METHODS: A retrospective analysis was conducted on a single-center dataset comprising computed tomography (CT) scans and corresponding clinical information from 461 patients with kidney stones. Radiomics features were extracted from CT images and underwent dimensionality reduction and selection. Multiple machine learning (Three types of shallow learning and four types of deep learning) algorithms were employed to construct radiomics and clinical models in this study. Performance evaluation and optimal model selection were done using receiver operating characteristic (ROC) curve analysis and Delong test. Univariate and multivariate logistic regression analyzed clinical and radiomics features to identify significant variables and develop a clinical model. A combined model integrating radiomics and clinical features was established. Model performance was assessed by ROC curve analysis, clinical utility was evaluated through decision curve analysis, and the accuracy of the model was analyzed via calibration curve.

RESULTS: Multilayer perceptron (MLP) showed higher classification accuracy than other classifiers (area under the curve (AUC) for radiomics model: train 0.96, test 0.94; AUC for clinical model: train 0.95, test 0.91. The combined radiomics-clinical model performed best (AUC for combined model: train 0.98, test 0.95). Decision curve and calibration curve analyses confirmed the model's clinical efficacy and calibration.

CONCLUSIONS: This study showed the effectiveness of combining radiomics and clinical features from CT scans to identify KS-UTIs. A combined model using MLP exhibited strong classification abilities.

PMID:39435322 | PMC:PMC11493090 | DOI:10.1093/ofid/ofae581

Front Oncol. 2024 Oct 7;14:1435041. doi: 10.3389/fonc.2024.1435041. eCollection 2024.

ABSTRACT

With its increasing global prevalence, lung cancer remains a critical health concern. Despite the advancement of screening programs, patient selection and risk stratification pose significant challenges. This study addresses the pressing need for early detection through a novel diagnostic approach that leverages innovative image processing techniques. The urgency of early lung cancer detection is emphasized by its alarming growth worldwide. While computed tomography (CT) surpasses traditional X-ray methods, a comprehensive diagnosis requires a combination of imaging techniques. This research introduces an advanced diagnostic tool implemented through image processing methodologies. The methodology commences with histogram equalization, a crucial step in artifact removal from CT images sourced from a medical database. Accurate lung CT image segmentation, which is vital for cancer diagnosis, follows. The Otsu thresholding method and optimization, employing Colliding Bodies Optimization (CBO), enhance the precision of the segmentation process. A local binary pattern (LBP) is deployed for feature extraction, enabling the identification of nodule sizes and precise locations. The resulting image underwent classification using the densely connected CNN (DenseNet) deep learning algorithm, which effectively distinguished between benign and malignant tumors. The proposed CBO+DenseNet CNN exhibits remarkable performance improvements over traditional methods. Notable enhancements in accuracy (98.17%), specificity (97.32%), precision (97.46%), and recall (97.89%) are observed, as evidenced by the results from the fractional randomized voting model (FRVM). These findings highlight the potential of the proposed model as an advanced diagnostic tool. Its improved metrics promise heightened accuracy in tumor classification and localization. The proposed model uniquely combines Colliding Bodies Optimization (CBO) with DenseNet CNN, enhancing segmentation and classification accuracy for lung cancer detection, setting it apart from traditional methods with superior performance metrics.

PMID:39435294 | PMC:PMC11491319 | DOI:10.3389/fonc.2024.1435041

Ophthalmol Sci. 2024 Aug 22;5(1):100597. doi: 10.1016/j.xops.2024.100597. eCollection 2025 Jan-Feb.

ABSTRACT

PURPOSE: Pupillary instability is a known risk factor for complications in cataract surgery. This study aims to develop and validate an innovative and reliable computational framework for the automated assessment of pupil morphologic changes during the various phases of cataract surgery.

DESIGN: Retrospective surgical video analysis.

SUBJECTS: Two hundred forty complete surgical video recordings, among which 190 surgeries were conducted without the use of pupil expansion devices (PEDs) and 50 were performed with the use of a PED.

METHODS: The proposed framework consists of 3 stages: feature extraction, deep learning (DL)-based anatomy recognition, and obstruction (OB) detection/compensation. In the first stage, surgical video frames undergo noise reduction using a tensor-based wavelet feature extraction method. In the second stage, DL-based segmentation models are trained and employed to segment the pupil, limbus, and palpebral fissure. In the third stage, obstructed visualization of the pupil is detected and compensated for using a DL-based algorithm. A dataset of 5700 intraoperative video frames across 190 cataract surgeries in the BigCat database was collected for validating algorithm performance.

