NPJ Precis Oncol. 2025 Feb 12;9(1):45. doi: 10.1038/s41698-025-00827-7.

NO ABSTRACT

PMID:39939705 | DOI:10.1038/s41698-025-00827-7

Sci Rep. 2025 Feb 12;15(1):5237. doi: 10.1038/s41598-025-89267-8.

ABSTRACT

Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples, significantly hindering the development of deep learning technologies in high-security domains. A key challenge is that current defense methods often lack universality, as they are effective only against certain types of adversarial attacks. This study addresses this challenge by focusing on analyzing adversarial examples through changes in model attention, and classifying attack algorithms into attention-shifting and attention-attenuation categories. Our main novelty lies in proposing two defense modules: the Feature Pyramid-based Attention Space-guided (FPAS) module to counter attention-shifting attacks, and the Attention-based Non-Local (ANL) module to mitigate attention-attenuation attacks. These modules enhance the model's defense capability with minimal intrusion into the original model. By integrating FPAS and ANL into the Wide-ResNet model within a boosting framework, we demonstrate their synergistic defense capability. Even when adversarial examples are embedded with patches, our models showed significant improvements over the baseline, enhancing the average defense rate by 5.47% and 7.74%, respectively. Extensive experiments confirm that this universal defense strategy offers comprehensive protection against adversarial attacks at a lower implementation cost compared to current mainstream defense methods, and is also adaptable for integration with existing defense strategies to further enhance adversarial robustness.

PMID:39939692 | DOI:10.1038/s41598-025-89267-8

Sci Rep. 2025 Feb 12;15(1):5202. doi: 10.1038/s41598-025-89219-2.

ABSTRACT

In this study, we investigated whether deep learning-based prediction of immediate implant placement is possible. Panoramic radiographs of 201 patients with 874 teeth (Group 1: 440 teeth difficult to place implant immediately after extraction, Group 2: 434 teeth possible of immediate implant placement after extraction) for extraction were evaluated for the training and testing of a deep learning model. DenseNet121, ResNet18, ResNet101, ResNeXt101, InceptionNetV3, and InceptionResNetV2 were used. Each model was trained using preprocessed dental data, and the dataset was divided into training, validation, and test sets to evaluate model performance. For each model, the sensitivity, precision, accuracy, balanced accuracy, and F1-score were all greater than 0.90. The results of this study confirm that deep-learning-based prediction of the possibility of immediate implant placement is possible at a fairly accurate level.

PMID:39939654 | DOI:10.1038/s41598-025-89219-2

Sci Rep. 2025 Feb 12;15(1):5173. doi: 10.1038/s41598-024-82405-8.

ABSTRACT

With the advent of new media, more people are turning to social media to share thoughts and emotions related to personal life experiences. We examined salient concerns of dementia caregivers on Twitter pre- and post-pandemic, aiming to shed light on how to better support and engage dementia caregivers post-COVID-19 pandemic. English tweets related to "dementia" and "caregiver" were extracted between 1st January 2013 and 31st December 2022. A supervised deep learning model (Bidirectional Encoder Representations from Transformers, BERT) was trained to select tweets describing individual's experience related to dementia caregiving. An unsupervised deep learning approach (BERT-based topic modelling) was applied to identify topics from selected tweets, with each topic further grouped into themes manually using thematic analysis. A total of 44,527 tweets were analysed, and stratified using the emergence of COVID-19 pandemic as a threshold. Three themes were derived: challenges of caregiving in dementia, strategies to inspire caregivers, and dementia-related stigmatization. Over time, there is a rising trend of tweets relating to dementia caregiving. Post-pandemic, challenges of caregiving remained the top discussed topic; with a notable increase in tweets related to dementia-related stigmatization (p < 0.001), especially in North America and other continents (and less so in Europe). The findings uncover a worrying trend of growing dementia-related stigmatization among the caregivers, manifested by caregivers internalizing publicly-held stigma and projecting negative stereotypes externally as a means to devalue others. The challenges faced by caregivers also remained a significant concern, highlighting the need for continued support and resources for caregivers even post-pandemic.

