Front Pharmacol. 2025 Feb 11;16:1525029. doi: 10.3389/fphar.2025.1525029. eCollection 2025.

ABSTRACT

Computational drug repositioning, serving as an effective alternative to traditional drug discovery plays a key role in optimizing drug development. This approach can accelerate the development of new therapeutic options while reducing costs and mitigating risks. In this study, we propose a novel deep learning-based framework KGRDR containing multi-similarity integration and knowledge graph learning to predict potential drug-disease interactions. Specifically, a graph regularized approach is applied to integrate multiple drug and disease similarity information, which can effectively eliminate noise data and obtain integrated similarity features of drugs and diseases. Then, topological feature representations of drugs and diseases are learned from constructed biomedical knowledge graphs (KGs) which encompasses known drug-related and disease-related interactions. Next, the similarity features and topological features are fused by utilizing an attention-based feature fusion method. Finally, drug-disease associations are predicted using the graph convolutional network. Experimental results demonstrate that KGRDR achieves better performance when compared with the state-of-the-art drug-disease prediction methods. Moreover, case study results further validate the effectiveness of KGRDR in predicting novel drug-disease interactions.

PMID:40008124 | PMC:PMC11850324 | DOI:10.3389/fphar.2025.1525029

J Oral Biol Craniofac Res. 2025 Mar-Apr;15(2):215-220. doi: 10.1016/j.jobcr.2025.01.016. Epub 2025 Feb 8.

ABSTRACT

BACKGROUND: Dental implants are the most accepted prosthetic alternative for missing teeth. With growing demands, several manufacturers have entered the market and produce a variety of implant brands creating a challenge for clinicians to identify the implant when the necessity arises. Currently, radiographs are the only tools for implant identification which is inherently a complex process, hence the need for implant identification technique. Artificial intelligence capable of analysing images in a radiograph and predicting implant type is an efficient tool. The study evaluated an advanced deep learning technique, DEtection TRanformer for implant identification.

METHODS: A transformer-based deep learning technique, DEtection TRanformer was trained to identify implants in radiographs. A dataset of 1138 images consisting of five implant types captured from periapical and panoramic radiographs was chosen for the study. After augmentation, a dataset of 1744 images was secured and then split into training, validation and test datasets for the model. The model was trained and evaluated for its performance.

RESULTS: The model achieved an overall precision of 0.83 and a recall score of 0.89. The model achieved an F1-score of 0.82 indicating a strong balance between recall and precision. The Precision-Recall Curve, with an AUC of 0.96, showed that the model performed well across various thresholds. The training and validation graphs showed a consistent decrease in the loss functions across classes.

CONCLUSION: The model showed high performance on the training data, though it faced challenges with unseen validation data. High precision, recall and F1 score indicate the model's potential for implant identification. Optimizing this model for a balance between accuracy and efficiency will be necessary for real-time medical imaging applications.

PMID:40008072 | PMC:PMC11849603 | DOI:10.1016/j.jobcr.2025.01.016

Front Hum Neurosci. 2025 Feb 11;19:1498297. doi: 10.3389/fnhum.2025.1498297. eCollection 2025.

ABSTRACT

The rapid expansion of dialectally unique Arabic material on social media and the internet highlights how important it is to categorize dialects accurately to maximize a variety of Natural Language Processing (NLP) applications. The improvement in classification performance highlights the wider variety of linguistic variables that the model can capture, providing a reliable solution for precise Arabic dialect recognition and improving the efficacy of NLP applications. Recent advances in deep learning (DL) models have shown promise in overcoming potential challenges in identifying Arabic dialects. In this paper, we propose a novel stacking model based on two transformer models, i.e., Bert-Base-Arabertv02 and Dialectal-Arabic-XLM-R-Base, to enhance the classification of dialectal Arabic. The proposed model consists of two levels, including base models and meta-learners. In the proposed model, Level 1 generates class probabilities from two transformer models for training and testing sets, which are then used in Level 2 to train and evaluate a meta-learner. The stacking model compares various models, including long-short-term memory (LSTM), gated recurrent units (GRU), convolutional neural network (CNN), and two transformer models using different word embedding. The results show that the stacking model combination of two models archives outperformance over single-model approaches due to capturing a broader range of linguistic features, which leads to better generalization across different forms of Arabic. The proposed model is evaluated based on the performance of IADD and Shami. For Shami, the Stacking-Transformer achieves the highest performance in all rates compared to other models with 89.73 accuracy, 89.596 precision, 89.73 recall, and 89.574 F1-score. For IADD, the Stacking-Transformer achieves the highest performance in all rates compared to other models with 93.062 accuracy, 93.368 precision, 93.062 recall, and 93.184 F1 score. The improvement in classification performance highlights the wider variety of linguistic variables that the model can capture, providing a reliable solution for precise Arabic dialect recognition and improving the efficacy of NLP applications.