MAIN OUTCOME MEASURES: The pupil analysis framework was assessed on the basis of segmentation performance for both obstructed and unobstructed pupils. Classification performance of models utilizing the segmented pupil time series to predict surgeon use of a PED was also assessed.

RESULTS: An architecture based on the Feature Pyramid Network model with Visual Geometry Group 16 backbone integrated with the adaptive wavelet tensor feature extraction feature extraction method demonstrated the highest performance in anatomy segmentation, with Dice coefficient of 96.52%. Incorporation of an OB compensation algorithm improved performance further (Dice 96.82%). Downstream analysis of framework output enabled the development of a Support Vector Machine-based classifier that could predict surgeon usage of a PED prior to its placement with 96.67% accuracy and area under the curve of 99.44%.

CONCLUSIONS: The experimental results demonstrate that the proposed framework (1) provides high accuracy in pupil analysis compared with human-annotated ground truth, (2) substantially outperforms isolated use of a DL segmentation model, and (3) can enable downstream analytics with clinically valuable predictive capacity.

FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

PMID:39435136 | PMC:PMC11492071 | DOI:10.1016/j.xops.2024.100597

Heliyon. 2024 Oct 3;10(19):e38907. doi: 10.1016/j.heliyon.2024.e38907. eCollection 2024 Oct 15.

ABSTRACT

This study aims to propose a deep learning (DL)-based physical education course recommendation system by combining the Internet of Things (IoT) technology and DL, to improve the accuracy and personalization of recommendation. Firstly, IoT devices such as smart bracelets and smart clothing are used to monitor students' physiological data in real-time, and IoT sensors are utilized to sense the environment around students. Secondly, IoT devices capture students' social interactions with their peers, recommending socially oriented courses. Meanwhile, by integrating IoT data with students' academic data, course recommendations are optimized to match students' learning progress and schedule. Finally, Generative Adversarial Network (GAN) models, especially the improved Regularization Penalty Conditional Feature Generative Adversarial Network (RP-CFGAN) model, deal with data sparsity and cold start problems. The experimental results show that this model performs well in TopN evaluation and is markedly enhanced compared with traditional models. This study denotes that integrating IoT technology and GAN models can more accurately understand student needs and provide personalized recommendations. Although the model performs well, there is still room for improvement, such as exploring more regularization techniques, protecting user privacy, and extending the system to diverse platforms and scenarios.

PMID:39435083 | PMC:PMC11492338 | DOI:10.1016/j.heliyon.2024.e38907

Heliyon. 2024 Oct 4;10(19):e38938. doi: 10.1016/j.heliyon.2024.e38938. eCollection 2024 Oct 15.

ABSTRACT

In acute aminoglycoside ototoxicity of the unilateral inner ear, physical abnormalities, such as nystagmus and postural alteration, are relieved within a few days by neural compensation. To examine exploratory behavior over an extended period, behaviors of freely moving mice after unilateral kanamycin injection into the inner ear were recorded in a home cage environment. The tail was excluded from deep learning-mediated object detection because of its delayed movement relative to the body. All detection results were confirmed using a convolutional neural network classification model. In kanamycin-injected mice, the total distance moved in 15 min increased on postoperative day 3. Furthermore, injured mice turned more frequently toward the healthy side up to 17 days after the surgery. This tendency resulted in increased clockwise movements in home cage recordings. Moreover, tail suspension and twisting toward the healthy side induced a physical sign for up to 14 days after the injury; the mice rapidly rotated with dorsal bending. Our analysis strategy employing deep learning helps to evaluate neuronal compensatory processes for an extended period and is useful for assessing the efficacy of therapeutic interventions.

PMID:39435078 | PMC:PMC11492029 | DOI:10.1016/j.heliyon.2024.e38938

MethodsX. 2024 Oct 4;13:102995. doi: 10.1016/j.mex.2024.102995. eCollection 2024 Dec.