PMID:39939632 | DOI:10.1038/s41598-024-82405-8

Sci Rep. 2025 Feb 12;15(1):5300. doi: 10.1038/s41598-025-86777-3.

ABSTRACT

The quality control of investment casting remains a critical challenge due to defect detection, real-time processing, and data traceability inefficiencies. This study presents an innovative Blockchain-integrated IoT system for advanced inspection of casting defects, combining a ResNet-based deep learning model for defect detection and dimensional measurement with Blockchain technology to ensure data integrity and traceability. The system demonstrated a significant improvement in defect detection accuracy, achieving an F1-score of 0.94, alongside high data integrity (0.99) and traceability (0.98) metrics. Additionally, it processes each casting in an average of 2.3 s, supporting a throughput of 26 castings per minute. By addressing critical challenges in smart manufacturing, this approach enhances operational efficiency, regulatory compliance, and user confidence. While scalability and energy efficiency remain areas for improvement, the proposed method provides a transformative solution for Industry 4.0, fostering transparency and reliability in manufacturing processes.

PMID:39939622 | DOI:10.1038/s41598-025-86777-3

Environ Sci Pollut Res Int. 2025 Feb 13. doi: 10.1007/s11356-025-36087-y. Online ahead of print.

NO ABSTRACT

PMID:39939571 | DOI:10.1007/s11356-025-36087-y

J Imaging Inform Med. 2025 Feb 12. doi: 10.1007/s10278-025-01431-8. Online ahead of print.

ABSTRACT

Accurate vocal fold (VF) pose estimation is crucial for diagnosing larynx diseases that can eventually lead to VF paralysis. The videoendoscopic examination is used to assess VF motility, usually estimating the change in the anterior glottic angle (AGA). This is a subjective and time-consuming procedure requiring extensive expertise. This research proposes a deep learning framework to estimate VF pose from laryngoscopy frames acquired in the actual clinical practice. The framework performs heatmap regression relying on three anatomically relevant keypoints as a prior for AGA computation, which is estimated from the coordinates of the predicted points. The assessment of the proposed framework is performed using a newly collected dataset of 471 laryngoscopy frames from 124 patients, 28 of whom with cancer. The framework was tested in various configurations and compared with other state-of-the-art approaches (direct keypoints regression and glottal segmentation) for both pose estimation, and AGA evaluation. The proposed framework obtained the lowest root mean square error (RMSE) computed on all the keypoints (5.09, 6.56, and 6.40 pixels, respectively) among all the models tested for VF pose estimation. Also for the AGA evaluation, heatmap regression reached the lowest mean average error (MAE) ( 5 . 87 ∘ ). Results show that relying on keypoints heatmap regression allows to perform VF pose estimation with a small error, overcoming drawbacks of state-of-the-art algorithms, especially in challenging images such as pathologic subjects, presence of noise, and occlusion.

PMID:39939476 | DOI:10.1007/s10278-025-01431-8

Sci Rep. 2025 Feb 13;15(1):5319. doi: 10.1038/s41598-025-89732-4.

ABSTRACT

Complex behavior is supported by the coordination of multiple brain regions. How do brain regions coordinate absent a homunculus? We propose coordination is achieved by a controller-peripheral architecture in which peripherals (e.g., the ventral visual stream) aim to supply needed inputs to their controllers (e.g., the hippocampus and prefrontal cortex) while expending minimal resources. We developed a formal model within this framework to address how multiple brain regions coordinate to support rapid learning from a few example images. The model captured how higher-level activity in the controller shaped lower-level visual representations, affecting their precision and sparsity in a manner that paralleled brain measures. In particular, the peripheral encoded visual information to the extent needed to support the smooth operation of the controller. Alternative models optimized by gradient descent irrespective of architectural constraints could not account for human behavior or brain responses, and, typical of standard deep learning approaches, were unstable trial-by-trial learners. While previous work offered accounts of specific faculties, such as perception, attention, and learning, the controller-peripheral approach is a step toward addressing next generation questions concerning how multiple faculties coordinate.