PMID:40007884 | PMC:PMC11850318 | DOI:10.3389/fnhum.2025.1498297

Front Hum Neurosci. 2025 Feb 11;19:1558584. doi: 10.3389/fnhum.2025.1558584. eCollection 2025.

NO ABSTRACT

PMID:40007883 | PMC:PMC11850378 | DOI:10.3389/fnhum.2025.1558584

Heliyon. 2025 Feb 5;11(3):e42476. doi: 10.1016/j.heliyon.2025.e42476. eCollection 2025 Feb 15.

ABSTRACT

Predicting drug-target binding affinity via in silico methods is crucial in drug discovery. Traditional machine learning relies on manually engineered features from limited data, leading to suboptimal performance. In contrast, deep learning excels at extracting features from raw sequences but often overlooks essential biological context features, hindering effective binding prediction. Additionally, these models struggle to capture global and local feature distributions efficiently in protein sequences and drug SMILES. Previous state-of-the-art models, like transformers and graph-based approaches, face scalability and resource efficiency challenges. Transformers struggle with scalability, while graph-based methods have difficulty handling large datasets and complex molecular structures. In this paper, we introduce InceptionDTA, a novel drug-target binding affinity prediction model that leverages CharVec, an enhanced variant of Prot2Vec, to incorporate both biological context and categorical features into protein sequence encoding. InceptionDTA utilizes a multi-scale convolutional architecture based on the Inception network to capture features at various spatial resolutions, enabling the extraction of both local and global features from protein sequences and drug SMILES. We evaluate InceptionDTA across a range of benchmark datasets commonly used in drug-target binding affinity prediction. Our results demonstrate that InceptionDTA outperforms various sequence-based, transformer-based, and graph-based deep learning approaches across warm-start, refined, and cold-start splitting settings. In addition to using CharVec, which demonstrates greater accuracy in absolute predictions, InceptionDTA also includes a version that employs simple label encoding and excels in ranking and predicting relative binding affinities. This versatility highlights how InceptionDTA can effectively adapt to various predictive requirements. These results emphasize the promise of our approach in expediting drug repurposing initiatives, enabling the discovery of new drugs, and contributing to advancements in disease treatment.

PMID:40007773 | PMC:PMC11850134 | DOI:10.1016/j.heliyon.2025.e42476

Sci Rep. 2025 Feb 25;15(1):6758. doi: 10.1038/s41598-025-90780-z.

ABSTRACT

Diabetic foot ulcers (DFUs) are a common and serious complication of diabetes, presenting as open sores or wounds on the sole. They result from impaired blood circulation and neuropathy associated with diabetes, increasing the risk of severe infections and even amputations if untreated. Early detection, effective wound care, and diabetes management are crucial to prevent and treat DFUs. Artificial intelligence (AI), particularly through deep learning, has revolutionized DFU diagnosis and treatment. This work introduces the DFU_XAI framework to enhance the interpretability of deep learning models for DFU labeling and localization, ensuring clinical relevance. The framework evaluates six advanced models-Xception, DenseNet121, ResNet50, InceptionV3, MobileNetV2, and Siamese Neural Network (SNN)-using interpretability techniques like SHAP, LIME, and Grad-CAM. Among these, the SNN model excelled with 98.76% accuracy, 99.3% precision, 97.7% recall, 98.5% F1-score, and 98.6% AUC. Grad-CAM heat maps effectively identified ulcer locations, aiding clinicians with precise and visually interpretable insights. The DFU_XAI framework integrates explainability into AI-driven healthcare, enhancing trust and usability in clinical settings. This approach addresses challenges of transparency in AI for DFU management, offering reliable and efficient solutions to this critical healthcare issue. Traditional DFU methods are labor-intensive and costly, highlighting the transformative potential of AI-driven systems.

