J Med Internet Res. 2025 Jul 17;27:e74402. doi: 10.2196/74402.

ABSTRACT

BACKGROUND: Traditional ocular gaze photograph preprocessing, relying on manual cropping and head tilt correction, is time-consuming and inconsistent, limiting artificial intelligence (AI) model development and clinical application.

OBJECTIVE: This study aimed to address these challenges using an advanced preprocessing algorithm to enhance the accuracy, efficiency, and standardization of eye region cropping for clinical workflows and AI data preprocessing.

METHODS: This retrospective and prospective cross-sectional study utilized 5832 images from 648 inpatients and outpatients, capturing 3 gaze positions under diverse conditions, including obstructions and varying distances. The preprocessing algorithm, based on a rotating bounding box detection framework, was trained and evaluated using precision, recall, and mean average precision (mAP) at various intersections over union thresholds. A 5-fold cross-validation was performed on an inpatient dataset, with additional testing on an independent outpatient dataset and an external cross-population dataset of 500 images from the IMDB-WIKI collection, representing diverse ethnicities and ages. Expert validation confirmed alignment with clinical standards across 96 images (48 images from a Chinese dataset of patients with strabismus and 48 images from IMDB-WIKI). Gradient-weighted class activation mapping heatmaps were used to assess model interpretability. A control experiment with 5 optometry specialists compared manual and automated cropping efficiency. Downstream task validation involved preprocessing 1000 primary gaze photographs using the Dlib toolkit, faster region-based convolutional neural network (R-CNN; both without head tilt correction), and our model (with correction), evaluating the impact of head tilt correction via the vision transformer strabismus screening network through 5-fold cross-validation.

RESULTS: The model achieved exceptional performance across datasets: on the 5-fold cross-validation set, it recorded a mean precision of 1.000 (95% CI 1.000-1.000), recall of 1.000 (95% CI 1.000-1.000), mAP50 of 0.995 (95% CI 0.995-0.995), and mAP95 of 0.893 (95% CI 0.870-0.918); on the internal independent test set, precision and recall were 1.000, with mAP50 of 0.995 and mAP95 of 0.801; and on the external cross-population test set, precision and recall were 1.000, with mAP50 of 0.937 and mAP95 of 0.792. The control experiment reduced image preparation time from 10 hours for manual cropping of 900 photos to 30 seconds with the automated model. Downstream strabismus screening task validation showed our model (with head tilt correction) achieving an area under the curve of 0.917 (95% CI 0.901-0.933), surpassing Dlib-toolkit and faster R-CNN (both without head tilt correction) with an area under the curve of 0.856 (P=.02) and 0.884 (P=.05), respectively. Heatmaps highlighted core ocular focus, aligning with head tilt directions.

CONCLUSIONS: This study delivers an AI-driven platform featuring a preprocessing algorithm that automates eye region cropping, correcting head tilt variations to improve image quality for AI development and clinical use. Integrated with electronic archives and patient-physician interaction, it enhances workflow efficiency, ensures telemedicine privacy, and supports ophthalmological research and strabismus care.

PMID:40674714 | DOI:10.2196/74402

Magn Reson Med. 2025 Jul 17. doi: 10.1002/mrm.30632. Online ahead of print.

ABSTRACT

PURPOSE: Propose and evaluate an automatic approach for predicting the optimal inversion time (TI) for dark and gray blood images for flow-independent dark-blood delayed-enhancement (FIDDLE) acquisition based on free-breathing FIDDLE TI-scout images.

METHODS: In 267 patients, the TI-scout sequence acquired single-shot magnetization-prepared and associated reference images (without preparation) on a 3 T Magnetom Vida and a 1.5 T Magnetom Sola scanner. Data were reconstructed into phase-corrected TI-scout images typically covering TIs from 140 to 440 ms (20 ms increment). A deep learning network was trained to segment the myocardium and blood pool in reference images. These segmentation masks were transferred to the TI-scout images to derive intensity features of myocardium and blood, with which T1-recovery curves were determined by logarithmic fitting. The optimal TI for dark and gray blood images were derived as linear functions of the TI in which both T1-curves cross. This TI-prediction pipeline was evaluated in 64 clinical subjects.

