NPJ Sci Food. 2025 Mar 15;9(1):31. doi: 10.1038/s41538-025-00393-z.

ABSTRACT

Veterinary drug residues in poultry and livestock products present persistent challenges to food safety, necessitating precise and efficient detection methods. Surface-enhanced Raman scattering (SERS) has been identified as a powerful tool for veterinary drug residue analysis due to its high sensitivity and specificity. However, the development of reliable SERS substrates and the interpretation of complex spectral data remain significant obstacles. This review summarizes the development process of SERS substrates, categorizing them into metal-based, rigid, and flexible substrates, and highlighting the emerging trend of multifunctional substrates. The diverse application scenarios and detection requirements for these substrates are also discussed, with a focus on their use in veterinary drug detection. Furthermore, the integration of deep learning techniques into SERS-based detection is explored, including substrate structure design optimization, optical property prediction, spectral preprocessing, and both qualitative and quantitative spectral analyses. Finally, key limitations are briefly outlined, such as challenges in selecting reporter molecules, data imbalance, and computational demands. Future trends and directions for improving SERS-based veterinary drug detection are proposed.

PMID:40089516 | DOI:10.1038/s41538-025-00393-z

Sci Rep. 2025 Mar 15;15(1):8938. doi: 10.1038/s41598-025-92994-7.

ABSTRACT

Cracks are common defects in physical structures, and if not detected and addressed in a timely manner, they can pose a severe threat to the overall safety of the structure. In recent years, with advancements in deep learning, particularly the widespread use of Convolutional Neural Networks (CNNs) and Transformers, significant breakthroughs have been made in the field of crack detection. However, CNNs still face limitations in capturing global information due to their local receptive fields when processing images. On the other hand, while Transformers are powerful in handling long-range dependencies, their high computational cost remains a significant challenge. To effectively address these issues, this paper proposes an innovative modification to the VM-UNet model. This modified model strategically integrates the strengths of the Mamba architecture and UNet to significantly improve the accuracy of crack segmentation. In this study, we optimized the original VM-UNet architecture to better meet the practical needs of crack segmentation tasks. Through comparative experiments on the Crack500 and Ozgenel public datasets, the results clearly demonstrate that the improved VM-UNet achieves significant advancements in segmentation accuracy. Compared to the original VM-UNet and other state-of-the-art models, VM-UNet++ shows a 3% improvement in mDS and a 4.6-6.2% increase in mIoU. These results fully validate the effectiveness of our improvement strategy. Additionally, VM-UNet++ demonstrates lower parameter count and floating-point operations, while maintaining a relatively satisfactory inference speed. These improvements make VM-UNet++ advantageous for practical applications.

PMID:40089495 | DOI:10.1038/s41598-025-92994-7

Magn Reson Imaging. 2025 Mar 13:110370. doi: 10.1016/j.mri.2025.110370. Online ahead of print.

ABSTRACT

OBJECTIVE: To evaluate whether deep learning analysis (DL) of intratumor subregion based on dynamic contrast-enhanced MRI (DCE-MRI) can help predict Ki-67 expression level in breast cancer.

MATERIALS AND METHODS: A total of 290 breast cancer patients from two hospitals were retrospectively collected. A k-means clustering algorithm confirmed subregions of tumor. DL features of whole tumor and subregions were extracted from DCE-MRI images based on 3D ResNet18 pre-trained model. The logistic regression model was constructed after dimension reduction. Model performance was assessed using the area under the curve (AUC), and clinical value was demonstrated through decision curve analysis (DCA).

RESULTS: The k-means clustering method clustered the tumor into two subregions (habitat 1 and habitat 2) based on voxel values. Both the habitat 1 model (validation set: AUC = 0.771, 95 %CI: 0.642-0.900 and external test set: AUC = 0.794, 95 %CI: 0.696-0.891) and the habitat 2 model (AUC = 0.734, 95 %CI: 0.605-0.862 and AUC = 0.756, 95 %CI: 0.646-0.866) showed better predictive capabilities for Ki-67 expression level than the whole tumor model (AUC = 0.686, 95 %CI: 0.550-0.823 and AUC = 0.680, 95 %CI: 0.555-0.804). The combined model based on the two subregions further enhanced the predictive capability (AUC = 0.808, 95 %CI: 0.696-0.921 and AUC = 0.842, 95 %CI: 0.758-0.926), and it demonstrated higher clinical value than other models in DCA.

