Radiology. 2025 Feb;314(2):e241351. doi: 10.1148/radiol.241351.

ABSTRACT

Background Deep learning (DL) methods enable faster shoulder MRI than conventional methods, but arthroscopy-validated evidence of good diagnostic performance is scarce. Purpose To validate the clinical efficacy of 7-minute threefold parallel imaging (PIx3)-accelerated DL super-resolution shoulder MRI against arthroscopic findings. Materials and Methods Adults with painful shoulder conditions who underwent PIx3-accelerated DL super-resolution 3-T shoulder MRI and arthroscopy between March and November 2023 were included in this retrospective study. Seven radiologists independently evaluated the MRI scan quality parameters and the presence of artifacts (Likert scale rating ranging from 1 [very bad/severe] to 5 [very good/absent]) as well as the presence of rotator cuff tears, superior and anteroinferior labral tears, biceps tendon tears, cartilage defects, Hill-Sachs lesions, Bankart fractures, and subacromial-subdeltoid bursitis. Interreader agreement based on κ values was evaluated, and diagnostic performance testing was conducted. Results A total of 121 adults (mean age, 55 years ± 14 [SD]; 75 male) who underwent MRI and arthroscopy within a median of 39 days (range, 1-90 days) were evaluated. The overall image quality was good (median rating, 4 [IQR, 4-4]), with high reader agreement (κ ≥ 0.86). Motion artifacts and image noise were minimal (rating of 4 [IQR, 4-4] for each), and reconstruction artifacts were absent (rating of 5 [IQR, 5-5]). Arthroscopy-validated abnormalities were detected with good or better interreader agreement (κ ≥ 0.68). The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were 89%, 90%, 89%, and 0.89, respectively, for supraspinatus-infraspinatus tendon tears; 82%, 63%, 68%, and 0.68 for subscapularis tendon tears; 93%, 73%, 86%, and 0.83 for superior labral tears; 100%, 100%, 100%, and 1.00 for anteroinferior labral tears; 68%, 90%, 82%, and 0.80 for biceps tendon tears; 42%, 93%, 81%, and 0.64 for cartilage defects; 93%, 99%, 98%, and 0.94 for Hill-Sachs deformities; 100%, 99%, 99%, and 1.00 for osseous Bankart lesions; and 97%, 63%, 92%, and 0.80 for subacromial-subdeltoid bursitis. Conclusion Seven-minute PIx3-accelerated DL super-resolution 3-T shoulder MRI has good diagnostic performance for diagnosing tendinous, labral, and osteocartilaginous abnormalities. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Tuite in this issue.

PMID:39964264 | DOI:10.1148/radiol.241351

Radiology. 2025 Feb;314(2):e240473. doi: 10.1148/radiol.240473.

ABSTRACT

Background Individuals eligible for lung cancer screening with low-dose CT face a higher cardiovascular mortality risk. Purpose To investigate the association between changes in epicardial adipose tissue (EAT) at the 2-year interval and mortality in individuals undergoing serial low-dose CT lung cancer screening. Materials and Methods This secondary analysis of the National Lung Screening Trial obtained EAT volume and density from serial low-dose CT scans using a validated automated deep learning algorithm. EAT volume and density changes over 2 years were categorized into typical (decrease of 7% to increase of 11% and decrease of 3% to increase of 2%, respectively) and atypical (increase or decrease beyond typical) changes, which were associated with all-cause, cardiovascular, and lung cancer mortality. Uni- and multivariable Cox proportional hazard regression models-adjusted for baseline EAT values, age, sex, race, ethnicity, smoking, pack-years, heart disease or myocardial infarction, stroke, hypertension, diabetes, education status, body mass index, and coronary artery calcium-were performed. Results Among 20 661 participants (mean age, 61.4 years ± 5.0 [SD]; 12 237 male [59.2%]), 3483 (16.9%) died over a median follow-up of 10.4 years (IQR, 9.9-10.8 years) (cardiovascular related: 816 [23.4%]; lung cancer related: 705 [20.2%]). Mean EAT volume increased (2.5 cm3/m2 ± 11.0) and density decreased (decrease of 0.5 HU ± 3.0) over 2 years. Atypical changes in EAT volume were independent predictors of all-cause mortality (atypical increase: hazard ratio [HR], 1.15 [95% CI: 1.06, 1.25] [P < .001]; atypical decrease: HR, 1.34 [95% CI: 1.23, 1.46] [P < .001]). An atypical decrease in EAT volume was associated with cardiovascular mortality (HR, 1.27 [95% CI: 1.06, 1.51]; P = .009). EAT density increase was associated with all-cause, cardiovascular, and lung cancer mortality (HR, 1.29 [95% CI: 1.18, 1.40] [P < .001]; HR, 1.29 [95% CI: 1.08, 1.54] [P = .005]; HR, 1.30 [95% CI: 1.07, 1.57] [P = .007], respectively). Conclusion EAT volume increase and decrease and EAT density increase beyond typical on subsequent chest CT scans were associated with all-cause mortality in participants screened for lung cancer. EAT volume decrease and EAT density increase were associated with elevated risk of cardiovascular mortality after adjustment for baseline EAT values. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Fuss in this issue.