PMID:39939457 | DOI:10.1038/s41598-025-89732-4

Eur Radiol. 2025 Feb 13. doi: 10.1007/s00330-025-11422-6. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aimed to develop a Hashimoto's thyroiditis nodule-artificial intelligence (HTN-AI) model to optimize the diagnosis of thyroid nodules with Hashimoto's thyroiditis (HT) of which the efficiency and accuracy remain challenging.

DESIGN AND METHODS: This study included 5709 patients from 10 hospitals between January 2014 and March 2024. Among them, 5053 thyroid nodules were divided into training and testing sets in a 9:1 ratio. Then, we tested the model on an external dataset (n = 432). Finally, we prospectively recruited 224 patients with dynamic ultrasound videos acquired and employed the HTN-AI model to identify nodules from the dynamic ultrasound videos. Radiologists of varying seniority performed the categorization of thyroid nodules as benign and malignant, both with and without the assistance of the HTN-AI model, and their diagnostic performances were compared.

RESULTS: The results indicated that for the external testing set, the HTN-AI model achieved a Dice similarity coefficient (DSC) of 0.91, outperforming several other common convolutional neural network (CNN) models. Specifically, the DSCs of the HTN-AI model were similar for thyroid nodule patients with and without HT which were 0.91 ± 0.06 and 0.91 ± 0.09. Moreover, when the HTN-AI model was used to assist diagnosis, it demonstrated an improvement in the diagnostic performance of radiologists. The diagnostic areas under the receiver operating characteristic curve (AUCs) of the junior radiologists increased from 0.59, 0.59, and 0.57 to 0.68, 0.65, and 0.65.

CONCLUSIONS: This research demonstrates that the HTN-AI model has excellent performance in identifying thyroid nodules associated with HT and can assist radiologists with more accurate and efficient diagnoses of thyroid nodules.

KEY POINTS: Question The study developed an HTN-AI model aimed at assisting in the diagnosis of thyroid nodules in patients with HT. Findings The HTN-AI model achieved great performance with a Dice similarity coefficient (DSC) of 0.91, and consistent performance across patients with and without HT. Clinical relevance The HTN-AI model enhances the accuracy and efficiency of thyroid nodule diagnosis, particularly in patients with HT. By assisting radiologists at varying experience levels, this model supports improved decision-making in the management of thyroid nodules.

PMID:39939425 | DOI:10.1007/s00330-025-11422-6

PLoS One. 2025 Feb 12;20(2):e0318560. doi: 10.1371/journal.pone.0318560. eCollection 2025.

ABSTRACT

Variability of myoelectric activity during walking is the result of human capability to adapt to both intrinsic and extrinsic perturbations. The availability of sEMG signals lasting at least some minutes (instead of seconds) is needed to comprehensively analyze the variability of surface electromyographic (sEMG) signals. The current study introduces a dataset of long-lasting sEMG signals recorded during walking sessions of 31 healthy subjects, aged between 20 and 30 years, conducted at the Movement Analysis Lab of Università Politecnica delle Marche, Ancona, Italy. The sEMG signals were captured from ten distinct lower-limb muscles (five per leg), including gastrocnemius lateralis (GL), tibialis anterior (TA), rectus femoris (RF), hamstrings (Ham), and vastus lateralis (VL). Synchronized electrogoniometric and foot-floor-contact signals are also supplied to enable the spatial/temporal analysis of the sEMG signals. The experimental procedure involves subjects walking barefoot on level ground for approximately 5 minutes at their natural speed and pace, following an eight-shaped path featuring linear diagonal segments, curves, accelerations, and decelerations. An advanced analysis of the sEMG signals was performed to test the reliability and usability of the current dataset. The considerable duration of the signals makes this dataset particularly useful for studies where a significant volume of data is crucial, such as machine/deep learning approaches, investigations examining the variability of muscle recruitment during physiological walking, validations of the reliability of novel sEMG-based algorithms, and assembly of reference datasets for pathological condition characterization.