CONCLUSIONS: The deep learning model derived from subregion of tumor showed better performance for predicting Ki-67 expression level in breast cancer patients. Additionally, the model that integrated two subregions further enhanced the predictive performance.

PMID:40089082 | DOI:10.1016/j.mri.2025.110370

Int J Radiat Oncol Biol Phys. 2025 Mar 13:S0360-3016(25)00243-3. doi: 10.1016/j.ijrobp.2025.03.012. Online ahead of print.

ABSTRACT

BACKGROUND: Patients with identical stages or similar tumor volumes can vary significantly in their responses to radiotherapy (RT) due to individual characteristics, making personalized RT for non-small cell lung cancer (NSCLC) challenging. This study aimed to develop a deep learning (DL) model by integrating pre- and mid-treatment computed tomography (CT) to predict the treatment response in NSCLC patients.

METHODS AND MATERIAL: We retrospectively collected data from 168 NSCLC patients across three hospitals. Data from A (35 patients) and B (93 patients) were used for model training and internal validation, while data from C (40 patients) was used for external validation. DL, radiomics, and clinical features were extracted to establish a varying time-interval long short-term memory network (VTI-LSTM) for response prediction. Furthermore, we derived a model-deduced personalize dose escalation (DE) for patients predicted to have suboptimal gross tumor volume (GTV) regression. The area under the receiver operating characteristic curve (AUC) and predicted absolute error (PAE) were used to evaluate the predictive Response Evaluation Criteria in Solid Tumors (RECIST) classification and proportion of GTV residual. DE was calculated as biological equivalent dose (BED) using an α/β ratio of 10 Gy.

RESULTS: The model using only pre-treatment CT achieved the highest AUC of 0.762 and 0.687 in internal and external validation respectively, while the model integrating both pre- and mid-treatment CT achieved AUC of 0.869 and 0.798, with PAE of 0.137 and 0.185. We performed personalized DE for 29 patients. Their original BED was approximately 72 Gy, within the range of 71.6 Gy to 75 Gy. DE ranged from 77.7 to 120 Gy for 29 patients, with 17 patients exceeding 100 Gy and eight patients reaching the model's preset upper limit of 120 Gy.

CONCLUSIONS: Combining pre- and mid-treatment CT enhances prediction performance for RT response and offers a promising approach for personalized DE in NSCLC.

PMID:40089073 | DOI:10.1016/j.ijrobp.2025.03.012

Neural Netw. 2025 Mar 10;187:107337. doi: 10.1016/j.neunet.2025.107337. Online ahead of print.

ABSTRACT

Deep learning for Electroencephalography (EEG) has become dominant in the tasks of discrimination and evaluation of brain disorders. However, despite its significant successes, this approach has long been facing challenges due to the limited availability of labeled samples and the individuality of subjects, particularly in complex scenarios such as Autism Spectrum Disorders (ASD). To facilitate the efficient optimization of EEG discrimination models in the face of these limitations, this study has developed a framework called STEM (Self-Training EEG Model). STEM accomplishes this by self-training the model, which involves initializing it with limited labeled samples and optimizing it with self-constructed samples. (1) Model initialization with multi-task learning: A multi-task model (MAC) comprising an AutoEncoder and a classifier offers guidance for subsequent pseudo-labeling. This guidance includes task-related latent EEG representations and prediction probabilities of unlabeled samples. The AutoEncoder, which consists of depth-separable convolutions and BiGRUs, is responsible for learning comprehensive EEG representations through the EEG reconstruction task. Meanwhile, the classifier, trained using limited labeled samples through supervised learning, directs the model's attention towards capturing task-related features. (2) Model optimization aided by pseudo-labeled samples construction: Next, trustworthy pseudo-labels are assigned to the unlabeled samples, and this approach (PLASC) combines the sample's distance relationship in the feature space mapped by the encoder with the sample's predicted probability, using the initial MAC model as a reference. The constructed pseudo-labeled samples then support the self-training of MAC to learn individual information from new subjects, potentially enhancing the adaptation of the optimized model to samples from new subjects. The STEM framework has undergone an extensive evaluation, comparing it to state-of-the-art counterparts, using resting-state EEG data collected from 175 ASD-suspicious children spanning different age groups. The observed results indicate the following: (1) STEM achieves the best performance, with an accuracy of 88.33% and an F1-score of 87.24%, and (2) STEM's multi-task learning capability outperforms supervised methods when labeled data is limited. More importantly, the use of PLASC improves the model's performance in ASD discrimination across different age groups, resulting in an increase in accuracy (3%-8%) and F1-scores (4%-10%). These increments are approximately 6% higher than those achieved by the comparison methods.