PMID:39964263 | DOI:10.1148/radiol.240473

ACS Sens. 2025 Feb 18. doi: 10.1021/acssensors.4c03096. Online ahead of print.

ABSTRACT

To enhance the sensitivity, integration, and practicality of the Raman detection system, a deep learning-based dual-functional subregional microfluidic integrated hydrogel surface-enhanced Raman scattering (SERS) platform is proposed in this paper. First, silver nanoparticles (Ag NPs) with a homogeneous morphology were synthesized using a one-step reduction method. Second, these Ag NPs were embedded in N-isopropylacrylamide/poly(vinyl alcohol) (Ag NPs-NIPAM/PVA) hydrogels. Finally, a dual-functional SERS platform featuring four channels, each equipped with a switch and a detection region, was developed in conjunction with microfluidics. This platform effectively allows the flow of the test material to be directed to a specific detection region by sequential activation of the hydrogel switches with an external heating element. It then utilizes the corresponding heating element in the detection region to adjust the gaps between Ag NPs, enabling the measurement of the Raman enhancement performance in the designated SERS detection area. The dual-functional microfluidic-integrated hydrogel SERS platform enables subregional sampling and simultaneous detection of multiple molecules. The platform demonstrated excellent detection performance for Rhodamine 6G (R6G), achieving a detection limit as low as 10-10 mol/L and an enhancement factor of 107, with relative standard deviations of the main characteristic peaks below 10%. Additionally, the platform is capable of simultaneous subarea detection of four real molecules─thiram, pyrene, anthracene, and dibutyl phthalate─combined with fully connected neural network technology, which offers improved predictability, practicality, and applicability for their classification and identification.

PMID:39964084 | DOI:10.1021/acssensors.4c03096

Front Plant Sci. 2025 Feb 3;16:1473153. doi: 10.3389/fpls.2025.1473153. eCollection 2025.

ABSTRACT

Sowing uniformity is an important evaluation indicator of mechanical sowing quality. In order to achieve accurate evaluation of sowing uniformity in hybrid rice mechanical sowing, this study takes the seeds in a seedling tray of hybrid rice blanket-seedling nursing as the research object and proposes a method for evaluating sowing uniformity by combining image processing methods and the ODConv_C2f-ECA-WIoU-YOLOv8n (OEW-YOLOv8n) network. Firstly, image processing methods are used to segment seed image and obtain seed grids. Next, an improved model named OEW-YOLOv8n based on YOLOv8n is proposed to identify the number of seeds in a unit seed grid. The improved strategies include the following: (1) Replacing the Conv module in the Bottleneck of C2f modules with the Omni-Dimensional Dynamic Convolution (ODConv) module, where C2f modules are located at the connection between the Backbone and Neck. This improvement can enhance the feature extraction ability of the Backbone network, as the new modules can fully utilize the information of all dimensions of the convolutional kernel. (2) An Efficient Channel Attention (ECA) module is added to the Neck for improving the network's capability to extract deep semantic feature information of the detection target. (3) In the Bbox module of the prediction head, the Complete Intersection over Union (CIoU) loss function is replaced by the Weighted Intersection over Union version 3 (WIoUv3) loss function to improve the convergence speed of the bounding box loss function and reduce the convergence value of the loss function. The results show that the mean average precision (mAP) of the OEW-YOLOv8n network reaches 98.6%. Compared to the original model, the mAP improved by 2.5%. Compared to the advanced object detection algorithms such as Faster-RCNN, SSD, YOLOv4, YOLOv5s YOLOv7-tiny, and YOLOv10s, the mAP of the new network increased by 5.2%, 7.8%, 4.9%, 2.8% 2.9%, and 3.3%, respectively. Finally, the actual evaluation experiment showed that the test error is from -2.43% to 2.92%, indicating that the improved network demonstrates excellent estimation accuracy. The research results can provide support for the mechanized sowing quality detection of hybrid rice and the intelligent research of rice seeder.