RESULTS: The pipeline predicted optimal TIs with an average error less than 10 ms compared to manually annotated optimal TIs.

CONCLUSION: The presented approach reliably and automatically predicted optimal TI for dark and gray blood FIDDLE acquisition, with an average error less than the TI increment of the FIDDLE TI-scout sequence.

PMID:40674608 | DOI:10.1002/mrm.30632

PLoS One. 2025 Jul 17;20(7):e0327586. doi: 10.1371/journal.pone.0327586. eCollection 2025.

ABSTRACT

Copy move forgery is a type of image forgery in which a portion of the original image is copied and pasted in a new location on the same image. The consistent illumination and noise pattern make this kind of forgery more difficult to detect. In copy-move forgery detection, conventional approaches are generally effective at identifying simple multiple copy-move forgeries. However, the conventional approaches and deep learning approaches often fall short in detecting multiple forgeries when transformations are applied to the copied regions. Motivated from these findings, a transform domain method for generating and analyzing multiple copy-move forgeries is proposed in this paper. This method utilizes the discrete wavelet transform (DWT) to decompose the original and patch image into approximate (low frequency) and detail coefficients (high frequency). The patch image approximate and details coefficients are inserted into the corresponding positions of the original image wavelet coefficients. The inverse DWT (IDWT) reconstructs the processed image planes after modification which simulates the multiple copy move forgery. In addition, this approach is tested by resizing the region of interest with varying patch sizes resulting in an interesting set of outcomes when evaluated against existing state-of-the-art techniques. This evaluation allows us to identify gaps in existing approaches and suggest improvements for creating more robust detection techniques for multiple copy-move forgeries.

PMID:40674455 | DOI:10.1371/journal.pone.0327586

PLoS One. 2025 Jul 17;20(7):e0324957. doi: 10.1371/journal.pone.0324957. eCollection 2025.

ABSTRACT

Pneumonia, a severe lung infection caused by various viruses, presents significant challenges in diagnosis and treatment due to its similarities with other respiratory conditions. Additionally, the need to protect patient privacy complicates the sharing of sensitive clinical data. This study introduces FLPneXAINet, an effective framework that combines federated learning (FL) with deep learning (DL) and explainable AI (XAI) to securely and accurately predict pneumonia using chest X-ray (CXR) images. We utilized a benchmark dataset from Kaggle, comprising 8,402 CXR images (3,904 normal and 4,498 pneumonia). The dataset was preprocessed and augmented using a cycle-consistent generative adversarial (CycleGAN) network to increase the volume of training data. Three pre-trained DL models named VGG16, NASNetMobile, and MobileNet were employed to extract features from the augmented dataset. Further, four ensemble DL (EDL) models were used to enhance feature extraction. Feature optimization was performed using recursive feature elimination (RFE), analysis of variance (ANOVA), and random forest (RF) to select the most relevant features. These optimized features were then inputted into machine learning (ML) models, including K-nearest neighbor (KNN), naive bayes (NB), support vector machine (SVM), and RF, for pneumonia prediction. The performance of the models was evaluated in a FL environment, with the EDL network achieving the best results: accuracy 97.61%, F1 score 98.36%, recall 98.13%, and precision 98.59%. The framework's predictions were further validated using two XAI techniques-Local Interpretable Model-Agnostic Explanations (LIME) and Grad-CAM. FLPneXAINet offers a robust solution for healthcare professionals to accurately diagnose pneumonia, ensuring timely treatment while safeguarding patient privacy.

PMID:40674439 | DOI:10.1371/journal.pone.0324957

Cancer Rep (Hoboken). 2025 Jul;8(7):e70240. doi: 10.1002/cnr2.70240.

