Adv Mater. 2025 Mar 3:e2416480. doi: 10.1002/adma.202416480. Online ahead of print.

ABSTRACT

The interlayer displacement has recently emerged as a crucial tuning parameter to control diverse physical properties in layered crystals. Transmission electron microscopy (TEM), an exceptionally powerful tool for structural analysis, directly observes the interlayer stacking and strain fields in various crystals. However, conventional analysis methods based on high-resolution phase-contrast TEM images are inadequate for recognizing spatially varying unit-cell patterns and their associated structure factors, hindering precise determination of interlayer displacements. Here, a deep learning-based analysis is introduced for atomic resolution TEM images, enabling unit-cell pattern recognition and precise identification of interlayer stacking displacement in bilayer phosphorene. The deep learning model applied to bilayer phosphorene accurately determines stacking displacement, with an error level of 3.3% displacement within the unit cell and a spatial resolution approaching the individual unit-cell level. Additionally, the model successfully processes a large set of in situ TEM data, capturing spatially varying, time-dependent interlayer displacement dynamics associated with edge reconstruction, demonstrating its potential for processing large-scale microscopy datasets.

PMID:40026027 | DOI:10.1002/adma.202416480

J Xray Sci Technol. 2025 Mar 3:8953996251317412. doi: 10.1177/08953996251317412. Online ahead of print.

ABSTRACT

BACKGROUND:: Myocardial blood flow (MBF) provides important diagnostic information for myocardial ischemia. However, dynamic computed tomography perfusion (CTP) needed for MBF involves multiple exposures, leading to high radiation doses.

OBJECTIVES:: This study investigated synthesizing MBF from simulated static myocardial CTP to explore dose reduction potential, bypassing the traditional dynamic input function.

METHODS:: The study included 253 subjects with intermediate-to-high pretest probabilities of obstructive coronary artery disease (CAD). MBF was reconstructed from dynamic myocardial CTP. A deep neural network (DNN) converted simulated static CTP into synthetic MBF. Beyond the usual image quality evaluation, the synthetic MBF was segmented and a clinical functional assessment was conducted, with quantitative analysis for consistency and correlation.

RESULTS:: Synthetic MBF closely matched the referenced MBF, with an average structure similarity (SSIM) of 0.87. ROC analysis of ischemic segments showed an area under curve (AUC) of 0.915 for synthetic MBF. This method can theoretically reduce the radiation dose for MBF significantly, provided satisfactory static CTP is obtained, reducing reliance on high time resolution of dynamic CTP.

CONCLUSIONS:: The proposed method is feasible, with satisfactory clinical functionality of synthetic MBF. Further investigation and validation are needed to confirm actual dose reduction in clinical settings.

PMID:40026015 | DOI:10.1177/08953996251317412

J Xray Sci Technol. 2025 Mar 3:8953996241308759. doi: 10.1177/08953996241308759. Online ahead of print.

ABSTRACT

Computed tomography (CT) image reconstruction is faced with challenge of balancing image quality and radiation dose. Recent unrolled optimization methods address low-dose CT image quality issues using convolutional neural networks or self-attention mechanisms as regularization operators. However, these approaches have limitations in adaptability, computational efficiency, or preservation of beneficial inductive biases. They also depend on initial reconstructions, potentially leading to information loss and error propagation. To overcome these limitations, Kernel Basis Attention Primal-Dual Network (KBA-PDNet) is proposed. The method unrolls multiple iterations of the proximal primal-dual optimization process, replacing traditional proximal operators with Kernel Basis Attention (KBA) modules. This design enables direct training from raw measurement data without relying on preliminary reconstructions. The KBA module achieves adaptability by learning and dynamically fusing kernel bases, generating customized convolution kernels for each spatial location. This approach maintains computational efficiency while preserving beneficial inductive biases of convolutions. By training end-to-end from raw projection data, KBA-PDNet fully utilizes all original information, potentially capturing details lost in preliminary reconstructions. Experiments on simulated and clinical datasets demonstrate that KBA-PDNet outperforms existing approaches in both image quality and computational efficiency.