PMID:40000748 | DOI:10.1038/s41598-025-90780-z

J Imaging Inform Med. 2025 Feb 25. doi: 10.1007/s10278-025-01436-3. Online ahead of print.

ABSTRACT

Ultrasound is widely used to monitor and diagnose thyroid nodules, but accurately segmenting these nodules in ultrasound images remains a challenge due to the presence of noise and artifacts, which often blur nodule boundaries. While several deep learning algorithms have been developed for this task, their performance is frequently suboptimal. In this study, we introduce the use of EfficientNet-B7 as the backbone for the DeepLabV3+ architecture in thyroid nodule segmentation, marking its first application in this area. We evaluated the proposed method using a dataset from the First Affiliated Hospital of Zhengzhou University, along with two public datasets. The results demonstrate high performance, with a pixel accuracy (PA) of 97.67%, a Dice similarity coefficient of 0.8839, and an Intersection over Union (IoU) of 79.69%. These outcomes outperform most traditional segmentation networks.

PMID:40000546 | DOI:10.1007/s10278-025-01436-3

Insights Imaging. 2025 Feb 25;16(1):48. doi: 10.1186/s13244-025-01923-9.

ABSTRACT

OBJECTIVES: To develop and validate a deep learning model based on nnU-Net combined with radiomics to achieve autosegmentation of gastric cancer (GC) and preoperative prediction via the Lauren classification.

METHODS: Patients with a pathological diagnosis of GC were retrospectively enrolled in three medical centers. The nnU-Net autosegmentation model was developed using manually segmented datasets and evaluated by the Dice similarity coefficient (DSC). The CT images were processed by the nnU-Net model to obtain autosegmentation results and extract radiomic features. The least absolute shrinkage and selection operator (LASSO) method selects optimal features for calculating the Radscore and constructing a radiomic model. Clinical characteristics and the Radscore were integrated to construct a combined model. Model performance was evaluated via the receiver operating characteristic (ROC) curve.

RESULTS: A total of 433 GC patients were divided into the training set, internal validation set, external test set-1, and external test set-2. The nnU-Net model achieved a DSC of 0.79 in the test set. The areas under the curve (AUCs) of the internal validation set, external test set-1, and external test set-2 were 0.84, 0.83, and 0.81, respectively, for the radiomic model; and 0.81, 0.81, and 0.82, respectively, for the combined model. The AUCs of the radiomic and combined models showed no statistically significant difference (p > 0.05). The radiomic model was selected as the optimal model.

CONCLUSIONS: The nnU-Net model can efficiently and accurately achieve automatic segmentation of GCs. The radiomic model can preoperatively predict the Lauren classification of GC with high accuracy.

CRITICAL RELEVANCE STATEMENT: This study highlights the potential of nnU-Net combined with radiomics to noninvasively predict the Lauren classification in gastric cancer patients, enhancing personalized treatment strategies and improving patient management.

KEY POINTS: The Lauren classification influences gastric cancer treatment and prognosis. The nnU-Net model reduces doctors' manual segmentation errors and workload. Radiomics models aid in preoperative Lauren classification prediction for patients with gastric cancer.

PMID:40000513 | DOI:10.1186/s13244-025-01923-9

Acad Radiol. 2025 Feb 24:S1076-6332(25)00104-7. doi: 10.1016/j.acra.2025.02.002. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: This study assesses the image quality of temporal bone ultra-high-resolution (UHR) Computed tomography (CT) scans in adults and children using hybrid iterative reconstruction (HIR) and a novel, vendor-specific deep learning-based reconstruction (DLR) algorithm called AiCE Inner Ear.