ABSTRACT

BACKGROUND AND AIMS: Lung cancer remains a leading cause of cancer-related deaths worldwide, with early detection critical for improving prognosis. Traditional machine learning (ML) models have shown limited generalizability in clinical settings. This study proposes a deep learning-based approach using transfer learning to accurately segment lung tumor regions from CT scans and classify images as cancerous or noncancerous, aiming to overcome the limitations of conventional ML models.

METHODS: We developed a two-stage model utilizing a ResNet50 backbone within a U-Net architecture for lesion segmentation, followed by a multi-layer perceptron (MLP) for binary classification. The model was trained on publicly available CT scan datasets and evaluated on an independent clinical dataset from Hazrat Rasool Hospital, Iran. Training employed binary cross-entropy and Dice loss functions. Data augmentation, dropout, and regularization were used to enhance model generalizability and prevent overfitting.

RESULTS: The model achieved 94% accuracy on the real-world clinical test set. Evaluation metrics, including F1 score, Matthews correlation coefficient (MCC), Cohen's kappa, and Dice index, confirmed the model's robustness and diagnostic reliability. In comparison, traditional ML models performed poorly on external test data despite high training accuracy, highlighting a significant generalization gap.

CONCLUSION: This research presents a reliable deep learning framework for lung cancer detection that outperforms traditional ML approaches on external validation. The results demonstrate its potential for clinical deployment. Future work will focus on prospective validation, interpretability techniques, and integration into hospital workflows to support real-time decision making and regulatory compliance.

PMID:40674395 | DOI:10.1002/cnr2.70240

IEEE Trans Biomed Eng. 2025 Jul 17;PP. doi: 10.1109/TBME.2025.3590258. Online ahead of print.

ABSTRACT

OBJECTIVE: To develop a robust group-level brain parcellation method using deep learning based on resting-state functional magnetic resonance imaging (rs-fMRI), aiming to release the model assumptions made by previous approaches.

METHODS: We proposed Brain Deep Embedded Clustering (BDEC), a deep clustering model that employs a loss function designed to maximize inter-class separation and enhance intra-class similarity, thereby promoting the formation of functionally coherent brain regions.

RESULTS: Compared to ten widely used brain parcellation methods, the BDEC model demonstrates significantly improved performance in various functional homogeneity metrics. It also showed favorable results in parcellation validity, downstream tasks, task inhomogeneity, and generalization capability.

CONCLUSION: The BDEC model effectively captures intrinsic functional properties of the brain, supporting reliable and generalizable parcellation outcomes.

SIGNIFICANCE: BDEC provides a useful parcellation for brain network analysis and dimensionality reduction of rs-fMRI data, while also contributing to a deeper understanding of the brain's functional organization.

PMID:40674200 | DOI:10.1109/TBME.2025.3590258

IEEE J Biomed Health Inform. 2025 Jul 17;PP. doi: 10.1109/JBHI.2025.3590463. Online ahead of print.

ABSTRACT

Existing electroencephalogram (EEG)-based deep learning models are mainly designed for single or several specific tasks in childhood epilepsy analysis, which limits the perceptual capabilities and generalisability of the model. Recently, Foundation Models (FMs) achieved significant success in medical analysis, motivating us to explore the capability of FMs in childhood epilepsy analysis. The objective is to construct a FM with strong generalization capability on multi-tasking childhood epilepsy analysis. To this end, we propose a knowledge-guided foundation model for childhood epilepsy analysis (M4CEA) in this paper. The main contributions of the M4CEA are using the knowledge-guided mask strategy and the temporal embedding of the temporal encoder, which allow the model to effectively capture multi-domain representations of childhood EEG signals. Through pre-training on an EEG dataset with more than 1,000 hours childhood EEG recording, and performance fine-tuning, the developed M4CEA model can achieve promising performance on 8 downstream tasks in childhood epilepsy analysis, including artifact detection, onset detection, seizure type classification, childhood epilepsy syndrome classification, hypoxic-ischaemic encephalopathy (HIE) grading, sleep stage classification, epileptiform activity detection and spike-wave index (SWI) quantification. Taking HUH (Helsinki University Hospital) seizure detection task as an example, our model shows 9.42% improvement over LaBraM (a state-of-the-art Large Brain foundation Model for EEG analysis) in Balanced Accuracy. The source code and pre-trained weight are available at: https://github.com/Evigouse/M4CEA Project.