PMID:40026009 | DOI:10.1177/08953996241308759

Small Methods. 2025 Mar 3:e2401616. doi: 10.1002/smtd.202401616. Online ahead of print.

ABSTRACT

Structured illumination microscopy (SIM) has emerged as a pivotal super-resolution technique in biological imaging. This review aims to introduce the fundamental principles of SIM, primarily focuses on the latest developments in super-resolution SIM imaging, such as the light illumination and modulation devices, and the image reconstruction algorithms. Additionally, the application of deep learning (DL) technology in SIM imaging is explored, which is employed to enhance image quality, accelerate imaging and reconstruction speed or replace the current image reconstruction method. Furthermore, the key evaluation metrics are proposed and discussed for assessment of deep-learning neural networks, especially for their employment in SIM. Finally, the future integration of artificial intelligence (AI) with SIM system and the perspective of smart microscope are also discussed.

PMID:40025917 | DOI:10.1002/smtd.202401616

J Appl Clin Med Phys. 2025 Mar 2:e70066. doi: 10.1002/acm2.70066. Online ahead of print.

ABSTRACT

PURPOSE: Reducing radiation dose attributed to computed tomography (CT) may compromise the accuracy of organ segmentation, an important step in 177Lu DOTATATE therapy that affects both activity and mass estimates. This study aimed to facilitate CT dose reduction using deep learning methods for patients undergoing serial single photon emission computed tomography (SPECT)/CT imaging during 177Lu DOTATATE therapy.

METHODS: The 177Lu DOTATATE patient dataset hosted in Deep Blue Data was used in this study. The noise insertion method incorporating the effect of bowtie filter, automatic exposure control, and electronic noise was applied to simulate images at four reduced dose levels. Organ segmentation was carried out using the TotalSegmentator model, while image denoising was performed with the DenseNet model. The impact of segmentation performance on the dosimetry accuracy of 177Lu DOTATATE therapy was quantified by calculating the percent difference between a dose rate map segmented with a reference mask and the same dose rate map segmented with a test mask (PDdose) for spleen, right kidney, left kidney, and liver.

RESULTS: Before denoising, the mean ± standard deviation of PDdose for all critical organs were 2.31 ± 2.94%, 4.86 ± 9.42%, 8.39 ± 14.76%, 12.95 ± 19.99% in CT images at dose levels down to 20%, 10%, 5%, 2.5% of the normal dose, respectively. After denoising, the corresponding results were 1.69 ± 2.25%, 2.84 ± 4.46%, 3.72 ± 4.22%, 7.98 ± 15.05% in CT images at dose levels down to 20%, 10%, 5%, 2.5% of the normal dose, respectively.

CONCLUSION: As dose reduction increased, CT image segmentation gradually deteriorated, which in turn deteriorated the dosimetry accuracy of 177Lu DOTATATE therapy. Improving CT image quality through denoising could enhance 177Lu DOTATATE dosimetry, making it a valuable tool to support CT dose reduction for patients undergoing serial SPECT/CT imaging during treatment.

PMID:40025651 | DOI:10.1002/acm2.70066

Int J Lab Hematol. 2025 Mar 2. doi: 10.1111/ijlh.14455. Online ahead of print.

ABSTRACT

INTRODUCTION: Von Willebrand factor (VWF) multimer analysis is essential for diagnosing and classifying von Willebrand disease (VWD) but requires expert interpretation and is subject to inter-rater variability. We developed an automated image analysis pipeline using deep learning to improve the reproducibility and efficiency of VWF multimer pattern classification.

METHODS: We trained a YOLOv8 deep learning model on 514 gel images (6168 labeled instances) to classify VWF multimer patterns into 12 classes. The model was validated on 192 images (2304 instances) and tested on an independent set of 94 images (1128 instances). Images underwent preprocessing, including histogram equalization, contrast enhancement, and gamma correction. Two expert raters provided ground truth classifications.