MATERIAL AND METHODS: In a retrospective, single-center study (February 1-July 30, 2023), UHR-CT scans of 57 temporal bones of 35 patients (5 children, 23 male) with at least one anatomical unremarkable temporal bone were included. There is an adult computed tomography dose index volume (CTDIvol 25.6 mGy) and a pediatric protocol (15.3 mGy). Images were reconstructed using HIR at normal resolution (0.5-mm slice thickness, 512² matrix) and UHR (0.25-mm, 1024² and 2048² matrix) as well as with a vendor-specific DLR advanced intelligent clear-IQ engine inner ear (AiCE Inner Ear) at UHR (0.25-mm, 1024² matrix). Three radiologists evaluated 18 anatomic structures using a 5-point Likert scale. Signal-to-noise (SNR) and contrast-to-noise ratio (CNR) were measured automatically.

RESULTS: In the adult protocol subgroup (n=30; median age: 51 [11-89]; 19 men) and the pediatric protocol subgroup (n=5; median age: 2 [1-3]; 4 men), UHR-CT with DLR significantly improved subjective image quality (p<0.024), reduced noise (p<0.001), and increased CNR and SNR (p<0.001). DLR also enhanced visualization of key structures, including the tendon of the stapedius muscle (p<0.001), tympanic membrane (p<0.009), and basal aspect of the osseous spiral lamina (p<0.018).

CONCLUSION: Vendor-specific DLR-enhanced UHR-CT significantly improves temporal bone image quality and diagnostic performance.

PMID:40000329 | DOI:10.1016/j.acra.2025.02.002

Acad Radiol. 2025 Feb 24:S1076-6332(25)00108-4. doi: 10.1016/j.acra.2025.02.007. Online ahead of print.

ABSTRACT

PURPOSE: This meta-analysis aims to evaluate the diagnostic performance of CT-based artificial intelligence (AI) in diagnosing cervical lymph node metastasis (LNM) of papillary thyroid cancer (PTC).

METHODS: A systematic search was conducted in PubMed, Embase, and Web of Science databases through December 2024, following PRISMA-DTA guidelines. Studies evaluating CT-based AI models for diagnosing cervical LNM in patients with pathologically confirmed PTC were included. The methodological quality was assessed using a modified QUADAS-2 tool. A bivariate random-effects model was used to calculate pooled sensitivity, specificity, and area under the curve (AUC). Heterogeneity was evaluated using I2 statistics, and meta-regression analyses were performed to explore potential sources of heterogeneity.

RESULTS: 17 studies comprising 1778 patients in internal validation sets and 4072 patients in external validation sets were included. In internal validation sets, AI demonstrated a sensitivity of 0.80 (95% CI: 0.71-0.86), specificity of 0.79 (95% CI: 0.73-0.84), and AUC of 0.86 (95% CI: 0.83-0.89). Radiologists suggested comparable performance with sensitivity of 0.77 (95% CI: 0.64-0.87), specificity of 0.79 (95% CI: 0.72-0.85), and AUC of 0.85 (95% CI: 0.81-0.88). Subgroup analyses revealed that deep learning methods outperformed machine learning in sensitivity (0.86 vs 0.72, P<0.05). No significant publication bias was found in internal validation sets for AI diagnosis (P=0.78).

CONCLUSION: CT-based AI showed comparable diagnostic performance to radiologists for detecting cervical LNM in PTC patients, with deep learning models showing superior sensitivity. AI could potentially serve as a valuable diagnostic support tool, though further prospective validation is warranted. Limitations include high heterogeneity among studies and insufficient external validation in diverse populations.

PMID:40000328 | DOI:10.1016/j.acra.2025.02.007

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Dec 25;41(6):1293-1300. doi: 10.7507/1001-5515.202404004.