PMID:40088831 | DOI:10.1016/j.neunet.2025.107337

Waste Manag. 2025 Mar 14;200:114742. doi: 10.1016/j.wasman.2025.114742. Online ahead of print.

ABSTRACT

The surge in medical waste has highlighted the urgent need for cost-effective and advanced management solutions. In this paper, a novel medical waste management approach, "MedBin," is proposed for automated sorting, reusing, and recycling. A comprehensive medical waste dataset, "MedBin-Dataset" is established, comprising 2,119 original images spanning 36 categories, with samples captured in various backgrounds. The lightweight "MedBin-Net" model is introduced to enable detection and instance segmentation of medical waste, enhancing waste recognition capabilities. Experimental results demonstrate the effectiveness of the proposed approach, achieving an average precision of 0.91, recall of 0.97, and F1-score of 0.94 across all categories with just 2.51 M parameters (where M stands for million, i.e., 2.51 million parameters), 5.20G FLOPs (where G stands for billion, i.e., 5.20 billion floating-point operations per second), and 0.60 ms inference time. Additionally, the proposed method includes a World Health Organization (WHO) Guideline-Based Classifier that categorizes detected waste into 5 types, each with a corresponding disposal method, following WHO medical waste classification standards. The proposed method, along with the dedicated dataset, offers a promising solution that supports sustainable medical waste management and other related applications. To access the MedBin-Dataset samples, please visit https://universe.roboflow.com/uob-ylti8/medbin_dataset. The source code for MedBin-Net can be found at https://github.com/Wayne3918/MedbinNet.

PMID:40088805 | DOI:10.1016/j.wasman.2025.114742

Ecohealth. 2025 Mar 15. doi: 10.1007/s10393-025-01705-1. Online ahead of print.

ABSTRACT

The World Health Organization (WHO) aims to ensure the highest level of health for all populations. Despite progress, increased life expectancy has not translated into a proportional rise in healthy life years, as chronic diseases are on the rise. In this context, health ecology emerges as a new scientific discipline focused on preserving health rather than curing diseases. It seeks to calculate healthy life expectancy by analyzing individual, social, and systemic choices, offering a proactive and rigorous approach to making informed decisions and improving long-term well-being.

PMID:40088354 | DOI:10.1007/s10393-025-01705-1

Interdiscip Sci. 2025 Mar 15. doi: 10.1007/s12539-025-00695-6. Online ahead of print.

ABSTRACT

Drug combination therapy has shown improved efficacy and decreased adverse effects, making it a practical approach for conditions like cancer. However, discovering all potential synergistic drug combinations requires extensive experimentation, which can be challenging. Recent research utilizing deep learning techniques has shown promise in reducing the number of experiments and overall workload by predicting synergistic drug combinations. Therefore, developing reliable and effective computational methods for predicting these combinations is essential. This paper proposed a novel method called Drug-molecule Connect Cell (DconnC) for predicting synergistic drug combinations. DconnC leverages cellular features as nodes to establish connections between drug molecular structures, allowing the extraction of pertinent features. These features are then optimized through self-augmented contrastive learning using bidirectional recurrent neural networks (Bi-RNN) and long short-term memory (LSTM) models, ultimately predicting the drug synergy. By integrating information about the molecular structure of drugs for the extraction of cell features, DconnC uncovers the inherent connection between drug molecular structures and cellular characteristics, thus improving the accuracy of predictions. The performance of our method is evaluated using a five-fold cross validation approach, demonstrating a 35 % reduction in the mean square error (MSE) compared to the next-best method. Moreover, our method significantly outperformed alternative approaches in various evaluation criteria, particularly in predicting different cell lines and Loewe synergy score intervals.