RESULTS: The model achieved 91% accuracy compared to Expert 1 (macro-averaged precision = 0.851, recall = 0.757, F1-score = 0.786) and 87% accuracy compared to Expert 2 (macro-averaged precision = 0.653, recall = 0.653, F1-score = 0.641). Inter-rater agreement was very high between experts (κ = 0.883), with strong agreement between the model and Expert 1 (κ = 0.845) and good agreement with Expert 2 (κ = 0.773). The model performed exceptionally well on common patterns (F1 > 0.93) but showed lower performance on rare subtypes.

CONCLUSION: Automated VWF multimer analysis using deep learning demonstrates high accuracy in pattern classification and could standardize the interpretation of VWF multimer patterns. While not replacing expert analysis, this approach could improve the efficiency of expert human review, potentially streamlining laboratory workflow and expanding access to VWF multimer testing.

PMID:40025642 | DOI:10.1111/ijlh.14455

Sci Rep. 2025 Mar 1;15(1):7266. doi: 10.1038/s41598-025-91786-3.

ABSTRACT

Cervical cancer, arising from the cells of the cervix, the lower segment of the uterus connected to the vagina-poses a significant health threat. The microscopic examination of cervical cells using Pap smear techniques plays a crucial role in identifying potential cancerous alterations. While developed nations demonstrate commendable efficiency in Pap smear acquisition, the process remains laborious and time-intensive. Conversely, in less developed regions, there is a pressing need for streamlined, computer-aided methodologies for the pre-analysis and treatment of cervical cancer. This study focuses on the classification of squamous cells into five distinct classes, providing a nuanced assessment of cervical cancer severity. Utilizing a dataset comprising over 4096 images from SimpakMed, available on Kaggle, we employed ensemble technique which included the Convolutional Neural Network (CNN), AlexNet, and SqueezeNet for image classification, achieving accuracies of 90.8%, 92%, and 91% respectively. Particularly noteworthy is the proposed ensemble technique, which surpasses individual model performances, achieving an impressive accuracy of 94%. This ensemble approach underscores the efficacy of our method in precise squamous cell classification and, consequently, in gauging the severity of cervical cancer. The results represent a promising advancement in the development of more efficient diagnostic tools for cervical cancer in resource-constrained settings.

PMID:40025091 | DOI:10.1038/s41598-025-91786-3

Sci Rep. 2025 Mar 1;15(1):7291. doi: 10.1038/s41598-025-89612-x.

ABSTRACT

The traditional molecular-based identification (TMID) technique of new infections from genome sequences (GSs) has made significant contributions so far. However, due to the sensitive nature of the medical data, the TMID technique of transferring the patient's data to the central machine or server may create severe privacy and security issues. In recent years, the progression of deep federated learning (DFL) and its remarkable success in many domains has guided as a potential solution in this field. Therefore, we proposed a dependable and privacy-preserving DFL-based identification model of new infections from GSs. The unique contributions include automatic effective feature selection, which is best suited for identifying new infections, designing a dependable and privacy-preserving DFL-based LeNet model, and evaluating real-world data. To this end, a comprehensive experimental performance evaluation has been conducted. Our proposed model has an overall accuracy of 99.12% after independently and identically distributing the dataset among six clients. Moreover, the proposed model has a precision of 98.23%, recall of 98.04%, f1-score of 96.24%, Cohen's kappa of 83.94%, and ROC AUC of 98.24% for the same configuration, which is a noticeable improvement when compared to the other benchmark models. The proposed dependable model, along with empirical results, is encouraging enough to recognize as an alternative for identifying new infections from other virus strains by ensuring proper privacy and security of patients' data.

PMID:40025035 | DOI:10.1038/s41598-025-89612-x

Biomed Eng Online. 2025 Mar 2;24(1):27. doi: 10.1186/s12938-025-01355-y.

ABSTRACT

BACKGROUND: To develop and validate a model that integrates clinical data, deep learning radiomics, and radiomic features to predict high-risk patients for cage subsidence (CS) after lumbar fusion.