PMID:39963535 | PMC:PMC11830705 | DOI:10.3389/fpls.2025.1473153

Front Plant Sci. 2025 Feb 3;16:1462694. doi: 10.3389/fpls.2025.1462694. eCollection 2025.

ABSTRACT

The increasing availability of genetic and genomic resources has underscored the need for automated microscopic phenotyping in plant-pathogen interactions to identify genes involved in disease resistance. Building on accumulated experience and leveraging automated microscopy and software, we developed BluVision Micro, a modular, machine learning-aided system designed for high-throughput microscopic phenotyping. This system is adaptable to various image data types and extendable with modules for additional phenotypes and pathogens. BluVision Micro was applied to screen 196 genetically diverse barley genotypes for interactions with powdery mildew fungi, delivering accurate, sensitive, and reproducible results. This enabled the identification of novel genetic loci and marker-trait associations in the barley genome. The system also facilitated high-throughput studies of labor-intensive phenotypes, such as precise colony area measurement. Additionally, BluVision's open-source software supports the development of specific modules for various microscopic phenotypes, including high-throughput transfection assays for disease resistance-related genes.

PMID:39963527 | PMC:PMC11832026 | DOI:10.3389/fpls.2025.1462694

Front Artif Intell. 2025 Feb 3;7:1449329. doi: 10.3389/frai.2024.1449329. eCollection 2024.

ABSTRACT

The accurate classification of potato leaf diseases plays a pivotal role in ensuring the health and productivity of crops. This study presents a unified approach for addressing this challenge by leveraging the power of Explainable AI (XAI) and transfer learning within a deep Learning framework. In this research, we propose a transfer learning-based deep learning model that is tailored for potato leaf disease classification. Transfer learning enables the model to benefit from pre-trained neural network architectures and weights, enhancing its ability to learn meaningful representations from limited labeled data. Additionally, Explainable AI techniques are integrated into the model to provide interpretable insights into its decision-making process, contributing to its transparency and usability. We used a publicly available potato leaf disease dataset to train the model. The results obtained are 97% for validation accuracy and 98% for testing accuracy. This study applies gradient-weighted class activation mapping (Grad-CAM) to enhance model interpretability. This interpretability is vital for improving predictive performance, fostering trust, and ensuring seamless integration into agricultural practices.

PMID:39963448 | PMC:PMC11830750 | DOI:10.3389/frai.2024.1449329

Front Ophthalmol (Lausanne). 2025 Feb 3;4:1497848. doi: 10.3389/fopht.2024.1497848. eCollection 2024.

ABSTRACT

INTRODUCTION: Glaucoma, optic neuritis (ON), and non-arteritic anterior ischemic optic neuropathy (NAION) produce distinct patterns of retinal ganglion cell (RGC) damage. We propose a booster Variational Autoencoder (bVAE) to capture spatial variations in RGC loss and generate latent space (LS) montage maps that visualize different degrees and spatial patterns of optic nerve bundle injury. Furthermore, the bVAE model is capable of tracking the spatial pattern of RGC thinning over time and classifying the underlying cause.

METHODS: The bVAE model consists of an encoder, a display decoder, and a booster decoder. The encoder decomposes input ganglion cell layer (GCL) thickness maps into two display latent variables (dLVs) and eight booster latent variables (bLVs). The dLVs capture primary spatial patterns of RGC thinning, while the display decoder reconstructs the GCL map and creates the LS montage map. The bLVs add finer spatial details, improving reconstruction accuracy. XGBoost was used to analyze the dLVs and bLVs, estimating normal/abnormal GCL thinning and classifying diseases (glaucoma, ON, and NAION). A total of 10,701 OCT macular scans from 822 subjects were included in this study.

