Front Med (Lausanne). 2025 May 13;12:1599340. doi: 10.3389/fmed.2025.1599340. eCollection 2025.

ABSTRACT

Nanomedicines are nanoscale, biocompatible materials that offer promising alternatives to conventional treatment options for brain disorders. The recent technological developments in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), are transforming the nanomedicine field by improving disease diagnosis, biomarker identification, prognostic assessment and disease monitoring, targeted drug delivery, and therapeutic intervention as well as contributing to computational and methodological developments. These advancements can be achieved by analysis of large clinical datasets and facilitating the design and optimization of nanomaterials for in vivo testing. Such advancement offers exciting possibilities for the improvement in the management of brain disorders, including brain cancer, Alzheimer's disease, Parkinson's disease, and multiple sclerosis, where early diagnosis, targeted delivery, and effective treatment strategies remain a great challenge. This review article provides an overview of recent advances in AI-based nanomedicine development to accelerate effective and quick diagnosis, biomarker identification, prognosis, drug delivery, methodological advancement and patient-specific therapies for managing brain disorders.

PMID:40432717 | PMC:PMC12106332 | DOI:10.3389/fmed.2025.1599340

Sci Data. 2025 May 27;12(1):872. doi: 10.1038/s41597-025-04906-z.

ABSTRACT

Pathology data, primarily consisting of slides and diagnostic reports, inherently contain knowledge that is pivotal for advancing data-driven biomedical research and clinical practice. However, the hidden and fragmented nature of this knowledge across various data modalities not only hinders its computational utilization, but also impedes the effective integration of AI technologies within the domain of pathology. To systematically organize pathology knowledge for its computational use, we propose PathoGraph, a knowledge representation method that describes pathology knowledge in a graph-based format. PathoGraph can represent: (1) pathological entities' types and morphological features; (2) the composition, spatial arrangements, and dynamic behaviors associated with pathological phenotypes; and (3) the differential diagnostic approaches used by pathologists. By applying PathoGraph to neoplastic diseases, we illustrate its ability to comprehensively and structurally capture multi-scale disease characteristics alongside pathologists' expertise. Furthermore, we validate its computational utility by demonstrating the feasibility of large-scale automated PathoGraph construction, showing performance improvements in downstream deep learning tasks, and presenting two illustrative use cases that highlight its clinical potential. We believe PathoGraph opens new avenues for AI-driven advances in the field of pathology.

PMID:40425649 | DOI:10.1038/s41597-025-04906-z

Thorac Cancer. 2025 May;16(10):e70048. doi: 10.1111/1759-7714.70048.

ABSTRACT

BACKGROUND: Lymph node metastasis (LNM) poses a considerable threat to survival in lung adenocarcinoma. Currently, minor resection is the recommended surgical approach for small-diameter lung cancer. The accurate preoperative identification of LNM in patients with small-diameter lung cancer is important for improving patient survival and outcomes.

METHODS: A total of 1740 patients with clinical early-stage lung adenocarcinoma who underwent surgical resection were enrolled in this study. The Lasso model was used to screen clinical and imaging features, and multivariate logistic regression analysis was used to analyze the relevant diagnostic factors to establish a diagnostic model for predicting LNM. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and calibration curve analysis were used to verify the clinical efficacy of the model, which was further validated with an internal validation set.

RESULTS: The proportion of solid components (PSC), sphericity, nodule margin, entropy, and edge blur were identified as diagnostic factors that were strongly correlated with LNM in lung adenocarcinoma patients. The area under the ROC curve (AUC) in the internal training set was 0.91. Decision curve analysis revealed that the model could achieve greater benefits for patients. The calibration curve was used to further verify the applicability of the prediction model.

CONCLUSIONS: Patients with early-stage lung adenocarcinoma with LNM can be identified by typical imaging features. The diagnostic model can help to optimize surgical planning among thoracic surgeons.

PMID:40425526 | DOI:10.1111/1759-7714.70048

J Neural Eng. 2025 May 27. doi: 10.1088/1741-2552/addd4a. Online ahead of print.