PMID:40088336 | DOI:10.1007/s12539-025-00695-6

J Mol Neurosci. 2025 Mar 15;75(1):36. doi: 10.1007/s12031-025-02329-4.

ABSTRACT

Parkinson's disease recognition (PDR) involves identifying Parkinson's disease using clinical evaluations, imaging studies, and biomarkers, focusing on early symptoms like tremors, rigidity, and bradykinesia to facilitate timely treatment. However, due to noise, variability, and the non-stationary nature of EEG signals, distinguishing PD remains a challenge. Traditional deep learning methods struggle to capture the intricate temporal and spatial dependencies in EEG data, limiting their precision. To address this, a novel fusion framework called graph embedding class-based convolutional recurrent attention network with Brown Bear Optimization Algorithm (GECCR2ANet + BBOA) is introduced for EEG-based PD recognition. Preprocessing is conducted using numerical operations and noise removal with weighted guided image filtering and entropy evaluation weighting (WGIF-EEW). Feature extraction is performed via the improved VGG19 with graph triple attention network (IVGG19-GTAN), which captures spatial and temporal dependencies in EEG data. The extracted features are classified using the graph embedding class-based convolutional recurrent attention network (GECCR2ANet), further optimized through the Brown Bear Optimization Algorithm (BBOA) to enhance classification accuracy. The model achieves 99.9% accuracy, 99.4% sensitivity, and a 99.3% F1-score on the UNM dataset, and 99.8% accuracy, 99.1% sensitivity, and 99.2% F1-score on the UC San Diego dataset, significantly outperforming existing methods. Additionally, it records an error rate of 0.5% and a computing time of 0.25 s. Previous models like 2D-MDAGTS, A-TQWT, and CWCNN achieved below 95% accuracy, while the proposed model's 99.9% accuracy underscores its superior performance in real-world clinical applications, enhancing early PD detection and improving diagnostic efficiency.

PMID:40088329 | DOI:10.1007/s12031-025-02329-4

Abdom Radiol (NY). 2025 Mar 15. doi: 10.1007/s00261-025-04883-2. Online ahead of print.

ABSTRACT

PURPOSE: Magnetic resonance elastography (MRE) measures liver stiffness for fibrosis staging, but its utility can be hindered by quality control (QC) challenges and measurement variability. The objective of the study was to fully automate liver MRE QC and liver stiffness measurement (LSM) using a deep learning (DL) method.

METHODS: In this retrospective, single center, IRB-approved human study, a curated dataset involved 897 MRE magnitude slices from 146 2D MRE scans [1.5 T and 3 T MRI, 2D Gradient Echo (GRE), and 2D Spin Echo-Echo Planar Imaging (SE-EPI)] of 69 patients (37 males, mean age 51.6 years). A SqueezeNet-based binary QC model was trained using combined and individual inputs of MRE magnitude slices and their 2D Fast-Fourier transforms to detect artifacts from patient motion, aliasing, and blurring. Three independent observers labeled MRE magnitude images as 0 (non-diagnostic quality) or 1 (diagnostic quality) to create a reference standard. A 2D U-Net segmentation model was trained on diagnostic slices with liver masks to support LSM. Intersection over union between the predicted segmentation and confidence masks identified measurable areas for LSM on elastograms. Cohen's unweighted Kappa coefficient, mean LSM error (%), and intra-class correlation coefficient were calculated to compare the DL-assisted approach with the observers' annotations. An efficiency analysis compared the DL-assisted vs manual LSM durations.

RESULTS: The top QC ensemble model (using MRE magnitude alone) achieved accuracy, precision, and recall of 0.958, 0.982, and 0.886, respectively. The mean LSM error between the DL-assisted approach and the reference standard was 1.9% ± 4.6%. DL-assisted approach completed LSM for 29 diagnostic slices in under 1 s, compared to 20 min manually.