ABSTRACT

Gastric tumors are neoplastic lesions that occur in the stomach, posing a great threat to human health. Gastric cancer represents the malignant form of gastric tumors, and early detection and treatment are crucial for patient recovery. Endoscopic examination is the primary method for diagnosing gastric tumors. Deep learning techniques can automatically extract features from endoscopic images and analyze them, significantly improving the detection rate of gastric cancer and serving as an important tool for auxiliary diagnosis. This paper reviews relevant literature in recent years, presenting the application of deep learning methods in the classification, object detection, and segmentation of gastric tumor endoscopic images. In addition, this paper also summarizes several computer-aided diagnosis (CAD) systems and multimodal algorithms related to gastric tumors, highlights the issues with current deep learning methods, and provides an outlook on future research directions, aiming to promote the clinical application of deep learning methods in the endoscopic diagnosis of gastric tumors.

PMID:40000222 | DOI:10.7507/1001-5515.202404004

Breast Cancer Res. 2025 Feb 25;27(1):29. doi: 10.1186/s13058-025-01983-1.

ABSTRACT

BACKGROUND: Radiomics and AI have been widely used in breast cancer imaging, but a comprehensive systematic analysis is lacking. Therefore, this study aims to conduct a bibliometrics analysis in this field to discuss its research status and frontier hotspots and provide a reference for subsequent research.

METHODS: Publications related to AI, radiomics, and breast cancer imaging were searched in the Web of Science Core Collection. CiteSpace plotted the relevant co-occurrence network according to authors and keywords. VOSviewer and Pajek were used to draw relevant co-occurrence maps according to country and institution. In addition, R was used to conduct bibliometric analysis of relevant authors, countries/regions, journals, keywords, and annual publications and citations based on the collected information.

RESULTS: A total of 2,701 Web of Science Core Collection publications were retrieved, including 2,486 articles (92.04%) and 215 reviews (7.96%). The number of publications increased rapidly after 2018. The United States of America (n = 17,762) leads in citations, while China (n = 902) leads in the number of publications. Sun Yat-sen University (n = 75) had the largest number of publications. Bin Zheng (n = 28) was the most published author. Nico Karssemeijer (n = 72.1429) was the author with the highest average citations. "Frontiers in Oncology" was the journal with the most publications, and "Radiology" had the highest IF. The keywords with the most frequent occurrence were "breast cancer", "deep learning", and "classification". The topic trends in recent years were "explainable AI", "neoadjuvant chemotherapy", and "lymphovascular invasion".

CONCLUSION: The application of radiomics and AI in breast cancer imaging has received extensive attention. Future research hotspots may mainly focus on the progress of explainable AI in the technical field and the prediction of lymphovascular invasion and neoadjuvant chemotherapy efficacy in clinical application.

PMID:40001088 | DOI:10.1186/s13058-025-01983-1

Radiat Oncol. 2025 Feb 25;20(1):26. doi: 10.1186/s13014-025-02603-0.

ABSTRACT

BACKGROUND: Deformable registration plays an important role in the accurate delineation of tumors. Most of the existing deep learning methods ignored two issues that can lead to inaccurate registration, including the limited field of view in MR scans and the different scanning angles that can exist between multimodal images. The purpose of this study is to improve the registration accuracy between CT and MR for nasopharyngeal carcinoma cases.

METHODS: 269 cases were enrolled in the study, and 188 cases were designated for training, while a separate set of 81 cases was reserved for testing. Each case had a CT volume and a T1-MR volume. The treatment table was removed from their CT images. The CycleFCNs model was used for deformable registration, and two strategies including adaptive mask registration strategy and weight allocation strategy were adopted for training. Dice similarity coefficient, Hausdorff distance, precision, and recall were calculated for normal tissues of CT-MR image pairs, before and after the registration. Three deformable registration methods including RayStation, Elastix, and VoxelMorph were compared with the proposed method.

RESULTS: The registration results of RayStation and Elastix are essentially consistent. Upon employing the VoxelMorph model and the proposed method for registration, a clear trend of increased dice similarity coefficient and decreased hausdorff distance can be observed. It is noteworthy that for the temporomandibular joint, pituitary, optic nerve, and optic chiasma, the proposed method has improved the average dice similarity coefficient from 0.86 to 0.91, 0.87 to 0.93, 0.85 to 0.89, and 0.77 to 0.83, respectively, as compared to RayStation. Additionally, within the same anatomical structures, the average hausdorff distance has been decreased from 2.98 mm to 2.28 mm, 1.83 mm to 1.53 mm, 3.74 mm to 3.56 mm, and 5.94 mm to 5.87 mm. Compared to the original CycleFCNs model, the improved model has significantly enhanced the dice similarity coefficient of the brainstem, pituitary gland, and optic nerve (P < 0.001).