PMID:39937870 | DOI:10.1371/journal.pone.0318560

PLoS One. 2025 Feb 12;20(2):e0316253. doi: 10.1371/journal.pone.0316253. eCollection 2025.

ABSTRACT

Intrusion detection plays a significant role in the provision of information security. The most critical element is the ability to precisely identify different types of intrusions into the network. However, the detection of intrusions poses a important challenge, as many new types of intrusion are now generated by cyber-attackers every day. A robust system is still elusive, despite the various strategies that have been proposed in recent years. Hence, a novel deep-learning-based architecture for detecting intrusions into a computer network is proposed in this paper. The aim is to construct a hybrid system that enhances the efficiency and accuracy of intrusion detection. The main contribution of our work is a novel deep learning-based hybrid architecture in which PSO is used for hyperparameter optimisation and three well-known pre-trained network models are combined in an optimised way. The suggested method involves six key stages: data gathering, pre-processing, deep neural network (DNN) architecture design, optimisation of hyperparameters, training, and evaluation of the trained DNN. To verify the superiority of the suggested method over alternative state-of-the-art schemes, it was evaluated on the KDDCUP'99, NSL-KDD and UNSW-NB15 datasets. Our empirical findings show that the proposed model successfully and correctly classifies different types of attacks with 82.44%, 90.42% and 93.55% accuracy values obtained on UNSW-B15, NSL-KDD and KDDCUP'99 datasets, respectively, and outperforms alternative schemes in the literature.

PMID:39937819 | DOI:10.1371/journal.pone.0316253

Gigascience. 2025 Jan 6;14:giae123. doi: 10.1093/gigascience/giae123.

ABSTRACT

Deep learning can revolutionise high-throughput image-based phenotyping by automating the measurement of complex traits, a task that is often labour-intensive, time-consuming, and prone to human error. However, its precision and adaptability in accurately phenotyping organ-level traits, such as fruit morphology, remain to be fully evaluated. Establishing the links between phenotypic and genotypic variation is essential for uncovering the genetic basis of traits and can also provide an orthologous test of pipeline effectiveness. In this study, we assess the efficacy of deep learning for measuring variation in fruit morphology in Arabidopsis using images from a multiparent advanced generation intercross (MAGIC) mapping family. We trained an instance segmentation model and developed a pipeline to phenotype Arabidopsis fruit morphology, based on the model outputs. Our model achieved strong performance with an average precision of 88.0% for detection and 55.9% for segmentation. Quantitative trait locus analysis of the derived phenotypic metrics of the MAGIC population identified significant loci associated with fruit morphology. This analysis, based on automated phenotyping of 332,194 individual fruits, underscores the capability of deep learning as a robust tool for phenotyping large populations. Our pipeline for quantifying pod morphological traits is scalable and provides high-quality phenotype data, facilitating genetic analysis and gene discovery, as well as advancing crop breeding research.

PMID:39937596 | DOI:10.1093/gigascience/giae123

J Synchrotron Radiat. 2025 Mar 1. doi: 10.1107/S1600577525000323. Online ahead of print.

ABSTRACT

Full-field ultra-high-speed (UHS) X-ray imaging experiments have been well established to characterize various processes and phenomena. However, the potential of UHS experiments through the joint acquisition of X-ray videos with distinct configurations has not been fully exploited. In this paper, we investigate the use of a deep learning-based spatio-temporal fusion (STF) framework to fuse two complementary sequences of X-ray images and reconstruct the target image sequence with high spatial resolution, high frame rate and high fidelity. We applied a transfer learning strategy to train the model and compared the peak signal-to-noise ratio (PSNR), average absolute difference (AAD) and structural similarity (SSIM) of the proposed framework on two independent X-ray data sets with those obtained from a baseline deep learning model, a Bayesian fusion framework and the bicubic interpolation method. The proposed framework outperformed the other methods with various configurations of the input frame separations and image noise levels. With three subsequent images from the low-resolution (LR) sequence of a four times lower spatial resolution and another two images from the high-resolution (HR) sequence of a 20 times lower frame rate, the proposed approach achieved average PSNRs of 37.57 dB and 35.15 dB, respectively. When coupled with the appropriate combination of high-speed cameras, the proposed approach will enhance the performance and therefore the scientific value of UHS X-ray imaging experiments.