ABSTRACT

Objective: Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive processes with an unparalleled combination of high temporal and spatial precision. While MEG data analytics have traditionally relied on advanced signal processing and mathematical and statistical tools, the recent surge in Artificial Intelligence (AI) has led to the growing use of Machine Learning (ML) methods for MEG data classification. An emerging trend in this field is the use of Artificial Neural Networks (ANNs) to address various MEG-related tasks. This review aims to provide a comprehensive overview of the state of the art in this area.Approach: This topical review included studies that applied ANNs to MEG data. Studies were sourced from PubMed, Google Scholar, arXiv, and bioRxiv using targeted search queries. The included studies were categorized into three groups: Classification, Modeling, and Other. Key findings and trends were summarized to provide a comprehensive assessment of the field.Main Results:The review identified 119 relevant studies, with 69 focused on Classification, 16 on Modeling, and 34 in the Other category. Classification studies addressed tasks such as brain decoding, clinical diagnostics, and BCI implementations, often achieving high predictive accuracy. Modeling studies explored the alignment between ANN activations and brain processes, offering insights into the neural representations captured by these networks. The Other category demonstrated innovative uses of ANNs for artifact correction, preprocessing, and neural source localization.Significance: By establishing a detailed portrait of the current state of the field, this review highlights the strengths and current limitations of ANNs in MEG research. It also provides practical recommendations for future work, offering a helpful reference for seasoned researchers and newcomers interested in using ANNs to explore the complex dynamics of the human brain with MEG.

PMID:40425030 | DOI:10.1088/1741-2552/addd4a

BMC Med Imaging. 2025 May 27;25(1):194. doi: 10.1186/s12880-025-01712-2.

ABSTRACT

BACKGROUND: Pineal region tumors (PRTs) are rare but deep-seated brain tumors, and complete surgical resection is crucial for effective tumor treatment. The choice of surgical approach is often challenging due to the low incidence and deep location. This study aims to combine machine learning and deep learning algorithms with pre-operative MRI images to build a model for PRTs surgical approaches recommendation, striving to model clinical experience for practical reference and education.

METHODS: This study was a retrospective study which enrolled a total of 173 patients diagnosed with PRTs radiologically from our hospital. Three traditional surgical approaches of were recorded for prediction label. Clinical and VASARI related radiological information were selected for machine learning prediction model construction. And MRI images from axial, sagittal and coronal views of orientation were also used for deep learning craniotomy approach prediction model establishment and evaluation.

RESULTS: 5 machine learning methods were applied to construct the predictive classifiers with the clinical and VASARI features and all methods could achieve area under the ROC (Receiver operating characteristic) curve (AUC) values over than 0.7. And also, 3 deep learning algorithms (ResNet-50, EfficientNetV2-m and ViT) were applied based on MRI images from different orientations. EfficientNetV2-m achieved the highest AUC value of 0.89, demonstrating a significant high performance of prediction. And class activation mapping was used to reveal that the tumor itself and its surrounding relations are crucial areas for model decision-making.

CONCLUSION: In our study, we used machine learning and deep learning to construct surgical approach recommendation models. Deep learning could achieve high performance of prediction and provide efficient and personalized decision support tools for PRTs surgical approach.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40426149 | DOI:10.1186/s12880-025-01712-2

BMC Cancer. 2025 May 27;25(1):953. doi: 10.1186/s12885-025-14354-y.

ABSTRACT

BACKGROUND: Diffusion-weighted imaging with higher b-value improves detection rate for prostate cancer lesions. However, obtaining high b-value DWI requires more advanced hardware and software configuration. Here we use a novel deep learning network, NAFNet, to generate a deep learning reconstructed (DLR1500) images from 800 b-value to mimic 1500 b-value images, and to evaluate its performance and lesion detection improvements based on whole-slide images (WSI).

METHODS: We enrolled 303 prostate cancer patients with both 800 and 1500 b-values from Fudan University Shanghai Cancer Centre between 2017 and 2020. We assigned these patients to the training and validation set in a 2:1 ratio. The testing set included 36 prostate cancer patients from an independent institute who had only preoperative DWI at 800 b-value. Two senior radiology doctors and two junior radiology doctors read and delineated cancer lesions on DLR1500, original 800 and 1500 b-values DWI images. WSI were used as the ground truth to assess the lesion detection improvement of DLR1500 images in the testing set.

RESULTS: After training and generating, within junior radiology doctors, the diagnostic AUC based on DLR1500 images is not inferior to that based on 1500 b-value images (0.832 (0.788-0.876) vs. 0.821 (0.747-0.899), P = 0.824). The same phenomenon is also observed in senior radiology doctors. Furthermore, in the testing set, DLR1500 images could significantly enhance junior radiology doctors' diagnostic performance than 800 b-value images (0.848 (0.758-0.938) vs. 0.752 (0.661-0.843), P = 0.043).