METHODS: This study analyzed preoperative CT and MRI data from 305 patients undergoing lumbar fusion surgery from three centers. Using a deep learning model based on 3D vision transformations, the data were divided the dataset into training (n = 214), validation (n = 61), and test (n = 30) groups. Feature selection was performed using LASSO regression, followed by the development of a logistic regression model. The predictive ability of the model was assessed using various machine learning algorithms, and a combined clinical model was also established.

RESULTS: Ultimately, 11 traditional radiomic features, 5 deep learning radiomic features, and 1 clinical feature were selected. The combined model demonstrated strong predictive performance, with area under the curve (AUC) values of 0.941, 0.832, and 0.935 for the training, validation, and test groups, respectively. Notably, our model outperformed predictions made by two experienced surgeons.

CONCLUSIONS: This study developed a robust predictive model that integrates clinical features and imaging data to identify high-risk patients for CS following lumbar fusion. This model has the potential to improve clinical decision-making and reduce the need for revision surgeries, easing the burden on healthcare systems.

PMID:40025592 | DOI:10.1186/s12938-025-01355-y

J Cheminform. 2025 Mar 2;17(1):27. doi: 10.1186/s13321-025-00975-9.

ABSTRACT

Molecular optimization is a crucial step in drug development, involving structural modifications to improve the desired properties of drug candidates. Although many deep-learning-based molecular optimization algorithms have been proposed and may perform well on benchmarks, they usually do not pay sufficient attention to the synthesizability of molecules, resulting in optimized compounds difficult to be synthesized. To address this issue, we first developed a general pipeline capable of constructing functional reaction template library specific to any property where a predictive model can be built. Based on these functional templates, we introduced Syn-MolOpt, a synthesis planning-oriented molecular optimization method. During optimization, functional reaction templates steer the process towards specific properties by effectively transforming relevant structural fragments. In four diverse tasks, including two toxicity-related (GSK3β-Mutagenicity and GSK3β-hERG) and two metabolism-related (GSK3β-CYP3A4 and GSK3β-CYP2C19) multi-property molecular optimizations, Syn-MolOpt outperformed three benchmark models (Modof, HierG2G, and SynNet), highlighting its efficacy and adaptability. Additionally, visualization of the synthetic routes for molecules optimized by Syn-MolOpt confirms the effectiveness of functional reaction templates in molecular optimization. Notably, Syn-MolOpt's robust performance in scenarios with limited scoring accuracy demonstrates its potential for real-world molecular optimization applications. By considering both optimization and synthesizability, Syn-MolOpt promises to be a valuable tool in molecular optimization.Scientific contribution Syn-MolOpt takes into account both molecular optimization and synthesis, allowing for the design of property-specific functional reaction template libraries for the properties to be optimized, and providing reference synthesis routes for the optimized compounds while optimizing the targeted properties. Syn-MolOpt's universal workflow makes it suitable for various types of molecular optimization tasks.

PMID:40025591 | DOI:10.1186/s13321-025-00975-9

J Cheminform. 2025 Mar 2;17(1):28. doi: 10.1186/s13321-025-00973-x.

ABSTRACT

Computer-aided drug design has the potential to significantly reduce the astronomical costs of drug development, and molecular docking plays a prominent role in this process. Molecular docking is an in silico technique that predicts the bound 3D conformations of two molecules, a necessary step for other structure-based methods. Here, we describe version 1.3 of the open-source molecular docking software GNINA. This release updates the underlying deep learning framework to PyTorch, resulting in more computationally efficient docking and paving the way for seamless integration of other deep learning methods into the docking pipeline. We retrained our CNN scoring functions on the updated CrossDocked2020 v1.3 dataset and introduce knowledge-distilled CNN scoring functions to facilitate high-throughput virtual screening with GNINA. Furthermore, we add functionality for covalent docking, where an atom of the ligand is covalently bound to an atom of the receptor. This update expands the scope of docking with GNINA and further positions GNINA as a user-friendly, open-source molecular docking framework. GNINA is available at https://github.com/gnina/gnina .Scientific contributions: GNINA 1.3 is an open source a molecular docking tool with enhanced support for covalent docking and updated deep learning models for more effective docking and screening.