PMID:40674185 | DOI:10.1109/JBHI.2025.3590463

Am J Orthod Dentofacial Orthop. 2025 Jul 16:S0889-5406(25)00218-5. doi: 10.1016/j.ajodo.2025.05.008. Online ahead of print.

ABSTRACT

INTRODUCTION: The panoramic radiograph is the most commonly used imaging modality for predicting maxillary canine impaction. Several prediction models have been constructed based on panoramic radiographs. This study aimed to compare the prediction accuracy of existing models in an external validation facilitated by an automatic landmark detection system based on deep learning.

METHODS: Patients aged 7-14 years who underwent panoramic radiographic examinations and received a diagnosis of impacted canines were included in the study. An automatic landmark localization system was employed to assist the measurement of geometric parameters on the panoramic radiographs, followed by the calculated prediction of the canine impaction. Three prediction models constructed by Arnautska, Alqerban et al, and Margot et al were evaluated. The metrics of accuracy, sensitivity, specificity, precision, and area under the receiver operating characteristic curve (AUC) were used to compare the performance of different models.

RESULTS: A total of 102 panoramic radiographs with 102 impacted canines and 102 nonimpacted canines were analyzed in this study. The prediction outcomes indicated that the model by Margot et al achieved the highest performance, with a sensitivity of 95% and a specificity of 86% (AUC, 0.97), followed by the model by Arnautska, with a sensitivity of 93% and a specificity of 71% (AUC, 0.94). The model by Alqerban et al showed poor performance with an AUC of only 0.20.

CONCLUSIONS: Two of the existing predictive models exhibited good diagnostic accuracy, whereas the third model demonstrated suboptimal performance. Nonetheless, even the most effective model is constrained by several limitations, such as logical and computational challenges, which necessitate further refinement.

PMID:40673857 | DOI:10.1016/j.ajodo.2025.05.008

Cancer Med. 2025 Jul;14(14):e71054. doi: 10.1002/cam4.71054.

ABSTRACT

BACKGROUND: Tumor-infiltrating lymphocytes (TILs) have been proven to be important prognostic factors for various tumors. However, their prognostic significance within the context of esophageal squamous cell carcinoma (ESCC) remains inadequately explored. This study aims to assess the prognostic potential of TILs in ESCC using deep learning (DL) methods.

MATERIALS AND METHODS: We retrospectively enrolled 626 pathologically confirmed ESCC patients from two research centers. Their digital whole-slide imaging (WSI) and corresponding clinical information were collected. Subsequently, the DL method was employed to identify the tumor margin and TILs within the WSI. Tissue was divided into intratumor, peritumoral, and stromal regions based on their distance from the tumor margin. TILs were counted in each region. Optimal cut-off values of TILs were determined using the X-tile software. To mitigate selection bias and intergroup heterogeneity, a propensity score matching (PSM) analysis was employed. Survival analysis was performed using Kaplan-Meier curves and the log-rank test. The Cox proportional hazards regression model was used to identify independent prognostic factors.

RESULTS: We classified patients based on the cell counts and cut-off values of intratumor-infiltrating lymphocytes (I-TILs) and peritumoral infiltrating lymphocytes (P-TILs). Patients with high I-TILs and P-TILs were defined as those whose counts of both I-TILs and P-TILs exceeded the determined cutoff value. Patients with high I-TILs and P-TILs showed significantly better overall survival (OS, p = 0.0092) and recurrence-free survival (RFS, p = 0.0088) than patients with low I-TILs and P-TILs after PSM. Multivariable Cox proportional hazards regression further supported this conclusion and recognized I-TILs and P-TILs as independent prognostic factors (p = 0.0136, hazard ratio = 0.63 for OS; p = 0.0098, hazard ratio = 0.63 for RFS).