CONCLUSIONS: The proposed method significantly improved the registration accuracy for multi-modal medical images in NPC cases. These findings have important clinical implications, as increased registration accuracy can lead to more precise tumor segmentation, optimized treatment planning, and ultimately, improved patient outcomes.

PMID:40001040 | DOI:10.1186/s13014-025-02603-0

BMC Cancer. 2025 Feb 25;25(1):341. doi: 10.1186/s12885-025-13711-1.

ABSTRACT

OBJECTIVE: We aimed to develop a preoperative clinical radiomics survival prediction model based on the radiomics features via deep learning to provide a reference basis for preoperative assessment and treatment decisions for patients with gallbladder carcinoma (GBC).

METHODS: A total of 168 GBC patients who underwent preoperative upper abdominal enhanced CT from one high-volume medical center between January 2011 to December 2020 were retrospectively analyzed. The region of interest (ROI) was manually outlined by two physicians using 3D Slicer software to establish a nnU-Net model. The DeepSurv survival prediction model was developed by combining radiomics features and preoperative clinical variables.

RESULTS: A total of 1502 radiomics features were extracted from the ROI results based on the nnU-Net model and manual segmentation, and 13 radiomics features were obtained through the 4-step dimensionality reduction methods, respectively. The C-index and AUC of 1-, 2-, and 3-year survival prediction for the nnU-Net based clinical radiomics DeepSurv model was higher than clinical and nnU-Net based radiomics DeepSurv models in the training and testing sets, and close to manual based clinical radiomics DeepSurv model. Delong-test was performed on the AUC of 1-, 2-, and 3-year survival prediction for the two preoperative clinical radiomics DeepSurv prediction models in the testing set, and the results showed that the two models had the same prediction efficiency (all P > 0.05).

CONCLUSIONS: By using the DeepSurv model via nnU-Net segmentation, postoperative survival outcomes for individual gallbladder carcinoma patients could be assessed and stratified, which can provide references for preoperative diagnosis and treatment decisions.

PMID:40001024 | DOI:10.1186/s12885-025-13711-1

BMC Med Imaging. 2025 Feb 25;25(1):61. doi: 10.1186/s12880-025-01602-7.

ABSTRACT

BACKGROUND: To compare the influence of rectal susceptibility artifacts on the subjective evaluation and deep learning (DL) in prostate cancer (PCa) diagnosis.

METHODS: This retrospective two-center study included 1052 patients who underwent MRI and biopsy due to clinically suspected PCa between November 2019 and November 2023. The extent of rectal artifacts in these patients' images was evaluated using the Likert four-level method. The PCa diagnosis was performed by six radiologists and an automated PCa diagnosis DL method. The performance of DL and radiologists was evaluated using the area under the receiver operating characteristic curve (AUC) and the area under the multi-reader multi-case receiver operating characteristic curve, respectively.

RESULTS: Junior radiologists and DL demonstrated statistically significantly higher AUCs in patients without artifacts compared to those with artifacts (R1: 0.73 vs. 0.64; P = 0.01; R2: 0.74 vs. 0.67; P = 0.03; DL: 0.77 vs. 0.61; P < 0.001). In subgroup analysis, no statistically significant differences in the AUC were observed among different grades of rectal artifacts for both all radiologists (0.08 ≤ P ≤ 0.90) and DL models (0.12 ≤ P ≤ 0.96). The AUC for DL without artifacts significantly exceeded those with artifacts in both the peripheral zone (PZ) and transitional zone (TZ) (DLPZ: 0.78 vs. 0.61; P = 0.003; DLTZ: 0.73 vs. 0.59; P = 0.011). Conversely, there were no statistically significant differences in AUC with and without artifacts for all radiologists in PZ and TZ (0.08 ≤ P ≤ 0.98).