PMID:39937516 | DOI:10.1107/S1600577525000323

Forensic Sci Med Pathol. 2025 Feb 12. doi: 10.1007/s12024-025-00962-4. Online ahead of print.

ABSTRACT

PURPOSE: This study aimed to develop an improved method for forensic age estimation using deep learning models applied to orthopantomography (OPG) images, focusing on distinguishing individuals under 12 years old from those aged 12 and above.

METHODS: A dataset of 1941 pediatric patients aged between five and 15 years was collected from two radiology departments. The primary research question addressed the identification of the most effective deep learning model for this task. Various deep learning models including Xception, ResNet, ShuffleNet, InceptionV3, DarkNet, NasNet, DenseNet, EfficientNet, MobileNet, ResNet18, GoogleNet, SqueezeNet, and AlexNet were evaluated using traditional metrics like Classification Accuracy (CA), Sensitivity (SE), Specificity (SP), Kappa (K), Area Under the Curve (AUC), alongside a novel Polygon Area Metric (PAM) designed to handle imbalanced datasets common in forensic applications.

RESULTS: "Forensic Xception" model derived from Xception outperformed others, achieving a PAM score of 0.8828. This model demonstrated superior performance in accurately classifying individuals' age groups, with high CA, SE, SP, K, AUC, and F1 Score. Notably, the introduction of the PAM metric provided a comprehensive evaluation of classifier performance.

CONCLUSION: This study represents a significant advancement in forensic age estimation from OPG images, emphasizing the potential of deep learning models, particularly the "Forensic Xception" model, in accurately classifying individuals based on age, especially in legal contexts. This research suggests a promising avenue for further advancements in forensic dental age estimation, with future studies encouraged to explore additional datasets, refine models, and address ethical and legal considerations.

PMID:39937388 | DOI:10.1007/s12024-025-00962-4

Discov Oncol. 2025 Feb 12;16(1):165. doi: 10.1007/s12672-025-01942-4.

ABSTRACT

PURPOSE: In this work, we propose to develop a 3D U-Net-based deep learning model that accurately predicts the dose distribution for breast cancer radiotherapy.

METHODS: This study included 176 breast cancer patients, divided into training, validating and testing sets. A deep learning model based on the 3D U-Net architecture was developed to predict dose distribution, which employed a double encoder combination attention (DECA) module, a cross stage partial + Resnet + Attention (CRA) module, a difficulty perception and a critical regions loss. The performance and generalization ability of this model were evaluated by the voxel mean absolute error (MAE), several clinically relevant dosimetric indexes and 3D gamma passing rates.

RESULTS: Our model accurately predicted the 3D dose distributions with each dosage level mirroring the clinical reality in shape. The generated dose-volume histogram (DVH) matched with the ground truth curve. The total dose error of our model was below 1.16 Gy, complying with clinical usage standards. When compared to other exceptional models, our model optimally predicted eight out of nine regions, and the prediction errors for the first planning target volume (PTV1) and PTV2 were merely 1.03 Gy and 0.74 Gy. Moreover, the mean 3%/3 mm 3D gamma passing rates for PTV1, PTV2, Heart and Lung L achieved 91.8%, 96.4%, 91.5%, and 93.2%, respectively, surpassing the other models and meeting clinical standards.

CONCLUSIONS: This study developed a new deep learning model based on 3D U-Net that can accurately predict dose distributions for breast cancer radiotherapy, which can improve the quality and planning efficiency.

PMID:39937302 | DOI:10.1007/s12672-025-01942-4

Eur Radiol. 2025 Feb 12. doi: 10.1007/s00330-025-11401-x. Online ahead of print.