CONCLUSION: In the present study, we identified the quantitative distribution of TILs in ESCC patients with the help of the DL method. We established that I-TILs and P-TILs serve as independent prognostic factors for these patients. Further studies should focus on the lymphocyte subgroups and make better use of the spatial information to improve the predictive efficacy of TILs.

PMID:40673386 | DOI:10.1002/cam4.71054

Nan Fang Yi Ke Da Xue Xue Bao. 2025 Jul 20;45(7):1563-1570. doi: 10.12122/j.issn.1673-4254.2025.07.24.

ABSTRACT

OBJECTIVES: To construct an intelligent analysis model for classifying fetal orientation during intrapartum ultrasound videos based on multi-feature fusion.

METHODS: The proposed model consists of the Input, Backbone Network and Classification Head modules. The Input module carries out data augmentation to improve the sample quality and generalization ability of the model. The Backbone Network was responsible for feature extraction based on Yolov8 combined with CBAM, ECA, PSA attention mechanism and AIFI feature interaction module. The Classification Head consists of a convolutional layer and a softmax function to output the final probability value of each class. The images of the key structures (the eyes, face, head, thalamus, and spine) were annotated with frames by physicians for model training to improve the classification accuracy of the anterior occipital, posterior occipital, and transverse occipital orientations.

RESULTS: The experimental results showed that the proposed model had excellent performance in the tire orientation classification task with the classification accuracy reaching 0.984, an area under the PR curve (average accuracy) of 0.993, and area under the ROC curve of 0.984, and a kappa consistency test score of 0.974. The prediction results by the deep learning model were highly consistent with the actual classification results.

CONCLUSIONS: The multi-feature fusion model proposed in this study can efficiently and accurately classify fetal orientation in intrapartum ultrasound videos.

PMID:40673320 | DOI:10.12122/j.issn.1673-4254.2025.07.24

Hortic Res. 2025 May 7;12(8):uhaf128. doi: 10.1093/hr/uhaf128. eCollection 2025 Aug.

ABSTRACT

Crop pests significantly reduce crop yield and threaten global food security. Conventional pest control relies heavily on insecticides, leading to pesticide resistance and ecological concerns. However, crops and their wild relatives exhibit varied levels of pest resistance, suggesting the potential for breeding pest-resistant varieties. This study integrates deep learning (DL)/machine learning (ML) algorithms, plant phenomics, quantitative genetics, and transcriptomics to conduct genomic selection (GS) of pest resistance in grapevine. Building deep convolutional neural networks (DCNNs), we accurately assess pest damage on grape leaves, achieving 95.3% classification accuracy (VGG16) and a 0.94 correlation in regression analysis (DCNN-PDS). The pest damage was phenotyped as binary and continuous traits, and genome resequencing data from 231 grapevine accessions were combined in a Genome-Wide Association Studies, which maps 69 quantitative trait locus (QTLs) and 139 candidate genes involved in pest resistance pathways, including jasmonic acid, salicylic acid, and ethylene. Combining this with transcriptome data, we pinpoint specific pest-resistant genes such as ACA12 and CRK3, which are crucial in herbivore responses. ML-based GS demonstrates a high accuracy (95.7%) and a strong correlation (0.90) in predicting pest resistance as binary and continuous traits in grapevine, respectively. In general, our study highlights the power of DL/ML in plant phenomics and GS, facilitating genomic breeding of pest-resistant grapevine.

PMID:40673235 | PMC:PMC12265469 | DOI:10.1093/hr/uhaf128

Data Brief. 2025 Jun 26;61:111780. doi: 10.1016/j.dib.2025.111780. eCollection 2025 Aug.