RESULTS: Incorporating bLVs improved reconstruction accuracy, with the image-based root-mean-square error (RMSE) between input and reconstructed GCL thickness maps decreasing from 5.55 ± 2.29 µm (two dLVs only) to 4.02 ± 1.61 µm (two dLVs and eight bLVs). However, the image-based structural similarity index (SSIM) remained similar (0.91 ± 0.04), indicating that just two dLVs effectively capture the main GCL spatial patterns. For classification, the XGBoost model achieved an AUC of 0.98 for identifying abnormal spatial patterns of GCL thinning over time using the dLVs. Disease classification yielded AUCs of 0.95 for glaucoma, 0.84 for ON, and 0.93 for NAION, with bLVs further increasing the AUCs to 0.96 for glaucoma, 0.93 for ON, and 0.99 for NAION.

CONCLUSION: This study presents a novel approach to visualizing and quantifying GCL thinning patterns in optic neuropathies using the bVAE model. The combination of dLVs and bLVs enhances the model's ability to capture key spatial features and predict disease progression. Future work will focus on integrating additional image modalities to further refine the model's diagnostic capabilities.

PMID:39963427 | PMC:PMC11830743 | DOI:10.3389/fopht.2024.1497848

J Multidiscip Healthc. 2025 Feb 12;18:787-799. doi: 10.2147/JMDH.S492163. eCollection 2025.

ABSTRACT

OBJECTIVE: To evaluate the effectiveness of deep learning technology based on generative adversarial networks (GANs) in reducing motion artifacts in cardiac magnetic resonance (CMR) cine sequences.

METHODS: The training and testing datasets consisted of 2000 and 200 pairs of clear and blurry images, respectively, acquired through simulated motion artifacts in CMR cine sequences. These datasets were used to establish and train a deep learning GAN model. To assess the efficacy of the deep learning network in mitigating motion artifacts, 100 images with simulated motion artifacts and 37 images with real-world motion artifacts encountered in clinical practice were selected. Image quality pre- and post-optimization was assessed using metrics including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Leningrad Focus Measure, and a 5-point Likert scale.

RESULTS: After GAN optimization, notable improvements were observed in the PSNR, SSIM, and focus measure metrics for the 100 images with simulated artifacts. These metrics increased from initial values of 23.85±2.85, 0.71±0.08, and 4.56±0.67, respectively, to 27.91±1.74, 0.83±0.05, and 7.74±0.39 post-optimization. Additionally, the subjective assessment scores significantly improved from 2.44±1.08 to 4.44±0.66 (P<0.001). For the 37 images with real-world artifacts, the Tenengrad Focus Measure showed a significant enhancement, rising from 6.06±0.91 to 10.13±0.48 after artifact removal. Subjective ratings also increased from 3.03±0.73 to 3.73±0.87 (P<0.001).

CONCLUSION: GAN-based deep learning technology effectively reduces motion artifacts present in CMR cine images, demonstrating significant potential for clinical application in optimizing CMR motion artifact management.

PMID:39963324 | PMC:PMC11830935 | DOI:10.2147/JMDH.S492163

Front Bioinform. 2025 Feb 3;5:1520382. doi: 10.3389/fbinf.2025.1520382. eCollection 2025.

ABSTRACT

Proteins, composed of amino acids, are crucial for a wide range of biological functions. Proteins have various interaction sites, one of which is the protein-ligand binding site, essential for molecular interactions and biochemical reactions. These sites enable proteins to bind with other molecules, facilitating key biological functions. Accurate prediction of these binding sites is pivotal in computational drug discovery, helping to identify therapeutic targets and facilitate treatment development. Machine learning has made significant contributions to this field by improving the prediction of protein-ligand interactions. This paper reviews studies that use machine learning to predict protein-ligand binding sites from sequence data, focusing on recent advancements. The review examines various embedding methods and machine learning architectures, addressing current challenges and the ongoing debates in the field. Additionally, research gaps in the existing literature are highlighted, and potential future directions for advancing the field are discussed. This study provides a thorough overview of sequence-based approaches for predicting protein-ligand binding sites, offering insights into the current state of research and future possibilities.

PMID:39963299 | PMC:PMC11830693 | DOI:10.3389/fbinf.2025.1520382

Front Neurosci. 2025 Feb 3;19:1461654. doi: 10.3389/fnins.2025.1461654. eCollection 2025.

ABSTRACT

INTRODUCTION: Music has a profound impact on human emotions, capable of eliciting a wide range of emotional responses, a phenomenon that has been effectively harnessed in the field of music therapy. Given the close relationship between music and language, researchers have begun to explore how music influences brain activity and cognitive processes by integrating artificial intelligence with advancements in neuroscience.