ABSTRACT

OBJECTIVE: To systematically evaluate glioma radiomics literature on differentiating between radiation-induced brain injury and tumor recurrence.

METHODS: Literature was searched on PubMed and Web of Science (end date: May 7, 2024). Quality of eligible papers was assessed using METhodological RadiomICs Score (METRICS) and Radiomics Quality Score (RQS). Reliability of quality scoring tools were analyzed. Meta-analysis, meta-regression, and subgroup analysis were performed.

RESULTS: Twenty-seven papers were included in the qualitative assessment. Mean average METRICS score and RQS percentage score across three readers was 57% (SD, 14%) and 16% (SD, 12%), respectively. Score-wise inter-rater agreement for METRICS ranged from poor to excellent, while RQS demonstrated moderate to excellent agreement. Item-wise agreement was moderate for both tools. Meta-analysis of 11 eligible studies yielded an estimated area under the receiver operating characteristic curve of 0.832 (95% CI, 0.757-0.908), with significant heterogeneity (I2 = 91%) and no statistical publication bias (p = 0.051). Meta-regression did not identify potential sources of heterogeneity. Subgroup analysis revealed high heterogeneity across all subgroups, with the lowest I2 at 68% in studies with proper validation and higher quality scores. Statistical publication bias was generally not significant, except in the subgroup with the lowest heterogeneity (p = 0.044). However, most studies in both qualitative analysis (26/27; 96%) and primary meta-analysis (10/11; 91%) reported positive effects of radiomics, indicating high non-statistical publication bias.

CONCLUSION: While a good performance was noted for radiomics, results should be interpreted cautiously due to heterogeneity, publication bias, and quality issues thoroughly examined in this study.

KEY POINTS: Question Radiomic literature on distinguishing radiation-induced brain injury from glioma recurrence lacks systematic reviews and meta-analyses that assess methodological quality using radiomics-specific tools. Findings While the results are encouraging, there was substantial heterogeneity, publication bias toward positive findings, and notable concerns regarding methodological quality. Clinical relevance Meta-analysis results need cautious interpretation due to significant problems detected during the analysis (e.g., suboptimal quality, heterogeneity, bias), which may help explain why radiomics has not yet been translated into clinical practice.

PMID:39937273 | DOI:10.1007/s00330-025-11401-x

Abdom Radiol (NY). 2025 Feb 12. doi: 10.1007/s00261-025-04834-x. Online ahead of print.

ABSTRACT

The purpose of this study was to explore the association between structural changes in abdominal organs and visceral fat obesity (VFO) using a fully automated three-dimensional (3D) volumetric computed tomography (CT) measurement method based on deep learning algorithm. A total of 610 patients (295 men and 315 women; mean age, 68.4 years old) were included. Fully automated 3D volumetric CT measurements of the abdominal organs were performed to determine the volume and average CT attenuation values of each organ. All patients were divided into 2 groups based on the measured visceral fat area: the VFO group (≥ 100 cm2) and non-VFO group (< 100 cm2), and the structural changes in abdominal organs were compared between these groups. The volumes of all organs were significantly higher in the VFO group than in the non-VFO group (all of p < 0.001). Conversely, the CT attenuation values of all organs in the VFO group were significantly lower than those in the non-VFO group (all of p < 0.001). Pancreatic CT values (r = - 0.701, p < 0.001) were most strongly associated with the visceral fat, followed by renal CT values (r = - 0.525, p < 0.001) and hepatic CT values (r = - 0.510, p < 0.001). Fully automated 3D volumetric CT measurement using a deep learning algorithm has the potential to detect the structural changes in the abdominal organs, especially the pancreas, such as an increase in the volumes and a decrease in CT attenuation values, probably due to increased ectopic fat accumulation in patients with VFO. This technique may provide valuable imaging support for the early detection and intervention of metabolic-related diseases.

PMID:39937214 | DOI:10.1007/s00261-025-04834-x

Biomacromolecules. 2025 Feb 12. doi: 10.1021/acs.biomac.4c01243. Online ahead of print.