CONCLUSION: An automated DL-based classification of liver MRE diagnostic quality, liver segmentation, and LSM approach demonstrates a promising high performance, with potential for clinical adoption.

PMID:40088296 | DOI:10.1007/s00261-025-04883-2

Med Biol Eng Comput. 2025 Mar 15. doi: 10.1007/s11517-025-03334-w. Online ahead of print.

ABSTRACT

Neurodegenerative disorders (NDD) represent a broad spectrum of diseases that progressively impact neurological function, yet available therapeutics remain conspicuously limited. They lead to altered rhythms and dynamics of walking, which are evident in the sequential footfall contact times measured from one stride to the next. Early detection of aberrant walking patterns can prevent the progression of risks associated with neurodegenerative diseases, enabling timely intervention and management. In this study, we propose a new methodology based on a parallel-path ConvMixer neural network for neurodegenerative disease classification from gait analysis. Earlier research in this field depended on either gait parameter-derived features or the ground reaction force signal. This study has emerged to combine both ground reaction force signals and extracted features to improve gait pattern analysis. The study is being carried out on the gait dynamics in the NDD database, i.e., on the benchmark dataset Physionet gaitndd. Leave one out cross-validation is carried out. The proposed model achieved the best average rates of accuracy, precision, recall, and an F1-score of 97.77 % , 96.37 % , 96.5 % , and 96.25 % , respectively. The experimental findings demonstrate that our approach outperforms the best results achieved by other state-of-the-art methods.

PMID:40088256 | DOI:10.1007/s11517-025-03334-w

J Am Chem Soc. 2025 Mar 15. doi: 10.1021/jacs.5c02046. Online ahead of print.

ABSTRACT

Determining hydrogen positions in metal hydride clusters remains a formidable challenge, which relies heavily on unaffordable neutron diffraction. While machine learning has shown promise, only one deep learning-based method has been proposed so far, which relies heavily on neutron diffraction data for training, limiting its general applicability. In this work, we present an innovative strategy─SSW-NN (stochastic surface walking with neural network)─a robust, non-neutron diffraction-dependent technique that accurately predicts hydrogen positions. Validated against neutron diffraction data for copper hydride clusters, SSW-NN proved effective for clusters where only X-ray diffraction data or DFT predictions are available. It offers superior accuracy, efficiency, and versatility across different metal hydrides, including silver and alloy hydride systems, currently without any neutron diffraction references. This approach not only establishes a new research paradigm for metal hydride clusters but also provides a universal solution for hydrogen localization in other research fields constrained by neutron sources.

PMID:40088162 | DOI:10.1021/jacs.5c02046

J Appl Clin Med Phys. 2025 Mar 14:e70012. doi: 10.1002/acm2.70012. Online ahead of print.

ABSTRACT

PURPOSE: Training deep learning dose prediction models for the latest cutting-edge radiotherapy techniques, such as AI-based nodal radiotherapy (AINRT) and Daily Adaptive AI-based nodal radiotherapy (DA-AINRT), is challenging due to limited data. This study aims to investigate the impact of transfer learning on the predictive performance of an existing clinical dose prediction model and its potential to enhance emerging radiotherapy approaches for head and neck cancer patients.

METHOD: We evaluated the impact and benefits of transfer learning by fine-tuning a Hierarchically Densely Connected U-net on both AINRT and DA-AINRT patient datasets, creating ModelAINRT (Study 1) and ModelDA-AINRT (Study 2). These models were compared against pretrained and baseline models trained from scratch. In Study 3, both fine-tuned models were tested using DA-AINRT patients' final adaptive sessions to assess ModelAINRT 's effectiveness on DA-AINRT patients, given that the primary difference is planning target volume (PTV) sizes between AINRT and DA-AINRT.