PMID:40025560 | DOI:10.1186/s13321-025-00973-x

BMC Oral Health. 2025 Mar 1;25(1):329. doi: 10.1186/s12903-025-05677-0.

ABSTRACT

BACKGROUND: Several commercial programs incorporate artificial intelligence in diagnosis, but very few dental professionals have been surveyed regarding its acceptability and usability. Furthermore, few have explored how these advances might be incorporated into routine practice.

METHODS: Our team developed and implemented a deep learning (DL) model employing semantic segmentation neural networks and object detection networks to precisely identify alveolar bone crestal levels (ABCLs) and cemento-enamel junctions (CEJs) to measure change in alveolar crestal height (ACH). The model was trained and validated using a 550 bitewing radiograph dataset curated by an oral radiologist, setting a gold standard for ACH measurements. A twenty-question survey was created to compare the accuracy and efficiency of manual X-ray examination versus the application and to assess the acceptability and usability of the application.

RESULTS: In total, 56 different dental professionals classified severe (ACH > 5 mm) vs. non-severe (ACH ≤ 5 mm) periodontal bone loss on 35 calculable ACH measures. Dental professionals accurately identified between 35-87% of teeth with severe periodontal disease, whereas the artificial intelligence (AI) application achieved an 82-87% accuracy rate. Among the 65 participants who completed the acceptability and usability survey, more than half the participants (52%) were from an academic setting. Only 21% of participants reported that they already used automated or AI-based software in their practice to assist in reading of X-rays. The majority, 57%, stated that they only approximate when measuring bone levels and only 9% stated that they measure with a ruler. The survey indicated that 84% of participants agreed or strongly agreed with the AI application measurement of ACH. Furthermore, 56% of participants agreed that AI would be helpful in their professional setting.

CONCLUSION: Overall, the study demonstrates that an AI application for detecting alveolar bone has high acceptability among dental professionals and may provide benefits in time saving and increased clinical accuracy.

PMID:40025477 | DOI:10.1186/s12903-025-05677-0

BMC Oral Health. 2025 Mar 1;25(1):328. doi: 10.1186/s12903-024-05231-4.

ABSTRACT

BACKGROUND: The aim of our study was to develop and evaluate a deep learning model (BiStageNet) for automatic detection of dens evaginatus (DE) premolars on orthodontic intraoral photographs. Additionally, based on the training results, we developed a DE detection platform for orthodontic clinical applications.

METHODS: We manually selected the premolar areas for automatic premolar recognition training using a dataset of 1,400 high-quality intraoral photographs. Next, we labeled each premolar for DE detection training using a dataset of 2,128 images. We introduced the Dice coefficient, accuracy, sensitivity, specificity, F1-score, ROC curve as well as areas under the ROC curve to evaluate the learning results of our model. Finally, we constructed an automatic DE detection platform based on our trained model (BiStageNet) using Pytorch.

RESULTS: Our DE detection platform achieved a mean Dice coefficient of 0.961 in premolar recognition, with a diagnostic accuracy of 85.0%, sensitivity of 88.0%, specificity of 82.0%, F1 Score of 0.854, and AUC of 0.93. Experimental results revealed that dental interns, when manually identifying DE, showed low specificity. With the tool's assistance, specificity significantly improved for all interns, effectively reducing false positives without sacrificing sensitivity. This led to enhanced diagnostic precision, evidenced by improved PPV, NPV, and F1-Scores.

CONCLUSION: Our BiStageNet was capable of recognizing premolars and detecting DE with high accuracy on intraoral photographs. On top of that, our self-developed DE detection platform was promising for clinical application and promotion.

PMID:40025464 | DOI:10.1186/s12903-024-05231-4

Commun Med (Lond). 2025 Mar 1;5(1):55. doi: 10.1038/s43856-025-00767-0.