CONCLUSIONS: DLR1500 DWIs were comparable in quality to original 1500 b-value images within both junior and senior radiology doctors. NAFNet based DWI enhancement can significantly improve the image quality of 800 b-value DWI, and therefore promote the accuracy of prostate cancer lesion detection for junior radiology doctors.

PMID:40426115 | DOI:10.1186/s12885-025-14354-y

J Imaging Inform Med. 2025 May 27. doi: 10.1007/s10278-025-01542-2. Online ahead of print.

ABSTRACT

Accurate assessment of computed tomography (CT) image quality is crucial for ensuring diagnostic accuracy, optimizing imaging protocols, and preventing excessive radiation exposure. In clinical settings, where high-quality reference images are often unavailable, developing no-reference image quality assessment (NR-IQA) methods is essential. Recently, CT-NR-IQA methods using deep learning have been widely studied; however, significant challenges remain in handling multiple degradation factors and accurately reflecting real-world degradations. To address these issues, we propose a novel CT-NR-IQA method. Our approach utilizes a dataset that combines two degradation factors (noise and blur) to train convolutional neural network (CNN) models capable of handling multiple degradation factors. Additionally, we leveraged RadImageNet pre-trained models (ResNet50, DenseNet121, InceptionV3, and InceptionResNetV2), allowing the models to learn deep features from large-scale real clinical images, thus enhancing adaptability to real-world degradations without relying on artificially degraded images. The models' performances were evaluated by measuring the correlation between the subjective scores and predicted image quality scores for both artificially degraded and real clinical image datasets. The results demonstrated positive correlations between the subjective and predicted scores for both datasets. In particular, ResNet50 showed the best performance, with a correlation coefficient of 0.910 for the artificially degraded images and 0.831 for the real clinical images. These findings indicate that the proposed method could serve as a potential surrogate for subjective assessment in CT-NR-IQA.

PMID:40425960 | DOI:10.1007/s10278-025-01542-2

J Imaging Inform Med. 2025 May 27. doi: 10.1007/s10278-025-01557-9. Online ahead of print.

ABSTRACT

Diffuse midline glioma (DMG) H3 K27M-altered is a rare pediatric brainstem cancer with poor prognosis. To advance the development of predictive models to gain a deeper understanding of DMG, there is a crucial need for seamlessly integrating automatic and highly accurate tumor segmentation techniques. There is only one method that tries to solve this task in this cancer; for that reason, this study develops a modified CNN-based 3D-Unet tool to automatically segment DMG in an accurate way in magnetic resonance (MR) images. The dataset consisted of 52 DMG patients and 70 images, each with T1W and T2W or FLAIR images. Three different datasets were created: T1W images, T2W or FLAIR images, and a combined set of T1W and T2W/FLAIR images. Denoising, bias field correction, spatial resampling, and normalization were applied as preprocessing steps to the MR images. Patching techniques were also used to enlarge the dataset size. For tumor segmentation, a 3D U-Net architecture with residual blocks was used. The best results were obtained for the dataset composed of all T1W and T2W/FLAIR images, reaching an average Dice Similarity Coefficient (DSC) of 0.883 on the test dataset. These results are comparable to other brain tumor segmentation models and to state-of-the-art results in DMG segmentation using fewer sequences. Our results demonstrate the effectiveness of the proposed 3D U-Net architecture for DMG tumor segmentation. This advancement holds potential for enhancing the precision of diagnostic and predictive models in the context of this challenging pediatric cancer.

PMID:40425959 | DOI:10.1007/s10278-025-01557-9

J Imaging Inform Med. 2025 May 27. doi: 10.1007/s10278-025-01549-9. Online ahead of print.