ABSTRACT

Machine learning and artificial intelligence have gained widespread popularity across various sectors in Bangladesh, with the notable exception of the agriculture industry. While wealthier nations have extensively adopted machine learning and deep learning techniques in agriculture, Bangladesh's agricultural sector has been slower to follow suit. A key factor in the success of any machine learning model is the availability of high-quality datasets. However, practitioners in Bangladesh's mango industry face challenges in leveraging these advanced computational methods due to the lack of standardized and publicly accessible datasets. A well-structured dataset is essential for developing accurate models and reducing misclassification in real-world applications. To address this gap, we have developed a standardized image dataset capturing different stages of mango growth. The dataset, collected between April and June at an orchard on the East West University campus in Bangladesh, consists of 2004 images, each annotated and categorized into four distinct growth stages: early-fruit, premature, mature, and ripe. Although the dataset was created using mangoes from Bangladesh, the growth stages documented are representative of mango development globally, making this dataset applicable to mango cultivation in other countries. The dataset is organized into four folders, each containing both images and corresponding annotation files. We anticipate that this dataset will serve as a valuable resource for researchers and practitioners working in the field of automated agriculture, facilitating the development of machine learning models for monitoring and analyzing mango growth stages.

PMID:40673194 | PMC:PMC12266470 | DOI:10.1016/j.dib.2025.111780

Data Brief. 2025 Jun 25;61:111811. doi: 10.1016/j.dib.2025.111811. eCollection 2025 Aug.

ABSTRACT

ArtInsight is an innovative dataset designed to detect deterioration in fine art, specifically easel paintings. The dataset includes high-resolution images captured at the University of Granada using a digital camera with a 105 mm lens, ISO 125, F5, and a shutter speed of 1/13, and processed for color calibration. Two types of images are featured: those showing stucco technique interventions and those with Lacune from the loss of the Painting Layer (LPL). The VGG Image Annotator was employed for manual damage labeling, with annotations exported in JSON format and labeled for stucco and LPL damages. The dataset comprises 14 images with 2909 distinct damage areas, split into training and validation datasets. Developed using Python 3.7 and fine-tuned on a pre-trained Mask-RCNN model, this dataset demonstrates high accuracy rates (98-100 %) in damage detection. ArtInsight aims to facilitate automated damage detection and foster future research in art conservation and restoration. The dataset is publicly available at 10.5281/zenodo.8429814.

PMID:40673191 | PMC:PMC12266542 | DOI:10.1016/j.dib.2025.111811

Data Brief. 2025 Jun 21;61:111815. doi: 10.1016/j.dib.2025.111815. eCollection 2025 Aug.

ABSTRACT

This dataset presents a curated collection of 1896 high-resolution image pairs extracted from four historically significant Vietnamese films set during the Vietnam War era. Each pair consists of an original color frame and its corresponding grayscale version, generated using the ITU-R BT.601 luminance formula. Designed to support research in historical image restoration and colorization, the dataset serves as a benchmark for evaluating AI-driven colorization techniques. Frames were systematically extracted at 3 s intervals from well-preserved archival footage, followed by manual selection to ensure visual diversity and contextual relevance. The dataset is organized into training, validation, and test sets, enabling researchers to train and assess deep learning models for restoring and colorizing historical imagery. In addition to addressing the challenges posed by aged film quality, temporal degradation, and complex visual content, this dataset contributes to digital heritage preservation by making grayscale historical visuals more accessible and engaging for modern audiences. Potential applications include the development of automated colorization systems, domain adaptation research, and AI-powered video restoration from static images.

PMID:40673188 | PMC:PMC12266529 | DOI:10.1016/j.dib.2025.111815

Transl Lung Cancer Res. 2025 Jun 30;14(6):1929-1944. doi: 10.21037/tlcr-24-1039. Epub 2025 Jun 26.