RESULT: Studies 1 and 2 revealed that the transfer learning model accurately predicted the mean dose within 0.71% and 0.86% of the prescription dose on the test data. This outperformed the pretrained and baseline models, which showed PTV mean dose prediction errors of 2.29% and 1.1% in Study 1, and 2.38% and 2.86% in Study 2 (P < 0.05). Additionally, Study 3 demonstrated significant improvements in PTV dose prediction error with ModelDA-AINRT, with a mean dose difference of 0.86% ± 0.73% versus 2.26% ± 1.65% (P < 0.05). This emphasizes the importance of training models for specific patient cohorts to achieve optimal outcomes.

CONCLUSION: Applying transfer learning to dose prediction models significantly improves prediction accuracy for PTV while maintaining similar dose performance in predicting organ-at-risk (OAR) dose compared to pretrained and baseline models. This approach enhances dose prediction models for novel radiotherapy methods with limited training data.

PMID:40087841 | DOI:10.1002/acm2.70012

Magn Reson Med. 2025 Mar 14. doi: 10.1002/mrm.30488. Online ahead of print.

ABSTRACT

PURPOSE: To develop a quantitative multislice chemical exchange saturation transfer (CEST) schedule optimization and pulse sequence that reduces the loss of sensitivity inherent to multislice sequences.

METHODS: A deep learning framework was developed for simultaneous optimization of scan parameters and slice order. The optimized sequence was tested in numerical simulations against a random schedule and an optimized single-slice schedule. The scan efficiency of each schedule was quantified. Three healthy subjects were scanned with the proposed sequence. Regions of interest in white matter (WM) and gray matter (GM) were defined. The sequence was compared with the single-slice sequence in vivo and differences quantified using Bland-Altman plots. Test-retest reproducibility was assessed, and the Lin's concordance correlation coefficient (CCC) was calculated for WM and GM. Intersubject variability was also measured with the CCC. Feasibility of whole-brain clinical imaging was tested using a multislab acquisition in 1 subject.

RESULTS: The optimized multislice sequence yielded a lower mean error than the random schedule for all tissue parameters and a lower error than the optimized single-slice schedule for four of six parameters. The optimized multislice sequence provided the highest scan efficiency. In vivo tissue-parameter values obtained with the proposed sequence agreed well with those of the optimized single-slice sequence and prior studies. The average WM/GM CCC was 0.8151/0.7779 for the test-retest scans and 0.7792/0.7191 for the intersubject variability experiment.

CONCLUSION: A multislice schedule optimization framework and pulse sequence were demonstrated for quantitative CEST. The proposed approach enables accurate and reproducible whole-brain quantitative CEST imaging in clinically relevant scan times.

PMID:40087839 | DOI:10.1002/mrm.30488

Biomark Res. 2025 Mar 14;13(1):45. doi: 10.1186/s40364-025-00758-2.

ABSTRACT

Artificial intelligence (AI) can transform drug discovery and early drug development by addressing inefficiencies in traditional methods, which often face high costs, long timelines, and low success rates. In this review we provide an overview of how to integrate AI to the current drug discovery and development process, as it can enhance activities like target identification, drug discovery, and early clinical development. Through multiomics data analysis and network-based approaches, AI can help to identify novel oncogenic vulnerabilities and key therapeutic targets. AI models, such as AlphaFold, predict protein structures with high accuracy, aiding druggability assessments and structure-based drug design. AI also facilitates virtual screening and de novo drug design, creating optimized molecular structures for specific biological properties. In early clinical development, AI supports patient recruitment by analyzing electronic health records and improves trial design through predictive modeling, protocol optimization, and adaptive strategies. Innovations like synthetic control arms and digital twins can reduce logistical and ethical challenges by simulating outcomes using real-world or virtual patient data. Despite these advancements, limitations remain. AI models may be biased if trained on unrepresentative datasets, and reliance on historical or synthetic data can lead to overfitting or lack generalizability. Ethical and regulatory issues, such as data privacy, also challenge the implementation of AI. In conclusion, in this review we provide a comprehensive overview about how to integrate AI into current processes. These efforts, although they will demand collaboration between professionals, and robust data quality, have a transformative potential to accelerate drug development.

PMID:40087789 | DOI:10.1186/s40364-025-00758-2

Biomed Eng Online. 2025 Mar 14;24(1):34. doi: 10.1186/s12938-025-01336-1.