METHODS: In this study, a total of 120 subjects were recruited, all of whom were students aged between 19 and 26 years. Each subject is required to listen to six 1-minute music segments expressing different emotions and speak at the 40-second mark. In terms of constructing the classification model, this study compares the classification performance of deep neural networks with other machine learning algorithms.

RESULTS: The differences in EEG signals between different emotions during speech are more pronounced compared to those in a quiet state. In the classification of EEG signals for speaking and quiet states, using deep neural network algorithms can achieve accuracies of 95.84% and 96.55%, respectively.

DISCUSSION: Under the stimulation of music with different emotions, there are certain differences in EEG between speaking and resting states. In the construction of EEG classification models, the classification performance of deep neural network algorithms is superior to other machine learning algorithms.

PMID:39963261 | PMC:PMC11830716 | DOI:10.3389/fnins.2025.1461654

J Biomed Opt. 2025 Feb;30(2):026001. doi: 10.1117/1.JBO.30.2.026001. Epub 2025 Feb 17.

ABSTRACT

SIGNIFICANCE: Current methods for complex conjugate removal (CCR) in frequency-domain optical coherence tomography (FD-OCT) often require additional hardware components, which increase system complexity and cost. A software-based solution would provide a more efficient and cost-effective alternative.

AIM: We aim to develop a deep learning approach to effectively remove complex conjugate artifacts (CCAs) from OCT scans without the need for extra hardware components.

APPROACH: We introduce a deep learning method that employs generative adversarial networks to eliminate CCAs from OCT scans. Our model leverages both conventional intensity images and phase images from the OCT scans to enhance the artifact removal process.

RESULTS: Our CCR-generative adversarial network models successfully converted conventional OCT scans with CCAs into artifact-free scans across various samples, including phantoms, human skin, and mouse eyes imaged in vivo with a phase-stable swept source-OCT prototype. The inclusion of phase images significantly improved the performance of the deep learning models in removing CCAs.

CONCLUSIONS: Our method provides a low-cost, data-driven, and software-based solution to enhance FD-OCT imaging capabilities by the removal of CCAs.

PMID:39963188 | PMC:PMC11831228 | DOI:10.1117/1.JBO.30.2.026001

Nanoscale. 2025 Feb 18. doi: 10.1039/d4nr05458j. Online ahead of print.

ABSTRACT

In addition to its excellent photoelectronic properties, the CsPbBr3 perovskite has been reported as a low thermal conductivity (k) material. However, few studies investigated the microscopic mechanisms underlying its low k. Studying its thermal transport behavior is crucial for understanding its thermal properties and thus improving its thermal stability. Here, we train a DFT-level deep-learning potential (DP) of CsPbBr3 and explore its ultra-low k using nonequilibrium molecular dynamics (NEMD). The k calculated using NEMD is 0.43 ± 0.01 W m-1 K-1, which is consistent with experimental results. Furthermore, the Cs vacancy contributes to the decrease in k due to the distortion of the Pb-Br cage, which enhances phonon scattering and reduces the phonon lifetime. Our research reveals the significant potential of machine learning force fields in thermal and phonon behavior research and the valuable insights gained from defect-regulated thermal conductivity.

PMID:39963065 | DOI:10.1039/d4nr05458j

J Pathol Transl Med. 2025 Feb 18. doi: 10.4132/jptm.2024.10.23. Online ahead of print.

ABSTRACT

BACKGROUND: High-grade serous ovarian carcinoma (HGSC) exhibits significant heterogeneity, posing challenges for effective clinical categorization. Understanding the histomorphological diversity within HGSC could lead to improved prognostic stratification and personalized treatment approaches.

METHODS: We applied the Histomic Atlases of Variation Of Cancers model to whole slide images from The Cancer Genome Atlas dataset for ovarian cancer. Histologically distinct tumor clones were grouped into common histomic clusters. Principal component analysis and K-means clustering classified HGSC samples into three groups: highly differentiated (HD), intermediately differentiated (ID), and lowly differentiated (LD).