ABSTRACT

BACKGROUND: The development of growth prediction models for multiple pulmonary ground-glass nodules (GGNs) could help predict their growth patterns and facilitate more precise identification of nodules that require close monitoring or early intervention. Previous studies have demonstrated the indolent growth pattern of GGNs and developed growth prediction models; however, these investigations predominantly focused on solitary GGN. This study aimed to investigate the natural history of multiple pulmonary GGNs and develop and validate growth prediction models based on computed tomography (CT) features, radiomics, and deep learning (DL) as well as compare their predictive performances.

METHODS: Patients with two or more persistent GGNs who underwent CT scans between October 2010 and November 2023 and had at least 3 years of follow-up without radiotherapy, chemotherapy, or surgery were retrospectively reviewed. The growth of GGN is defined as an increase in mean diameter by at least 2 mm, an increase in volume by at least 30%, or the emergence or enlargement of a solid component by at least 2 mm. Based on the interval changes during follow-up, the enrolled patients and GGNs were categorized into growth and non-growth groups. The data were randomly divided into a training set and a validation set at a ratio of 7:3. Clinical model, Radiomics model, DL model, Clinical-Radiomics model, and Clinical-DL model were constructed. Model performance was assessed using the area under the receiver operating characteristic curve (AUC).

RESULTS: A total of 732 GGNs [mean diameter (interquartile range, IQR), 5.5 (4.5-6.5) mm] from 231 patients (mean age 54.1±9.9 years; 26.4% male, 73.6% female) were included. Of the 156 (156/231, 67.5%) patients with GGN growth, the fastest-growing GGN had a volume doubling time (VDT) and mass doubling time (MDT) of 2,285 (IQR, 1,369-3,545) and 2,438 (IQR, 1,361-4,140) days, respectively. Among the growing 272 (272/732, 37.2%) GGNs, the median VDT and MDT were 2,934 (IQR, 1,648-4,491) and 2,875 (IQR, 1,619-5,148) days, respectively. Lobulation (P=0.049), vacuole (P=0.009), initial volume (P=0.01), and mass (P=0.01) were risk factors of GGN growth. The sensitivity and specificity of the Clinical model 1, Clinical model 2, Radiomics, DL, Clinical-Radiomics, and Clinical-DL models were 77.2% and 80.0%, 77.2% and 79.3%, 75.9% and 77.8%, 59.5% and 75.6%, 82.3% and 86.7%, 78.5% and 80.7%, respectively. The AUC for Clinical model 1, Clinical model 2, Radiomics, DL, Clinical-Radiomics, and Clinical-DL models were 0.876, 0.869, 0.845, 0.735, 0.908, and 0.887, respectively.

CONCLUSIONS: Multiple pulmonary GGNs exhibit indolent biological behaviour. The Clinical-Radiomics model demonstrated superior accuracy in predicting the growth of multiple GGNs compared to Clinical, Radiomics, DL, Clinical-DL models.

PMID:40673084 | PMC:PMC12261256 | DOI:10.21037/tlcr-24-1039

Front Neurosci. 2025 Jul 2;19:1555657. doi: 10.3389/fnins.2025.1555657. eCollection 2025.

ABSTRACT

OBJECTIVE: Alzheimer's disease (AD) is mainly identified by cognitive function deterioration. Diagnosing AD at early stages poses significant challenges for both researchers and healthcare professionals due to the subtle nature of early brain changes. Currently, electroencephalography (EEG) is widely used in the study of neurodegenerative diseases. However, most existing research relies solely on functional connectivity methods to infer inter-regional brain connectivity, overlooking the importance of spatial connections. Moreover, many existing approaches fail to fully integrate multi-frequency EEG features, limiting the comprehensive understanding of dynamic brain activity across different frequency bands. This study aims to address these limitations by developing a novel graph-based deep learning model that fully utilizes both functional and structural information from multi-frequency EEG data.