ABSTRACT

BACKGROUND: Glioblastoma is a highly heterogeneous brain tumor, posing challenges for precision therapies and patient stratification in clinical trials. Understanding how genetic mutations influence tumor imaging may improve patient management and treatment outcomes. This study investigates the relationship between imaging features, spatial patterns of tumor location, and genetic alterations in IDH-wildtype glioblastoma, as well as the likely sequence of mutational events.

METHODS: We conducted a retrospective analysis of 357 IDH-wildtype glioblastomas with pre-operative multiparametric MRI and targeted genetic sequencing data. Radiogenomic signatures and spatial distribution maps were generated for key mutations in genes such as EGFR, PTEN, TP53, and NF1 and their corresponding pathways. Machine and deep learning models were used to identify imaging biomarkers and stratify tumors based on their genetic profiles and molecular heterogeneity.

RESULTS: Here, we show that glioblastoma mutations produce distinctive imaging signatures, which are more pronounced in tumors with less molecular heterogeneity. These signatures provide insights into how mutations affect tumor characteristics such as neovascularization, cell density, invasion, and vascular leakage. We also found that tumor location and spatial distribution correlate with genetic profiles, revealing associations between tumor regions and specific oncogenic drivers. Additionally, imaging features reflect the cross-sectionally inferred evolutionary trajectories of glioblastomas.

CONCLUSIONS: This study establishes clinically accessible imaging biomarkers that capture the molecular composition and oncogenic drivers of glioblastoma. These findings have potential implications for noninvasive tumor profiling, personalized therapies, and improved patient stratification in clinical trials.

PMID:40025245 | DOI:10.1038/s43856-025-00767-0

Sci Rep. 2025 Mar 1;15(1):7301. doi: 10.1038/s41598-025-91823-1.

ABSTRACT

The classification of tongue shapes is essential for objective tongue diagnoses. However, the accuracy of classification is influenced by numerous factors. First, considerable differences exist between individuals with the same tongue shape. Second, the lips interfere with tongue shape classification. Additionally, small datasets make it difficult to conduct network training. To address these issues, this study builds a two-level nested tongue segmentation and tongue image classification network named IF-RCNet based on feature fusion and mixed input methods. In IF-RCNet, RCA-UNet is used to segment the tongue body, and RCA-Net is used to classify the tongue shape. The feature fusion strategy can enhance the network's ability to extract tongue features, and the mixed input can expand the data input of RCA-Net. The experimental results show that tongue shape classification based on IF-RCNet outperforms many other classification networks (VGG 16, ResNet 18, AlexNet, ViT and MobileNetv4). The method can accurately classify tongues despite the negative effects of differences between homogeneous tongue shapes and the misclassification of normal versus bulgy tongues due to lip interference. The method exhibited better performance on a small dataset of tongues, thereby enhancing the accuracy of tongue shape classification and providing a new approach for tongue shape classification.

PMID:40025207 | DOI:10.1038/s41598-025-91823-1

Sci Rep. 2025 Mar 1;15(1):7337. doi: 10.1038/s41598-025-92105-6.

ABSTRACT

Machine learning approaches including deep learning models have shown promising performance in the automatic detection of Parkinson's disease. These approaches rely on different types of data with voice recordings being the most used due to the convenient and non-invasive nature of data acquisition. Our group has successfully developed a novel approach that uses convolutional neural network with transfer learning to analyze spectrogram images of the sustained vowel /a/ to identify people with Parkinson's disease. We tested this approach by collecting a dataset of voice recordings via analog telephone lines, which support limited bandwidth. The convolutional neural network with transfer learning approach showed superior performance against conventional machine learning methods that collapse measurements across time to generate feature vectors. This study builds upon our prior results and presents two novel contributions: First, we tested the performance of our approach on a larger voice dataset recorded using smartphones with wide bandwidth. Our results show comparable performance between two datasets generated using different recording platforms despite the differences in most important features resulting from the limited bandwidth of analog telephonic lines. Second, we compared the classification performance achieved using linear-scale and mel-scale spectrogram images and showed a small but statistically significant gain using mel-scale spectrograms.