ABSTRACT

This study aimed to develop a fully automated semantic placenta segmentation model that integrates the U-Net and SegNeXt architectures through ensemble learning. A total of 218 pregnant women with suspected placental abnormalities who underwent magnetic resonance imaging (MRI) were enrolled, yielding 1090 annotated images for developing a deep learning model for placental segmentation. The images were standardized and divided into training and test sets. The performance of Placental Segmentation Network (PlaNet-S), which integrates U-Net and SegNeXt within an ensemble framework, was assessed using Intersection over Union (IoU) and counting connected components (CCC) against the U-Net, U-Net + + , and DS-transUNet. PlaNet-S had significantly higher IoU (0.78, SD = 0.10) than that of U-Net (0.73, SD = 0.13) (p < 0.005) and DS-transUNet (0.64, SD = 0.16) (p < 0.005), while the difference with U-Net + + (0.77, SD = 0.12) was not statistically significant. The CCC for PlaNet-S was significantly higher than that for U-Net (p < 0.005), U-Net + + (p < 0.005), and DS-transUNet (p < 0.005), matching the ground truth in 86.0%, 56.7%, 67.9%, and 20.9% of the cases, respectively. PlaNet-S achieved higher IoU than U-Net and DS-transUNet, and comparable IoU to U-Net + + . Moreover, PlaNet-S significantly outperformed all three models in CCC, indicating better agreement with the ground truth. This model addresses the challenges of time-consuming physician-assisted manual segmentation and offers the potential for diverse applications in placental imaging analyses.

PMID:40425958 | DOI:10.1007/s10278-025-01549-9

Aesthetic Plast Surg. 2025 May 27. doi: 10.1007/s00266-025-04924-7. Online ahead of print.

ABSTRACT

BACKGROUND: The biomechanics of frontalis muscle contraction and its interaction with skin remain contentious, particularly the debated bidirectional movement theory. This study introduces the quantum elasticity and resistance gradient (QERG) model to explain observed skin dynamics during frontalis contraction using elastic resistance principles.

METHODS: An AI-driven biomechanical model incorporating deep learning frameworks (TensorFlow, PyTorch) was developed to simulate skin deformation and muscle forces during frontalis contraction. The model was trained using 3D facial scans from a diverse cohort of 600 subjects, representing various ethnicities, genders, and ages. Resistance gradients and wrinkle formation were calculated using finite element analysis, and machine learning (random forest, deep neural networks) was employed to predict skin behaviour.

RESULTS: Cranial displacement averaged 6.9 mm across all subjects, with younger individuals (18-30 years) showing higher displacement than older individuals (50-65 years). Ethnic differences in displacement and wrinkle formation were observed, with Caucasians exhibiting greater displacement (7.3 mm) compared to African Americans and Asians (6.0 mm and 5.8 mm). The QERG model predicted skin folding at an average threshold of 41.2 mm above the eyebrows, with variations linked to ethnicity, age, and gender. AI models achieved high accuracy (R2 = 0.96), validating the model's predictive power.

CONCLUSION: The QERG model confirms that frontalis muscle contraction is unidirectional, with skin folding attributed to elastic resistance rather than opposing forces. These findings challenge previous theories of bidirectional contraction and have implications for aesthetic treatments.

LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

PMID:40425886 | DOI:10.1007/s00266-025-04924-7

Aesthetic Plast Surg. 2025 May 27. doi: 10.1007/s00266-025-04938-1. Online ahead of print.

NO ABSTRACT

PMID:40425882 | DOI:10.1007/s00266-025-04938-1

Nat Med. 2025 May 27. doi: 10.1038/s41591-025-03715-6. Online ahead of print.

ABSTRACT

There is limited evidence supporting the feasibility of using omics and functional technologies to inform treatment decisions. Here we present results from a cohort of 116 melanoma patients in the prospective, multicentric observational Tumor Profiler (TuPro) precision oncology project. Nine independent technologies, mostly at single-cell level, were used to analyze 126 patient samples, generating up to 500 Gb of data per sample (40,000 potential markers) within 4 weeks. Among established and experimental markers, the molecular tumor board selected 54 to inform its treatment recommendations. In 75% of cases, TuPro-based data were judged to be useful in informing recommendations. Patients received either standard of care (SOC) treatments or highly individualized, polybiomarker-driven treatments (beyond SOC). The objective response rate in difficult-to-treat palliative, beyond SOC patients (n = 37) was 38%, with a disease control rate of 54%. Progression-free survival of patients with TuPro-informed therapy decisions was 6.04 months, (95% confidence interval, 3.75-12.06) and 5.35 months (95% confidence interval, 2.89-12.06) in ≥third therapy lines. The proof-of-concept TuPro project demonstrated the feasibility and relevance of omics-based tumor profiling to support data-guided clinical decision-making. ClinicalTrials.gov identifier: NCT06463509 .