CONCLUSIONS: Rectal susceptibility artifacts have significant negative effects on subjective evaluation of junior radiologists and DL.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40000986 | DOI:10.1186/s12880-025-01602-7

Sci Rep. 2025 Feb 25;15(1):6820. doi: 10.1038/s41598-025-91553-4.

ABSTRACT

Monitoring the daily activity levels of black cattle is a crucial aspect of their well-being. The rapid advancements in artificial intelligence have transformed computer vision applications, including object detection, segmentation, and tracking. This has led to more effective and precise monitoring techniques for livestock. In modern cattle farms, video monitoring is essential for analyzing behavior, evaluating health, and predicting estrus events in precision farming. This paper introduces the novel Customized Multi-Camera Multi-Cattle Tracking (MCMCT) system. This unique approach uses four cameras to overcome the challenges of detecting and tracking black cattle in complex open ranch environments. The MCMCT system enhances a tracking-by-detection model with the YOLO v8 segmentation model as the detection backbone network to develop a precision black cattle monitoring system. Single-camera setups in real-world datasets of our open ranches, covering 23.3 m x 20 m with 55 cattle, have limitations in capturing all necessary details. Therefore, a multi-camera solution provides better coverage and more accurate behavior detection of cattle. The effectiveness of the MCMCT system is demonstrated through experimental results, with the YOLOv8-MCMCT system achieving an average Multi-Object Tracking Accuracy (MOTA) of 95.61% across 10 cases of 4 cameras at a processing speed of 30 frames per second. This high accuracy is a testament to the performance of the proposed MCMCT system. Additionally, integrating the Segment Anything Model (SAM) with YOLOv8 enhances the system's capability by automating cattle mask region extraction, reducing the need for manual labeling. Comparative analysis with state-of-the-art deep learning-based tracking methods, including Bot-sort, Byte-track, and OC-sort, further highlights the MCMCT's performance in multi-cattle tracking within complex natural scenes. The advanced algorithms and capabilities of the MCMCT system make it a valuable tool for non-contact automatic livestock monitoring in precision cattle farming. Its adaptability ensures effective performance across varied ranch environments without extensive retraining. This research significantly contributes to livestock monitoring, offering a robust solution for tracking black cattle and enhancing overall agricultural efficiency and management.

PMID:40000894 | DOI:10.1038/s41598-025-91553-4

Commun Med (Lond). 2025 Feb 25;5(1):50. doi: 10.1038/s43856-025-00770-5.

ABSTRACT

BACKGROUND: Huntington's disease, a neurodegenerative disorder, impairs both upper and lower limb function, typically assessed in clinical settings. However, wearable sensors offer the opportunity to monitor real-world data that complements clinical assessments, providing a more comprehensive understanding of disease symptoms.

METHODS: In this study, we monitor upper limb function in individuals with Huntington's disease (HD, n = 16), prodromal HD (pHD, n = 7), and controls (CTR, n = 16) using a wrist-worn wearable sensor over a 7-day period. Goal-directed hand movements are detected through a deep learning model, and kinematic features of each movement are analyzed. The collected data is used to predict disease groups and clinical scores using statistical and machine learning models.

RESULTS: Here we show that significant differences in goal-directed movement features exist between the groups. Additionally, several of these features strongly correlate with clinical scores. Classification models accurately distinguish between HD, pHD, and CTR individuals, achieving a balanced accuracy of 67% and a recall of 0.72 for the HD group. Regression models effectively predict clinical scores.

CONCLUSIONS: This study demonstrates the potential of wearable sensors and machine learning to monitor upper limb function in Huntington's disease, offering a tool for early detection, remote monitoring, and assessing treatment efficacy in clinical trials.

PMID:40000872 | DOI:10.1038/s43856-025-00770-5

Sci Rep. 2025 Feb 25;15(1):6702. doi: 10.1038/s41598-025-91203-9.