RESULTS: HD tumors showed diverse patterns, lower densities, and stronger eosin staining. ID tumors had intermediate densities and balanced staining, while LD tumors were dense, patternless, and strongly hematoxylin-stained. RNA sequencing revealed distinct patterns in mitochondrial oxidative phosphorylation and energy metabolism, with upregulation in the HD, downregulation in the LD, and the ID positioned in between. Survival analysis showed significantly lower overall survival for the LD compared to the HD and ID, underscoring the critical role of mitochondrial dynamics and energy metabolism in HGSC progression.

CONCLUSIONS: Deep learning-based histologic analysis effectively stratifies HGSC into clinically relevant prognostic groups, highlighting the role of mitochondrial dynamics and energy metabolism in disease progression. This method offers a novel approach to HGSC categorization.

PMID:39962925 | DOI:10.4132/jptm.2024.10.23

Sci Rep. 2025 Feb 17;15(1):5693. doi: 10.1038/s41598-025-85802-9.

ABSTRACT

Recently, deep learning-based medical image classification models have made substantial advancements. However, many existing models prioritize performance at the cost of efficiency, limiting their practicality in clinical use. Traditional Convolutional Neural Network (CNN)-based methods, Transformer-based methods, and hybrid approaches combining these two struggle to balance performance and model complexity. To achieve efficient predictions with a low parameter count, we propose DenseAttentionNetwork (DANet), a lightweight model for Alzheimer's disease detection in 3D MRI images. DANet leverages dense connections and a linear attention mechanism to enhance feature extraction and capture long-range dependencies. Its architecture integrates convolutional layers for localized feature extraction with linear attention for global context, enabling efficient multi-scale feature reuse across the network. By replacing traditional self-attention with a parameter-efficient linear attention mechanism, DANet overcomes some limitations of standard self-attention. Extensive experiments across multi-institutional datasets demonstrate that DANet achieves the best performance in area under the receiver operating characteristic curve (AUC), which underscores the model's robustness and effectiveness in capturing relevant features for Alzheimer's disease detection while also attaining a strong accuracy structure with fewer parameters. Visualizations based on activation maps further verify the model's ability to highlight AD-relevant regions in 3D MRI images, providing clinically interpretable insights into disease progression.

PMID:39962113 | DOI:10.1038/s41598-025-85802-9

Sci Rep. 2025 Feb 17;15(1):5815. doi: 10.1038/s41598-025-85703-x.

ABSTRACT

Alzheimer's Disease (AD) is a progressive condition of a neurological brain disorder recognized by symptoms such as dementia, memory loss, alterations in behaviour, and impaired reasoning abilities. Recently, many researchers have been working to develop an effective AD recognition system using deep learning (DL) based convolutional neural network (CNN) model aiming to deploy the automatic medical image diagnosis system. The existing system is still facing difficulties in achieving satisfactory performance in terms of accuracy and efficiency because of the lack of feature ineffectiveness. This study proposes a lightweight Stacked Convolutional Neural Network with a Channel Attention Network (SCCAN) for MRI based on AD classification to overcome the challenges. In the procedure, we sequentially integrate 5 CNN modules, which form a stack CNN aiming to generate a hierarchical understanding of features through multi-level extraction, effectively reducing noise and enhancing the weight's efficacy. This feature is then fed into a channel attention module to select the practical features based on the channel dimension, facilitating the selection of influential features. . Consequently, the model exhibits reduced parameters, making it suitable for training on smaller datasets. Addressing the class imbalance in the Kaggle MRI dataset, a balanced distribution of samples among classes is emphasized. Extensive experiments of the proposed model with the ADNI1 Complete 1Yr 1.5T, Kaggle, and OASIS-1 datasets showed 99.58%, 99.22%, and 99.70% accuracy, respectively. The proposed model's high performance surpassed state-of-the-art (SOTA) models and proved its excellence as a significant advancement in AD classification using MRI images.

PMID:39962097 | DOI:10.1038/s41598-025-85703-x

Insights Imaging. 2025 Feb 17;16(1):44. doi: 10.1186/s13244-025-01911-z.

ABSTRACT

OBJECTIVE: To assess the accuracy of deep learning reconstruction (DLR) technique on synthetic MRI (SyMRI) including T2 measurements and diagnostic performance of DLR synthetic MRI (SyMRIDL) in patients with knee osteoarthritis (KOA) using conventional MRI as standard reference.