PMID:40425842 | DOI:10.1038/s41591-025-03715-6

Sci Rep. 2025 May 27;15(1):18556. doi: 10.1038/s41598-025-03010-x.

ABSTRACT

Alzheimer's disease (AD) is the most prevalent type of dementia. It is linked with a gradual decline in various brain functions, such as memory. Many research efforts are now directed toward non-invasive procedures for early diagnosis because early detection greatly benefits the patient care and treatment outcome. Additional to an accurate diagnosis and reduction of the rate of misdiagnosis; Computer-Aided Design (CAD) systems are built to give definitive diagnosis. This paper presents a novel CAD system to determine stages of AD. Initially, deep learning techniques are utilized to extract features from the AD brain MRIs. Then, the extracted features are reduced using a proposed feature reduction technique named Deep Downsized Kernel Partial Least Squares (DDKPLS). The proposed approach selects a reduced number of samples from the initial information matrix. The samples chosen give rise to a new data matrix further processed by KPLS to deal with the high dimensionality. The reduced feature space is finally classified using ELM. The implementation is named DDKPLS-ELM. Reference tests have been performed on the Kaggle MRI dataset, which exhibit the efficacy of the DDKPLS-based classifier; it achieves accuracy up to 95.4% and an F1 score of 95.1%.

PMID:40425715 | DOI:10.1038/s41598-025-03010-x

Sci Rep. 2025 May 27;15(1):18552. doi: 10.1038/s41598-025-02739-9.

ABSTRACT

Markerless motion capture (ML) systems, which utilize deep learning algorithms, have significantly expanded the applications of biomechanical analysis. Jump tests are now essential tools for athlete monitoring and injury prevention. However, the validity of kinematic and kinetic parameters derived from ML for lower limb joints requires further validation in populations engaged in high-intensity jumping sports. The purpose of this study was to compare lower limb kinematic and kinetic estimates between marker-based (MB) and ML motion capture systems during jumps. Fourteen male Division I movement collegiate athletes performed a minimum of three squat jumps (SJ), drop jumps (DJ), and countermovement jumps (CMJ) in a fixed sequence. The movements were synchronized using ten infrared cameras, six high-resolution cameras, and two force measurement platforms, all controlled by Vicon Nexus software. Motion data were collected, and the angles, moments, and power at the hip, knee, and ankle joints were calculated using Theia3D software. These results were then compared with those obtained from the Vicon system. Comparative analyses included Pearson correlation coefficients (r), root mean square differences (RMSD), extreme error values, and statistical parametric mapping (SPM).SPM analysis of the three movements in the sagittal plane revealed significant differences in hip joint angles, with joint angle RMSD ≤ 5.6°, hip joint moments RMSD ≤ 0.26 N·M/kg, and power RMSD ≤ 2.12 W/kg showing considerable variation, though not reaching statistical significance. ML systems demonstrate high measurement accuracy in estimating knee and ankle kinematics and kinetics in the sagittal plane during these conventional jump tests; however, the accuracy of hip joint kinematic measurements in the sagittal plane requires further validation.

PMID:40425708 | DOI:10.1038/s41598-025-02739-9

PLoS One. 2025 May 27;20(5):e0324293. doi: 10.1371/journal.pone.0324293. eCollection 2025.

ABSTRACT

Cotton is a major cash crop, and increasing its production is extremely important worldwide, especially in agriculture-led economies. The crop is susceptible to various diseases, leading to decreased yields. In recent years, advancements in deep learning methods have enabled researchers to develop automated methods for detecting diseases in cotton crops. Such automation not only assists farmers in mitigating the effects of the disease but also conserves resources in terms of labor and fertilizer costs. However, accurate classification of multiple diseases simultaneously in cotton remains challenging due to multiple factors, including class imbalance, variation in disease symptoms, and the need for real-time detection, as most existing datasets are acquired under controlled conditions. This research proposes a novel method for addressing these challenges and accurately classifying seven classes, including six diseases and a healthy class. We address the class imbalance issue through synthetic data generation using conventional methods like scaling, rotating, transforming, shearing, and zooming and propose a customized StyleGAN for synthetic data generation. After preprocessing, we combine features extracted from MobileNet and VGG16 to create a comprehensive feature vector, passed to three classifiers: Long Short Term Memory Units, Support Vector Machines, and Random Forest. We propose a StackNet-based ensemble classifier that takes the output probabilities of these three classifiers and predicts the class label among six diseases-Bacterial blight, Curl virus, Fusarium wilt, Alternaria, Cercospora, Greymildew-and a healthy class. We trained and tested our method on publicly available datasets, achieving an average accuracy of 97%. Our robust method outperforms state-of-the-art techniques to identify the six diseases and the healthy class.