ABSTRACT

The segmentation of pancreas and pancreatic tumor remain a persistent challenge for radiologists. Consequently, it is essential to develop automated segmentation methods to address this task. U-Net based models are most often used among various deep learning-based techniques in tumor segmentation. This paper introduces an innovative hybrid two-stage U-Net model for segmenting both the pancreas and pancreatic tumors. The optimization technique, used in this approach, involves a combination of meta-heuristic optimization algorithms namely, Grey Wolf Border Collie Optimization (GWBCO) technique, combining the Grey Wolf Optimization algorithm and the Border Collie Optimization algorithm. Our approach is evaluated using key parameters, such as Dice Similarity Coefficient (DSC), Jaccard Index (JI), sensitivity, specificity and precision to assess its effectiveness and achieves a DSC of 93.33 % for pancreas segmentation. Additionally, the model also achieves high DSC of 91.46 % for pancreatic tumor segmentation. This method helps in improving the diagnostic accuracy and assists medical professionals to provide treatment at an early stage with precise intervention. The method offers•Two-stage U-Net model addresses both pancreas and tumor segmentation.•Combination of two metaheuristic optimization algorithms, Grey Wolf and Border Collie for enhanced performance.•High dice similarity coefficient for pancreas and tumor segmentation.

PMID:39435045 | PMC:PMC11491966 | DOI:10.1016/j.mex.2024.102995

Biomed Eng Online. 2024 Oct 21;23(1):103. doi: 10.1186/s12938-024-01297-x.

ABSTRACT

Multiphase information fusion and spatiotemporal feature modeling play a crucial role in the task of four-phase CT lesion recognition. In this paper, we propose a four-phase CT lesion recognition algorithm based on multiphase information fusion framework and spatiotemporal prediction module. Specifically, the multiphase information fusion framework uses the interactive perception mechanism to realize the channel-spatial information interactive weighting between multiphase features. In the spatiotemporal prediction module, we design a 1D deep residual network to integrate multiphase feature vectors, and use the GRU architecture to model the temporal enhancement information between CT slices. In addition, we employ CT image pseudo-color processing for data augmentation and train the whole network based on a multi-task learning framework. We verify the proposed network on a four-phase CT dataset. The experimental results show that the proposed network can effectively fuse the multi-phase information and model the temporal enhancement information between CT slices, showing excellent performance in lesion recognition.

PMID:39434126 | DOI:10.1186/s12938-024-01297-x

BMC Urol. 2024 Oct 21;24(1):230. doi: 10.1186/s12894-024-01618-1.

ABSTRACT

BACKGROUND: This study was conducted to investigate the clinical value of a navigation system based on deep learning and mixed reality for the treatment of kidney stones with percutaneous nephrolithotomy (PNL), and to improve its theoretical basis for the treatment of kidney stones.

METHODS: The data of 136 patients with kidney stones from October 2021 to December 2023 were retrospectively analyzed. All patients underwent PNL, and were categorized into a control group (Group 1) and a surgical navigation group (Group 2) according to puncture positioning method. Preoperative computed tomography (CT) was performed in both groups. In group 1, procedures were performed under standard ultrasound guidance. PNL was performed with navigation system fused with ultrasound to guide percutaneous puncture in group 2. The baseline information and procedural characteristics of both groups were compared.

RESULTS: PNL was successfully performed in both groups. No significant difference was found in the baseline date between the two groups. In group 2, real-time ultrasound images could be accurately matched with CT images with the aid of navigation system. The success rate of single puncture, puncture time, and decrease in hemoglobin were significantly improved in group 2 compared to group 1. (p < 0.05).

CONCLUSIONS: The application of navigation system based on deep learning and mixed reality in PNL for kidney stones allows for real-time intraoperative navigation, with acceptable accuracy and safety. Most importantly, this technique is easily mastered, particularly by novice surgeons in the field of PNL.

PMID:39434080 | DOI:10.1186/s12894-024-01618-1

BMC Cancer. 2024 Oct 21;24(1):1300. doi: 10.1186/s12885-024-13049-0.

ABSTRACT

PURPOSE: Despite suffering from the same disease, each patient exhibits a distinct microbiological profile and variable reactivity to prescribed treatments. Most doctors typically use a standardized treatment approach for all patients suffering from a specific disease. Consequently, the challenge lies in the effectiveness of this standardized treatment and in adapting it to each individual patient. Personalized medicine is an emerging field in which doctors use diagnostic tests to identify the most effective medical treatments for each patient. Prognosis, disease monitoring, and treatment planning rely on manual, error-prone methods. Artificial intelligence (AI) uses predictive techniques capable of automating prognostic and monitoring processes, thus reducing the error rate associated with conventional methods.

METHODS: This paper conducts an analysis of current literature, encompassing the period from January 2015 to 2023, based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).

RESULTS: In assessing 25 pertinent studies concerning predicting neoadjuvant treatment (NAT) response in breast cancer (BC) patients, the studies explored various imaging modalities (Magnetic Resonance Imaging, Ultrasound, etc.), evaluating results based on accuracy, sensitivity, and area under the curve. Additionally, the technologies employed, such as machine learning (ML), deep learning (DL), statistics, and hybrid models, were scrutinized. The presentation of datasets used for predicting complete pathological response (PCR) was also considered.