ABSTRACT

Natural topological proteins exhibit unique properties including enhanced stability, controlled quaternary structures, and dynamic switching properties, highlighting topology as a unique dimension in protein engineering. Although artificial design and synthesis of topological proteins have achieved certain success, their diversity and complexity remain rather limited due to the scarcity of available entangling motifs essential for the construction of nontrivial protein topologies. In this work, we developed a deep-learning model to predict the entanglement features of a homodimer based solely on its amino acid sequence via the Gauss linking number matrices. The model achieved a search speed that was dozens of times faster than AlphaFold-Multimer, while maintaining comparable mean squared error. It was used to screen for entangling motifs from the genome of a hyperthermophilic archaeon. We demonstrated the effectiveness of our model by successful wet-lab synthesis of protein catenanes using two candidate entangling motifs. These findings show the great potential of our model for advancing the design and synthesis of novel topological proteins.

PMID:39937127 | DOI:10.1021/acs.biomac.4c01243

J Med Eng Technol. 2025 Feb 12:1-14. doi: 10.1080/03091902.2025.2462310. Online ahead of print.

ABSTRACT

This research aimed to develop an algorithm for classifying scalogram images generated from electromyography data of patients with Rheumatoid Arthritis and Prolapsed Intervertebral Disc. Electromyography is valuable for assessing muscle function and diagnosing neurological disorders, but limitations, such as background noise, cross-talk, and inter-subject variability complicate the interpretation and assessment. To mitigate this, the present study uses scalogram images and attention-network architecture. The algorithm utilises a combination of features extracted from an attention module and a convolution feature module, followed by classification using a Convolutional Neural Network classifier. A comparison of eight alternative architectures, including individual implementations of attention and convolution filters and a Convolutional Neural Network-only model, shows that the hybrid Convolutional Neural Network model proposed in this study outperforms the others. The model exhibits excellent discriminatory ability between gait abnormalities with an accuracy of 96.7%, a precision of 95.2%, a recall of 94.8%, and an Area Under Curve of 0.99. These findings suggest that the proposed model is highly accurate in classifying scalogram images of electromyography signals and may have significant clinical implications for early diagnosis and treatment planning.

PMID:39936825 | DOI:10.1080/03091902.2025.2462310

Rev Soc Bras Med Trop. 2025 Feb 7;58:e004012024. doi: 10.1590/0037-8682-0178-2024. eCollection 2025.

ABSTRACT

BACKGROUND: Emerging infections have posed persistent threats to humanity throughout history. Rapid and unprecedented anthropogenic, behavioral, and social transformations witnessed in the past century have expedited the emergence of novel pathogens, intensifying their impact on the global human population.

METHODS: This study aimed to comprehensively analyze and compare the genomic sequences of four distinct viruses: SARS-CoV-2, SARS, MERS, and Ebola. Advanced genomic sequencing techniques and a Gated Recurrent Unit-based deep learning model were used to examine the intricate genetic makeup of these viruses. The proposed study sheds light on their evolutionary dynamics, transmission patterns, and pathogenicity and contributes to the development of effective diagnostic and therapeutic interventions.

RESULTS: This model exhibited exceptional performance as evidenced by accuracy values of 99.01%, 98.91%, 98.35%, and 98.04% for SARS-CoV-2, SARS, MERS, and Ebola respectively. Precision values ranged from 98.1% to 98.72%, recall values consistently surpassed 92%, and F1 scores ranged from 95.47% to 96.37%.

CONCLUSIONS: These results underscore the robustness of this model and its potential utility in genomic analysis, paving the way for enhanced understanding, preparedness, and response to emerging viral threats. In the future, this research will focus on creating better diagnostic instruments for the early identification of viral illnesses, developing vaccinations, and tailoring treatments based on the genetic composition and evolutionary patterns of different viruses. This model can be modified to examine a more extensive variety of diseases and recently discovered viruses to predict future outbreaks and their effects on global health.

PMID:39936709 | DOI:10.1590/0037-8682-0178-2024