MATERIALS AND METHODS: This prospective study recruited 36 volunteers and 70 patients with suspected KOA from May to October 2023. DLR and non-DLR synthetic T2 measurements (T2-SyMRIDL, T2-SyMRI) for phantom and in vivo knee cartilage were compared with multi-echo fast-spin-echo (MESE) sequence acquired standard T2 values (T2MESE). The inter-reader agreement on qualitative evaluation of SyMRIDL and the positive percent agreement (PPA) and negative percentage agreement (NPA) were analyzed using routine images as standard diagnosis.

RESULTS: DLR significantly narrowed the quantitative differences between T2-SyMRIDL and T2MESE for 0.8 ms with 95% LOA [-5.5, 7.1]. The subjective assessment between DLR synthetic MR images and conventional MRI was comparable (all p > 0.05); Inter-reader agreement for SyMRIDL and conventional MRI was substantial to almost perfect with values between 0.62 and 0.88. SyMRIDL MOAKS had substantial inter-reader agreement and high PPA/NPA values (95%/99%) using conventional MRI as standard reference. Moreover, T2-SyMRIDL measurements instead of non-DLR ones significantly differentiated normal-appearing from injury-visible cartilages.

CONCLUSION: DLR synthetic knee MRI provided both weighted images for clinical diagnosis and accurate T2 measurements for more confidently identifying early cartilage degeneration from normal-appearing cartilages.

CRITICAL RELEVANCE STATEMENT: One-acquisition synthetic MRI based on deep learning reconstruction provided an accurate quantitative T2 map and morphologic images in relatively short scan time for more confidently identifying early cartilage degeneration from normal-appearing cartilages compared to the conventional morphologic knee sequences.

KEY POINTS: Deep learning reconstruction (DLR) synthetic knee cartilage T2 values showed no difference from conventional ones. DLR synthetic T1-, proton density-, STIR-weighted images had high positive percent agreement and negative percentage agreement using MRI OA Knee Score features. DLR synthetic T2 measurements could identify early cartilage degeneration from normal-appearing ones.

PMID:39961957 | DOI:10.1186/s13244-025-01911-z

Med Biol Eng Comput. 2025 Feb 17. doi: 10.1007/s11517-025-03311-3. Online ahead of print.

ABSTRACT

Aortic aneurysms pose a significant risk of rupture. Previous research has shown that areas exposed to low wall shear stress (WSS) are more prone to rupture. Therefore, precise WSS determination on the aneurysm is crucial for rupture risk assessment. Computational fluid dynamics (CFD) is a powerful approach for WSS calculations, but they are computationally intensive, hindering time-sensitive clinical decision-making. In this study, we propose a deep learning (DL) surrogate, MultiViewUNet, to rapidly predict time-averaged WSS (TAWSS) distributions on abdominal aortic aneurysms (AAA). Our novel approach employs a domain transformation technique to translate complex aortic geometries into representations compatible with state-of-the-art neural networks. MultiViewUNet was trained on 23 real and 230 synthetic AAA geometries, demonstrating an average normalized mean absolute error (NMAE) of just 0.362 % in WSS prediction. This framework has the potential to streamline hemodynamic analysis in AAA and other clinical scenarios where fast and accurate stress quantification is essential.

PMID:39961912 | DOI:10.1007/s11517-025-03311-3

Med Biol Eng Comput. 2025 Feb 17. doi: 10.1007/s11517-025-03318-w. Online ahead of print.

ABSTRACT

Caries segmentation holds significant clinical importance in medical image analysis, particularly in the early detection and treatment of dental caries. However, existing deep learning segmentation methods often struggle with accurately segmenting complex caries boundaries. To address this challenge, this paper proposes a novel network, named AEDD-Net, which combines an attention mechanism with a dual-decoder structure to enhance the performance of boundary segmentation for caries. Unlike traditional methods, AEDD-Net integrates atrous spatial pyramid pooling with cross-coordinate attention mechanisms to effectively fuse global and multi-scale features. Additionally, the network introduces a dedicated boundary generation module that precisely extracts caries boundary information. Moreover, we propose an innovative boundary loss function to further improve the learning of boundary features. Experimental results demonstrate that AEDD-Net significantly outperforms other comparison networks in terms of Dice coefficient, Jaccard similarity, precision, and sensitivity, particularly showing superior performance in boundary segmentation. This study provides an innovative approach for automated caries segmentation, with promising potential for clinical applications.

PMID:39961911 | DOI:10.1007/s11517-025-03318-w

Eur Radiol. 2025 Feb 17. doi: 10.1007/s00330-025-11450-2. Online ahead of print.