PMID:40424461 | DOI:10.1371/journal.pone.0324293

Bioinformatics. 2025 May 27:btaf319. doi: 10.1093/bioinformatics/btaf319. Online ahead of print.

ABSTRACT

MOTIVATION: Drug side effects refer to harmful or adverse reactions that occur during drug use, unrelated to the therapeutic purpose. A core issue in drug side effect prediction is determining the frequency of these drug side effects in the population, which can guide patient medication use and drug development. Many computational methods have been developed to predict the frequency of drug side effects as an alternative to clinical trials. However, existing methods typically build regression models on five frequency classes of drug side effects and tend to overfit the training set, leading to boundary handling issues and the risk of overfitting.

RESULTS: To address this problem, we develop a multi-source similarity fusion-based model, named MSSF, for predicting five frequency classes of drug side effects. Compared to existing methods, our model utilizes the multi-source feature fusion module and the self-attention mechanism to explore the relationships between drugs and side effects deeply and employs Bayesian variational inference to more accurately predict the frequency classes of drug side effects. The experimental results indicate that MSSF consistently achieves superior performance compared to existing models across multiple evaluation settings, including cross-validation, cold-start experiments, and independent testing. The visual analysis and case studies further demonstrate MSSF's reliable feature extraction capability and promise in predicting the frequency classes of drug side effects.

AVAILABILITY: The source code of MSSF is available on GitHub (https://github.com/dingxlcse/MSSF.git) and archived on Zenodo (DOI: 10.5281/zenodo.15462041).

SUPPLEMENTARY INFORMATION: Additional files are available at Bioinformatics online.

PMID:40424358 | DOI:10.1093/bioinformatics/btaf319

PLoS One. 2025 May 27;20(5):e0322225. doi: 10.1371/journal.pone.0322225. eCollection 2025.

ABSTRACT

With the digital transformation of the financial industry, credit score prediction, as a key component of risk management, faces increasingly complex challenges. Traditional credit scoring methods often have difficulty in fully capturing the characteristics of large-scale, high-dimensional financial data, resulting in limited prediction performance. To address these issues, this paper proposes a credit score prediction model that combines CNNs and BiGRUs, and uses the GWO algorithm for hyperparameter tuning. CNN performs well in feature extraction and can effectively capture patterns in customer historical behaviors, while BiGRU is good at handling time dependencies, which further improves the prediction accuracy of the model. The GWO algorithm is introduced to further improve the overall performance of the model by optimizing key parameters. Experimental results show that the CNN-BiGRU-GWO model proposed in this paper performs well on multiple public credit score datasets, significantly improving the accuracy and efficiency of prediction. On the LendingClub loan dataset, the MAE of this model is 15.63, MAPE is 4.65%, RMSE is 3.34, and MSE is 12.01, which are 64.5%, 68.0%, 21.4%, and 52.5% lower than the traditional method plawiak of 44.07, 14.51%, 4.25, and 25.29, respectively. In addition, compared with traditional methods, this model also shows stronger advantages in adaptability and generalization ability. By integrating advanced technologies, this model not only provides an innovative technical solution for credit score prediction, but also provides valuable insights into the application of deep learning in the financial field, making up for the shortcomings of existing methods and demonstrating its potential for wide application in financial risk management.

PMID:40424348 | DOI:10.1371/journal.pone.0322225

PLoS One. 2025 May 27;20(5):e0322555. doi: 10.1371/journal.pone.0322555. eCollection 2025.