CONCLUSION: This paper seeks to unveil crucial insights into the application of AI techniques in personalized oncology, particularly in the monitoring and prediction of responses to NAT for BC patients. Finally, the authors suggest avenues for future research into AI-based monitoring systems.

PMID:39434042 | DOI:10.1186/s12885-024-13049-0

Commun Biol. 2024 Oct 21;7(1):1359. doi: 10.1038/s42003-024-07080-x.

ABSTRACT

Fluorescence lifetime imaging microscopy (FLIM) provides quantitative readouts of biochemical microenvironments, holding great promise for biomedical imaging. However, conventional FLIM relies on slow photon counting routines to accumulate sufficient photon statistics, restricting acquisition speeds. Here we demonstrate SparseFLIM, an intelligent paradigm for achieving high-fidelity FLIM reconstruction from sparse photon measurements. We develop a coupled bidirectional propagation network that enriches photon counts and recovers hidden spatial-temporal information. Quantitative analysis shows over tenfold photon enrichment, dramatically improving signal-to-noise ratio, lifetime accuracy, and correlation compared to the original sparse data. SparseFLIM enables reconstructing spatially and temporally undersampled FLIM at full resolution and channel count. The model exhibits strong generalization across experimental modalities including multispectral FLIM and in vivo endoscopic FLIM. This work establishes deep learning as a promising approach to enhance fluorescence lifetime imaging and transcend limitations imposed by the inherent codependence between measurement duration and information content.

PMID:39433929 | DOI:10.1038/s42003-024-07080-x

Sci Rep. 2024 Oct 22;14(1):24798. doi: 10.1038/s41598-024-76035-3.

ABSTRACT

Due to the development of magnetic resonance (MR) imaging processing technology, image-based identification of endolymphatic hydrops (EH) has played an important role in understanding inner ear illnesses, such as Meniere's disease or fluctuating sensorineural hearing loss. We segmented the inner ear, consisting of the cochlea, vestibule, and semicircular canals, using a 3D-based deep neural network model for accurate and automated EH volume ratio calculations. We built a dataset of MR cisternography (MRC) and HYDROPS-Mi2 stacks labeled with the segmentation of the perilymph fluid space and endolymph fluid space of the inner ear to devise a 3D segmentation deep neural network model. End-to-end learning was used to segment the perilymph fluid and the endolymph fluid spaces simultaneously using aligned pair data of the MRC and HYDROPS-Mi2 stacks. Consequently, the segmentation performance of the total fluid space and endolymph fluid space had Dice similarity coefficients of 0.9574 and 0.9186, respectively. In addition, the EH volume ratio calculated by experienced otologists and the EH volume ratio value predicted by the proposed deep learning model showed high agreement according to the interclass correlation coefficient (ICC) and Bland-Altman plot analysis.

PMID:39433848 | DOI:10.1038/s41598-024-76035-3

Sci Rep. 2024 Oct 21;14(1):24724. doi: 10.1038/s41598-024-75700-x.

ABSTRACT

Proteins, nucleic acids, and lipids all interact with intrinsically disordered protein areas. Lipid-binding regions are involved in a variety of biological processes as well as a number of human illnesses. The expanding body of experimental evidence for these interactions and the dearth of techniques to anticipate them from the protein sequence serve as driving forces. Although large-scale laboratory techniques are considered to be essential for equipment for studying binding residues, they are time consuming and costly, making it challenging for researchers to predict lipid binding residues. As a result, computational techniques are being looked at as a different strategy to overcome this difficulty. To predict disordered lipid-binding residues (DLBRs), we proposed iDLB-Pred predictor utilizing benchmark dataset to compute feature through extraction techniques to identify relevant patterns and information. Various classification techniques, including deep learning methods such as Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Multilayer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs), were employed for model training. The proposed model, iDLB-Pred, was rigorously validated using metrics such as accuracy, sensitivity, specificity, and Matthew's correlation coefficient. The results demonstrate the predictor's exceptional performance, achieving accuracy rates of 81% on an independent dataset and 86% in 10-fold cross-validation.

PMID:39433833 | DOI:10.1038/s41598-024-75700-x

Sci Rep. 2024 Oct 21;14(1):24771. doi: 10.1038/s41598-024-75901-4.