ABSTRACT

OBJECTIVES: This study proposes a multimodal deep learning (DL) approach to investigate the impact of tumors and visceral fat on occult peritoneal metastasis in colorectal cancer (CRC) patients.

METHODS: We developed a DL model named Multi-scale Feature Fusion Network (MSFF-Net) based on ResNet18, which extracted features of tumors and visceral fat from the longest diameter tumor section and the third lumbar vertebra level (L3) in preoperative CT scans of CRC patients. Logistic regression analysis was applied to patients' clinical data that integrated with DL features. A random forest (RF) classifier was established to evaluate the MSFF-Net's performance on internal and external test sets and compare it with radiologists' performance.

RESULTS: The model incorporating fat features outperformed the single tumor modality in the internal test set. Combining clinical information with DL provided the best diagnostic performance for predicting peritoneal metastasis in CRC patients. The AUCs were 0.941 (95% CI: [0.891, 0.986], p = 0.03) for the internal test set and 0.911 (95% CI: [0.857, 0.971], p = 0.013) for the external test set. CRC patients with peritoneal metastasis had a higher visceral adipose tissue index (VATI) compared to those without. Maximum tumor diameter and VATI were identified as independent prognostic factors for peritoneal metastasis. Grad-CAM decision regions corresponded with the independent prognostic factors identified by logistic regression analysis.

CONCLUSION: The study confirms the network features of tumors and visceral fat significantly enhance predictive performance for peritoneal metastasis in CRC. Visceral fat is a meaningful imaging biomarker for peritoneal metastasis's early detection in CRC patients.

KEY POINTS: Question Current research on predicting colorectal cancer with peritoneal metastasis mainly focuses on single-modality analysis, while studies based on multimodal imaging information are relatively scarce. Findings The Multi-scale Feature Fusion Network, constructed based on ResNet18, can utilize CT images of tumors and visceral fat to detect occult peritoneal metastasis in colorectal cancer. Clinical relevance This study identified independent prognostic factors for colorectal cancer peritoneal metastasis and combines them with tumor and visceral fat network features, aiding early diagnosis and accurate prognostic assessment.

PMID:39961863 | DOI:10.1007/s00330-025-11450-2

Sci Rep. 2025 Feb 17;15(1):5759. doi: 10.1038/s41598-025-90430-4.

ABSTRACT

The concept of a smart city has spread as a solution ensuring wider availability of data and services to citizens, apart from as a means to lower the environmental footprint of cities. Crowd density monitoring is a cutting-edge technology that enables smart cities to monitor and effectively manage crowd movements in real time. By utilizing advanced artificial intelligence and video analytics, valuable insights are accumulated from crowd behaviour, assisting cities in improving operational efficiency, improving public safety, and urban planning. This technology also significantly contributes to resource allocation and emergency response, contributing to smarter, safer urban environments. Crowd density classification in smart cities using deep learning (DL) employs cutting-edge NN models to interpret and analyze information from sensors such as IoT devices and CCTV cameras. This technique trains DL models on large datasets to accurately count people in a region, assisting traffic management, safety, and urban planning. By utilizing recurrent neural networks (RNNs) for time-series data and convolutional neural networks (CNNs) for image processing, the model adapts to varying crowd scenarios, lighting, and angles. This manuscript presents a Deep Convolutional Neural Network-based Crowd Density Monitoring for Intelligent Urban Planning (DCNNCDM-IUP) technique on smart cities. The proposed DCNNCDM-IUP technique utilizes DL methods to detect crowd densities, which can significantly assist in urban planning for smart cities. Initially, the DCNNCDM-IUP technique performs image preprocessing using Gaussian filtering (GF). The DCNNCDM-IUP technique utilizes the SE-DenseNet approach, which effectually learns complex feature patterns for feature extraction. Moreover, the hyperparameter selection of the SE-DenseNet approach is accomplished by using the red fox optimization (RFO) methodology. Finally, the convolutional long short-term memory (ConvLSTM) methodology recognizes varied crowd densities. A comprehensive simulation analysis is conducted to demonstrate the improved performance of the DCNNCDM-IUP technique. The experimental validation of the DCNNCDM-IUP technique portrayed a superior accuracy value of 98.40% compared to existing DL models.

PMID:39962323 | DOI:10.1038/s41598-025-90430-4