ABSTRACT

BACKGROUND: In recent years, artificial intelligence and machine learning algorithms have been used more extensively to diagnose diabetic retinopathy and other diseases. Still, the effectiveness of these methods has not been thoroughly investigated. This study aimed to evaluate the performance and limitations of machine learning and deep learning algorithms in detecting diabetic retinopathy.

METHODS: This study was conducted based on the PRISMA checklist. We searched online databases, including PubMed, Scopus, and Google Scholar, for relevant articles up to September 30, 2023. After the title, abstract, and full-text screening, data extraction and quality assessment were done for the included studies. Finally, a meta-analysis was performed.

RESULTS: We included 76 studies with a total of 1,371,517 retinal images, of which 51 were used for meta-analysis. Our meta-analysis showed a significant sensitivity and specificity with a percentage of 90.54 (95%CI [90.42, 90.66], P < 0.001) and 78.33% (95%CI [78.21, 78.45], P < 0.001). However, the AUC (area under curvature) did not statistically differ across studies, but had a significant figure of 0.94 (95% CI [- 46.71, 48.60], P = 1).

CONCLUSIONS: Although machine learning and deep learning algorithms can properly diagnose diabetic retinopathy, their discriminating capacity is limited. However, they could simplify the diagnosing process. Further studies are required to improve algorithms.

PMID:40087776 | DOI:10.1186/s12938-025-01336-1

Comput Biol Med. 2025 Mar 13;189:109894. doi: 10.1016/j.compbiomed.2025.109894. Online ahead of print.

ABSTRACT

Advanced imaging techniques, like magnetic resonance imaging (MRI), have revolutionised cardiovascular disease diagnosis and monitoring in humans and animal models. Real-time (RT) MRI, which can capture a single slice during each consecutive heartbeat while the animal or patient breathes continuously, generates large data sets that necessitate automatic myocardium segmentation to fully exploit these technological advancements. While automatic segmentation is common in human adults, it remains underdeveloped in preclinical animal models. In this study, we developed and trained a fully automated 2D convolutional neural network (CNN) for segmenting the left and right ventricles and the myocardium in non-human primates (NHPs) using RT cardiac MR images of rhesus macaques, in the following referred to as PrimUNet. Based on the U-Net framework, PrimUNet achieved optimal performance with a learning rate of 0.0001, an initial kernel size of 64, a final kernel size of 512, and a batch size of 32. It attained an average Dice score of 0.9, comparable to human studies. Testing PrimUNet on additional RT MRI data from rhesus macaques demonstrated strong agreement with manual segmentation for left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), and left ventricular myocardial volume (LVMV). It also performs well on cine MRI data of rhesus macaques and acceptably on those of baboons. PrimUNet is well-suited for automatically segmenting extensive RT MRI data, facilitating strain analyses of individual heartbeats. By eliminating human observer variability, PrimUNet enhances the reliability and reproducibility of data analysis in animal research, thereby advancing translational cardiovascular studies.

PMID:40086292 | DOI:10.1016/j.compbiomed.2025.109894

Comput Biol Med. 2025 Mar 13;189:109924. doi: 10.1016/j.compbiomed.2025.109924. Online ahead of print.

ABSTRACT

- In recent times, the electrocardiogram (ECG) has been considered as a significant and effective screening mode in clinical practice to assess cardiac arrhythmias. Precise feature extraction and classification are considered as essential concerns in the automated prediction of heart disease. A deep bi-directional capsule network (Bi-CapsNet) uses a new method based on an intelligent deep learning (DL) classifier model to make the classification process very accurate. Initially, the input ECG signal data are acquired and the preprocessing steps such as DC drift, normalization, LPF filtering, spectrogram analysis, and artifact removal are applied. After preprocessing the data, the Deep Ensemble CNN-RNN approach is employed for feature extraction. Finally, the Deep Bi-CapsNet model is used to predict and classify the cardiac arrhythmia. For performance validation, the dataset is referred to the MIT-BIH arrhythmia database, which selects five different types of arrhythmias from the ECG waveform to estimate the proposed model. Various ECG arrhythmia categories, including Normal (NOR), Right Bundle Branch Block (RBBB), Premature Ventricular Contraction (PVC), Atrial Premature Beat (APB), and Left Bundle Branch Block (LBBB) have been identified. For performance analysis, the metrics such as precision, accuracy, F1-score, error rate, sensitivity, false positive rate, specificity, Mathew coefficient, Kappa coefficient, and outcomes are included and compared with the traditional methods to validate the effectiveness of the implemented scheme. The proposed scheme has achieved an overall accuracy rate of approximately 97.19 % compared to the traditional deep learning models like CNN (89.87 %), FTBO (85 %), and Capsule Network (97.0 %). The comparison results indicate that the proposed hybrid model outperforms these traditional models.