PMID:40025201 | DOI:10.1038/s41598-025-92105-6

NPJ Digit Med. 2025 Mar 1;8(1):133. doi: 10.1038/s41746-025-01527-z.

ABSTRACT

This systematic review evaluated natural language processing (NLP) approaches for detecting cognitive impairment in electronic health record clinical notes. Following PRISMA guidelines, we analyzed 18 studies (n = 1,064,530) that employed rule-based algorithms (67%), traditional machine learning (28%), and deep learning (17%). NLP models demonstrated robust performance in identifying cognitive decline, with median sensitivity 0.88 (IQR 0.74-0.91) and specificity 0.96 (IQR 0.81-0.99). Deep learning architectures achieved superior results, with area under the receiver operating characteristic curves up to 0.997. Major implementation challenges included incomplete electronic health record data capture, inconsistent clinical documentation practices, and limited external validation. While NLP demonstrates promise, successful clinical translation requires establishing standardized approaches, improving access to annotated datasets, and developing equitable deployment frameworks.

PMID:40025194 | DOI:10.1038/s41746-025-01527-z

Sci Rep. 2025 Mar 1;15(1):7327. doi: 10.1038/s41598-025-91839-7.

ABSTRACT

As the water economy advances and the concepts of water ecology protection and sustainable development take root in people's minds, underwater imaging equipment has made remarkable progress. However, due to various factors, underwater images still suffer from low quality. How to enhance the quality of underwater images so that people can understand them quickly has become a crucial issue. Therefore, aiming at the degradation problems such as detail blurring, color imbalance, and noise interference in low-quality underwater images, this paper proposes an optimized UNet framework with a joint loss function (OUNet-JL). Firstly, to alleviate the problem of detail blurring, we construct a multi-residual module (MRM) to enhance the ability to represent detail features by using serially stacked convolutional blocks and residual connections. Secondly, we build a spatial multi-scale feature extraction module fused with channel attention (SMFM) to address the color imbalance issue through multi-scale dilated convolution and channel attention. Thirdly, to improve the signal-to-noise ratio of the enhanced image and solve the problem of blurring distortion, a strengthen-operate-subtract feature reconstruction module (SOSFM) is presented. Fourthly, to guide the network to perform training more efficiently and help it converge rapidly, a joint loss function is designed by integrating four different loss functions. Extensive experiments conducted on the well-known UIEB and UFO-120 datasets have shown the superiority of our OUNet-JL compared with several state-of-the-art algorithms. Moreover, ablation studies have also verified the effectiveness of the proposed modules. Our source code is publicly available at https://github.com/WangXin81/OUNet_JL .

PMID:40025128 | DOI:10.1038/s41598-025-91839-7

Pest Manag Sci. 2025 Feb 28. doi: 10.1002/ps.8728. Online ahead of print.

ABSTRACT

BACKGROUND: Precision weed mapping in turf according to its susceptibility to selective herbicides allows the smart sprayer to spot-spray the most pertinent herbicides onto the susceptible weeds. The objective of this study was to evaluate the feasibility of implementing herbicide susceptibility-based weed mapping using deep convolutional neural networks (DCNNs) to facilitate targeted and efficient herbicide applications. Additionally, applying path-planning algorithms to weed mapping data to guide the spraying nozzle ensures minimal travel paths for herbicide application.

RESULTS: DenseNet achieved high precision, recall, overall accuracy, and F1 score values for all categories of herbicides and no herbicides, with F1 scores ranging from 0.996 to 0.999 in the validation dataset and from 0.992 to 0.997 in the testing dataset. The average accuracies attained by DenseNet, GoogLeNet and ResNet were 0.9985, 0.9953 and 0.9980, respectively. By considering both accuracy and computational efficiency, the ResNet model was identified as the most effective among the models compared to weed detection. The performance of the Christofides, Greedy and 2-opt algorithms in optimizing path planning for single or dual spraying nozzles was compared and analyzed. The Greedy algorithm proved the most efficient in optimizing the nozzle's trajectory.