ABSTRACT

Generally, music genres have not new established framework, since they are often determined by the composer's background by cultural or historical impact and geographical origin. In this work, a new methodology is presented based on deep learning and metaheuristic algorithms to enhance the performance in music style categorization. The model consists of two main parts: a pre-trained model, a ZFNet, through which high level features are extracted from audio signals and a ResNeXt model for classification. A fractional-order-based variant of the Grey Lag Goose Optimization (FGLGO) algorithm is used to optimize the parameters of ResNeXt to boost the performance of the model. A dual-path recurrent network is employed for real-time music generation and evaluate the model on two benchmark datasets, ISMIR2004 and extended Ballroom, compared to the state-of-the-art models included CNN, PRCNN, BiLSTM and BiRNN. Experimental results show that with accuracy rates of 0.918 on the extended Ballroom dataset and 0.954 on the ISMIR2004 dataset, the proposed model improves accuracy and efficiency incrementally over existing models.

PMID:40000796 | DOI:10.1038/s41598-025-91203-9

Sci Rep. 2025 Feb 25;15(1):6798. doi: 10.1038/s41598-025-91329-w.

ABSTRACT

Ozone pollution affects food production, human health, and the lives of individuals. Due to rapid industrialization and urbanization, Liaocheng has experienced increasing of ozone concentration over several years. Therefore, ozone has become a major environmental problem in Liaocheng City. Long short-term memory (LSTM) and artificial neural network (ANN) models are established to predict ozone concentrations in Liaocheng City from 2014 to 2023. The results show a general improvement in the accuracy of the LSTM model compared to the ANN model. Compared to the ANN, the LSTM has an increase in determination coefficient (R2), value from 0.6779 to 0.6939, a decrease in root mean square error (RMSE) value from 27.9895 μg/m3 to 27.2140 μg/m3 and a decrease in mean absolute error (MAE) value from 21.6919 μg/m3 to 20.8825 μg/m3. The prediction accuracy of the LSTM is superior to the ANN in terms of R, RMSE, and MAE. In summary, LSTM is a promising technique for predicting ozone concentrations. Moreover, by leveraging historical data and LSTM enables accurate predictions of future ozone concentrations on a global scale. This model will open up new avenues for controlling and mitigating ozone pollution.

PMID:40000767 | DOI:10.1038/s41598-025-91329-w

Sci Rep. 2025 Feb 25;15(1):6800. doi: 10.1038/s41598-025-91362-9.

ABSTRACT

This study was conducted to develop and validate a novel deep reinforcement learning (DRL) algorithm incorporating the segment anything model (SAM) to enhance the accuracy of automatic contouring organs at risk during radiotherapy for cervical cancer patients. CT images were collected from 150 cervical cancer patients treated at our hospital between 2021 and 2023. Among these images, 122 CT images were used as a training set for the algorithm training of the DRL model based on the SAM model, and 28 CT images were used for the test set. The model's performance was evaluated by comparing its segmentation results with the ground truth (manual contouring) obtained through manual contouring by expert clinicians. The test results were compared with the contouring results of commercial automatic contouring software based on the deep learning (DL) algorithm model. The Dice similarity coefficient (DSC), 95th percentile Hausdorff distance, average symmetric surface distance (ASSD), and relative absolute volume difference (RAVD) were used to quantitatively assess the contouring accuracy from different perspectives, enabling the contouring results to be comprehensively and objectively evaluated. The DRL model outperformed the DL model across all evaluated metrics. DRL achieved higher median DSC values, such as 0.97 versus 0.96 for the left kidney (P < 0.001), and demonstrated better boundary accuracy with lower HD95 values, e.g., 14.30 mm versus 17.24 mm for the rectum (P < 0.001). Moreover, DRL exhibited superior spatial agreement (median ASSD: 1.55 mm vs. 1.80 mm for the rectum, P < 0.001) and volume prediction accuracy (median RAVD: 10.25 vs. 10.64 for the duodenum, P < 0.001). These findings indicate that integrating SAM with RL (reinforcement learning) enhances segmentation accuracy and consistency compared to conventional DL methods. The proposed approach introduces a novel training strategy that improves performance without increasing model complexity, demonstrating its potential applicability in clinical practice.

PMID:40000766 | DOI:10.1038/s41598-025-91362-9