ABSTRACT

Ulcerative Colitis is an Inflammatory Bowel disease caused by a variety of factors that lead to a serious impact on the quality of life of the patients if left untreated. Due to complexities in the identification procedures of this disease, the treatment timeline and quality can be severely affected, leading to further consequences for the sufferer. The difficulties in identification are due to high patients to healthcare professionals ratio. Researchers have proposed variety of machine/deep learning methods for automated detection of ulcerative colitis, however, several challenges exists including class imbalance problem, comprehensive feature extraction and accurate classification. We propose a novel method for accurate detection of ulcerative colitis with augmentation techniques to overcome class imbalance issue, a comprehensive feature vector extraction using custom architecture of Vision Transformer (ViT) and accurate classification using customized Convolutional Neural Network (CNN). We used the TMC-UCM and LIMUC datasets in this research for training and testing of proposed method and achieved accuracy of 90% with AUC-ROC scores of 0.91, 0.81, 0.94, and 0.94 for the endoscopic classes of Mayo 0, Mayo 1, Mayo 2, and Mayo 3 respectively. We have compared the proposed method with existing state of the art methods and conclude that the proposed method outperforms the existing methods.

PMID:39433818 | DOI:10.1038/s41598-024-75901-4

Sci Rep. 2024 Oct 21;14(1):24688. doi: 10.1038/s41598-024-75522-x.

ABSTRACT

In the field of intelligent fault diagnosis, particularly concerning rotating machinery, convolutional neural networks (CNNs) face significant challenges when applied to real industrial vibration data. These data are not only contaminated by various types of noise but also exhibit fault features that vary across different scales. Consequently, the effective suppression of extraneous noise and accurate extraction of multi-scale fault features are crucial issues. To address these challenges, this study proposes a novel deep neural network framework, termed the Multidimensional Fusion Residual Attention Network (MFRANet), for gearbox fault diagnosis. The MFRANet employs a multi-scale deep separable convolution module to thoroughly investigate the fundamental characteristics of the original vibration signals in both the time and time-frequency domains. To enhance the detailed analysis of diagnostic data and mitigate the risks of overfitting and noise interference, an efficient residual channel attention module is incorporated to weight and denoise the feature maps. Additionally, an external attention module is introduced to create implicit connections between the denoised multi-scale feature maps and to highlight potential correlations within the sample data, thereby improving the accuracy of fault diagnosis. Experimental evaluations on a gearbox fault dataset demonstrate that the proposed method surpasses several benchmark and state-of-the-art techniques in terms of diagnostic performance, exhibiting robust noise resilience across various noise levels. This indicates enhanced reliability and accuracy in gearbox fault diagnosis, providing an innovative and efficient solution for fault diagnosis in rotating machinery. The study underscores the contributions of artificial intelligence through the innovative structure of the method and the integration of advanced deep learning modules, while its engineering application is evidenced by addressing practical challenges in rotating machinery fault diagnosis. This work meets the urgent need for reliable diagnostic methods in industrial environments.

PMID:39433807 | DOI:10.1038/s41598-024-75522-x

Sci Rep. 2024 Oct 21;14(1):24715. doi: 10.1038/s41598-024-73510-9.

ABSTRACT

Insider threats pose a significant challenge in cybersecurity, demanding advanced detection methods for effective risk mitigation. This paper presents a comparative evaluation of data imbalance addressing techniques for CNN-based insider threat detection. Specifically, we integrate Convolutional Neural Networks (CNN) with three popular data imbalance addressing techniques: Synthetic Minority Over-sampling Technique (SMOTE), Borderline-SMOTE, and Adaptive Synthetic Sampling (ADASYN). The objective is to enhance insider threat detection accuracy and robustness in imbalanced datasets common to cybersecurity domains. Our study addresses the lack of consensus in the literature regarding the superiority of data imbalance addressing techniques in this field. We analyze a human behavior-based dataset (i.e., CERT) that reports users' Information Technology (IT) activities with a substantial number of samples to provide a clear conclusion on the effectiveness of these balancing techniques when coupled with CNN. Experimental results demonstrate that ADASYN, in conjunction with CNN, achieves a ROC curve of 96%, surpassing SMOTE and Borderline-SMOTE in enhancing detection accuracy in imbalanced datasets. We compare the results of these three hybrid models (CNN + imbalance addressing techniques) with state-of-the-art selective studies focusing on ROC, recall, and accuracy measures. Our findings contribute to the advancement of insider threat detection methodologies.

PMID:39433789 | DOI:10.1038/s41598-024-73510-9