PMID:40086290 | DOI:10.1016/j.compbiomed.2025.109924

Am J Alzheimers Dis Other Demen. 2025 Jan-Dec;40:15333175251325091. doi: 10.1177/15333175251325091. Epub 2025 Mar 14.

ABSTRACT

White matter hyperintensity (WMH) is associated with cognitive impairment. In this study, 79 patients with WMH from hospital 1 were randomly divided into a training set (62 patients) and an internal validation set (17 patients). In addition, 29 WMH patients from hospital 2 were used as an external validation set. Cognitive status was determined based on neuropsychological assessment results. A deep learning convolutional neural network of VB-Nets was used to automatically identify and segment whole-brain subregions and WMH. The PyRadiomics package in Python was used to automatically extract radiomic features from the WMH and bilateral hippocampi. Delong tests revealed that the random forest model based on combined features had the best performance for the detection of cognitive impairment in WMH patients, with an AUC of 0.900 in the external validation set. Our results provide clinical doctors with a reliable tool for the early diagnosis of cognitive impairment in WMH patients.

PMID:40087144 | DOI:10.1177/15333175251325091

Chin J Traumatol. 2025 Feb 18:S1008-1275(25)00009-4. doi: 10.1016/j.cjtee.2024.11.001. Online ahead of print.

ABSTRACT

PURPOSE: To construct a decision-making app for pre-hospital damage control resuscitation (PHDCR) for severely injured patients, and to make a preliminary trial test on the effectiveness and usability aspects of the constructed app.

METHODS: Decision-making algorithms were first established by a thorough literature review, and were then used to be learned by computer with 3 kinds of text segmentation algorithms, i.e., dictionary-based segmentation, machine learning algorithms based on labeling, and deep learning algorithms based on understanding. B/S architecture mode and Spring Boot were used as a framework to construct the app. A total of 16 Grade-5 medical students were recruited to test the effectiveness and usability aspects of the app by using an animal model-based test on simulated PHDCR. Twelve adult Bama miniature pigs were subjected to penetrating abdominal injuries and were randomly assigned to the 16 students, who were randomly divided into 2 groups (n = 8 each): group A (decided on PHDCR by themselves) and group B (decided on PHDCR with the aid of the app). The students were asked to complete the PHDCR within 1 h, and then blood samples were taken and thromboelastography, routine coagulation test, blood cell count, and blood gas analysis were examined. The lab examination results along with the value of mean arterial pressure were used to compare the resuscitation effects between the 2 groups. Furthermore, a 4-statement-based post-test survey on a 5-point Likert scale was performed in group B students to test the usability aspects of the constructed app.

RESULTS: With the above 3 kinds of text segmentation algorithm, B/S architecture mode, and Spring Boot as the development framework, the decision-making app for PHDCR was successfully constructed. The time to decide PHDCR was (28.8 ± 3.41) sec in group B, much shorter than that in group A (87.5 ± 8.53) sec (p < 0.001). The outcomes of animals treated by group B students were much better than that by group A students as indicated by higher mean arterial pressure, oxygen saturation and fibrinogen concentration and maximum amplitude, and lower R values in group B than those in group A. The post-test survey revealed that group B students gave a mean score of no less than 4 for all 4 statements.

CONCLUSION: A decision-making app for PHDCR was constructed in the present study and the preliminary trial test revealed that it could help to improve the resuscitation effect in animal models of penetrating abdominal injury.

PMID:40087116 | DOI:10.1016/j.cjtee.2024.11.001