ABSTRACT

Human Action Recognition (HAR) has grown significantly because of its many uses, including real-time surveillance and human-computer interaction. Various variations in routine human actions make the recognition process of action more difficult. In this paper, we proposed a novel deep learning architecture known as Inverted Bottleneck Residual with Self-Attention (InBRwSA). The proposed architecture is based on two different modules. In the first module, 6-parallel inverted bottleneck residual blocks are designed, and each block is connected with a skip connection. These blocks aim to learn complex human actions in many convolutional layers. After that, the second module is designed based on the self-attention mechanism. The learned weights of the first module are passed to self-attention, extract the most essential features, and can easily discriminate complex human actions. The proposed architecture is trained on the selected datasets, whereas the hyperparameters are chosen using the particle swarm optimization (PSO) algorithm. The trained model is employed in the testing phase for the feature extraction from the self-attention layer and passed to the shallow wide neural network classifier for the final classification. The HMDB51 and UCF 101 are frequently used as action recognition standard datasets. These datasets are chosen to allow for meaningful comparison with earlier research. UCF101 dataset has a wide range of activity classes, and HMDB51 has varied real-world behaviors. These features test the generalizability and flexibility of the presented model. Moreover, these datasets define the evaluation scope within a particular domain and guarantee relevance to real-world circumstances. The proposed technique is tested on both datasets, and accuracies of 78.80% and 91.80% were achieved, respectively. The ablation study demonstrated that a margin of error value of 70.1338 ± 3.053 (±4.35%) and 82.7813 ± 2.852 (±3.45%) for the confidence level 95%,1.960σx̄ is obtained for HMDB51 and UCF datasets respectively. The training time for the highest accuracy for HDMB51 and UCF101 is 134.09 and 252.10 seconds, respectively. The proposed architecture is compared with several pre-trained deep models and state-of-the-art (SOTA) existing techniques. Based on the results, the proposed architecture outperformed existing techniques.

PMID:40424287 | DOI:10.1371/journal.pone.0322555

PLoS One. 2025 May 27;20(5):e0324916. doi: 10.1371/journal.pone.0324916. eCollection 2025.

ABSTRACT

Traditional communication signal detection heavily relies on manually designed features, making it difficult to fully characterize the essential characteristics of the signal, resulting in limited detection accuracy. Based on this, the study innovatively combines Multiple Input Multiple Output (MIMO) with orthogonal frequency division multiplexing technology to construct a data-driven detection system. The system adopts a Multi-DNN method with a dual-DNN cascade structure and mixed activation function design to optimize the channel estimation and signal detection coordination process of the MIMO part. At the same time, a DCNet decoder based on a convolutional neural network batch normalization mechanism is designed to suppress inter-subcarrier interference in OFDM systems effectively. The results showed that on the simulation training set, the accuracy of the research model was 93.8%, the symbol error rate was 17.6%, the throughput was 81.3%, and the modulation error rate was 0.004%. On the simulation test set, its accuracy, symbol error rate, throughput, and modulation error rate were 90.7%, 18.1%, 81.2%, and 0.006%. In both 2.4 GHz and 5 GHz WiFi signals, the signal detection accuracy of the research model reached 91.5% and 91.6%, with false detection rates of 1.9% and 1.5%, and missed detection rates of 1.6% and 4.2%. In resource consumption assessment, the detection speed of this model reached 120 signals/s, with an average latency of 50 ms. The model loading time was only 2.4 s, and the CPU usage was as low as 25%, with moderate memory usage. Overall, the research model has achieved significant results in improving detection accuracy, optimizing real-time performance, and reducing resource consumption. It has broad application prospects in the field of communication signal detection.

PMID:40424260 | DOI:10.1371/journal.pone.0324916

PLoS One. 2025 May 27;20(5):e0321270. doi: 10.1371/journal.pone.0321270. eCollection 2025.

ABSTRACT

Deep learning techniques are widely used in the field of medicine and image classification. In past studies, SwimTransformer and RepVGG are very efficient and classical deep learning models. Multi-scale feature fusion and attention mechanisms are effective means to enhance the performance of deep learning models. In this paper, we introduce a novel Swim-Rep fusion network, along with a new multi-scale feature fusion module called multi-scale strip pooling fusion module(MPF) and a new attention module called Faster Recurrent Criss Cross Polarized Attention (FRCPA), both of which excel at extracting multi-dimensional cross-attention and fine-grained features. Our fully supervised model achieved an impressive accuracy of 99.82% on the MIT-BIH database, outperforming the ViT model classifier by 0.12%. Additionally, our semi-supervised model demonstrated strong performance, achieving 98.4% accuracy on the validation set. Experimental results on the remote sensing image classification dataset RSSCN7 demonstrate that our new base model achieves a classification accuracy of 92.5%, which is 8.57% better than the classification performance of swim-transformer-base and 12.9% better than that of RepVGG-base, and increasing the depth of the module yields superior performance.

PMID:40424251 | DOI:10.1371/journal.pone.0321270