CONCLUSION: Implementing herbicide susceptibility-based weed mapping facilitates targeted herbicide application by directing the nozzle to the grid cells containing the weeds susceptible to the herbicides. Moreover, the strategic integration of herbicide susceptibility-based weed mapping with optimized path planning for the spraying mechanism can be adeptly implemented on smart sprayers, which could effectively reduce the herbicide input. © 2025 Society of Chemical Industry.

PMID:40022516 | DOI:10.1002/ps.8728

J Cachexia Sarcopenia Muscle. 2025 Apr;16(2):e13728. doi: 10.1002/jcsm.13728.

ABSTRACT

BACKGROUND: Age-related conditions, such as osteoporosis and sarcopenia, alongside chronic diseases, can result in significant musculoskeletal tissue loss. This impacts individuals' quality of life and increases risk of falls and fractures. Computed tomography (CT) has been widely used for assessing musculoskeletal tissues. Although automatic techniques have been investigated for segmenting tissues in the abdomen and mid-thigh regions, studies in proximal hip remain limited. This study aims to develop a deep learning technique for segmentation and quantification of musculoskeletal tissues in CT scans of proximal hip.

METHODS: We examined 300 participants (men, 73 ± 6 years) from two cohorts of the Osteoporotic Fractures in Men Study (MrOS). We manually segmented cortical bone, trabecular bone, marrow adipose tissue (MAT), haematopoietic bone marrow (HBM), muscle, intermuscular adipose tissue (IMAT) and subcutaneous adipose tissue (SAT) from CT scan images at the proximal hip level. Using these data, we trained a U-Net-like deep learning model for automatic segmentation. The association between model-generated quantitative results and outcome variables such as grip strength, chair sit-to-stand time, walking speed, femoral neck and spine bone mineral density (BMD), and total lean mass was calculated.

RESULTS: An average Dice similarity coefficient (DSC) above 90% was observed across all tissue types in the test dataset. Grip strength showed positive correlations with cortical bone area (coefficient: 0.95, 95% confidence interval: [0.10, 1.80]), muscle area (0.41, [0.19, 0.64]) and average Hounsfield unit for muscle adjusted for height squared (AHU/h2) (1.1, [0.53, 1.67]), while it was negatively correlated with IMAT (-1.45, [-2.21, -0.70]) and SAT (-0.32, [-0.50, -0.13]). Gait speed was directly related to muscle area (0.01, [0.00, 0.02]) and inversely to IMAT (-0.04, [-0.07, -0.01]), while chair sit-to-stand time was associated with muscle area (0.98, [0.98, 0.99]), IMAT area (1.04, [1.01, 1.07]), SAT area (1.01, [1.01, 1.02]) and AHU/h2 for muscle (0.97, [0.95, 0.99]). MAT area showed a potential link to non-trauma fractures post-50 years (1.67, [0.98, 2.83]). Femoral neck BMD was associated with cortical bone (0.09, [0.08, 0.10]), MAT (-0.11, [-0.13, -0.10]), MAT adjusted for total bone marrow area (-0.06, [-0.07, -0.05]) and AHU/h2 for muscle (0.01, [0.00, 0.02]). Total spine BMD showed similar associations and with AHU for muscle (0.02, [0.00, 0.05]). Total lean mass was correlated with cortical bone (517.3, [148.26, 886.34]), trabecular bone (924, [262.55, 1585.45]), muscle (381.71, [291.47, 471.96]), IMAT (-1096.62, [-1410.34, -782.89]), SAT (-413.28, [-480.26, -346.29]), AHU (527.39, [159.12, 895.66]) and AHU/h2 (300.03, [49.23, 550.83]).

CONCLUSION: Our deep learning-based technique offers a fast and accurate method for segmentation and quantification of musculoskeletal tissues in proximal hip, with potential clinical value.

PMID:40022453 | DOI:10.1002/jcsm.13728