METHODS: This paper introduces a Multi-Frequency EEG data-based Multi-Graph Convolutional Network (MF-MGCN) model for AD diagnosis. This method integrates both functional and structural connectivity to more thoroughly capture the relationships among brain regions. By extracting differential entropy (DE) features from five distinct frequency bands of EEG signals for each segment and using graph convolutional networks (GCNs) to aggregate these features, the model effectively distinguishes between AD and healthy controls (HC).

RESULTS: The outcomes show that the developed model outperforms existing methods, achieving 96.15% accuracy and 98.74% AUC in AD and HC classification.

CONCLUSION: These findings highlight the potential of the MF-MGCN model as a clinical tool for Alzheimer's disease diagnosis. This approach could help clinicians detect Alzheimer's at earlier stages, enabling timely intervention and personalized treatment plans.

PMID:40672873 | PMC:PMC12263931 | DOI:10.3389/fnins.2025.1555657

Front Med (Lausanne). 2025 Jul 2;12:1596726. doi: 10.3389/fmed.2025.1596726. eCollection 2025.

ABSTRACT

Retinal diseases are among the leading causes of blindness worldwide, requiring early detection for effective treatment. Manual interpretation of ophthalmic imaging, such as optical coherence tomography (OCT), is traditionally time-consuming, prone to inconsistencies, and requires specialized expertise in ophthalmology. This study introduces OculusNet, an efficient and explainable deep learning (DL) approach for detecting retinal diseases using OCT images. The proposed method is specifically tailored for complex medical image patterns in OCTs to identify retinal disorders, such as choroidal neovascularization (CNV), diabetic macular edema (DME), and age-related macular degeneration characterized by drusen. The model benefits from Saliency Map visualization, an Explainable AI (XAI) technique, to interpret and explain how it reaches conclusions when identifying retinal disorders. Furthermore, the proposed model is deployed on a web page, allowing users to upload retinal OCT images and receive instant detection results. This deployment demonstrates significant potential for integration into ophthalmic departments, enhancing diagnostic accuracy and efficiency. In addition, to ensure an equitable comparison, a transfer learning approach has been applied to four pre-trained models: VGG19, MobileNetV2, VGG16, and DenseNet-121. Extensive evaluation reveals that the proposed OculusNet model achieves a test accuracy of 95.48% and a validation accuracy of 98.59%, outperforming all other models in comparison. Moreover, to assess the proposed model's reliability and generalizability, the Matthews Correlation Coefficient and Cohen's Kappa Coefficient have been computed, validating that the model can be applied in practical clinical settings to unseen data.

PMID:40672824 | PMC:PMC12263695 | DOI:10.3389/fmed.2025.1596726

Plant Environ Interact. 2025 Jul 15;6(4):e70072. doi: 10.1002/pei3.70072. eCollection 2025 Aug.

ABSTRACT

Littoral soils along Ghana's coastal zones, hosting diverse halophytes with multiple potential applications, contain significant salt content due to seawater influence. This study identified and explored the nutritional, ecological, and medicinal significance of these halophytes, focusing on their salt tolerance and desalination abilities. Deep learning image recognition was employed to identify plant species, followed by a greenhouse experiment on five selected halophytes (Ipomoea aquatica, Lactuca taraxacifolia, Paspalum vaginatum, Sesuvium portulacastrum, and Talinum triangulare) to assess their response to varying salt concentrations (0, 25, and 50 dS/m) and soil types (sea sand and arable soil). High salt concentrations (50 dS/m) generally reduced plant growth rates and biomass accumulation while increasing soil electrical conductivity (EC), total dissolved solids (TDS), and pH. Arable soil improved halophyte Relative Growth Rate (RGR) and performance index (PI) by 5% and 52%, respectively, compared to sea sand. Sesuvium portulacastrum exhibited enhanced PI at elevated salinity and demonstrated superior salt ion accumulation in roots and leaves at 50 dS/m. Both P. vaginatum and S. portulacastrum maintained the highest shoot and root dry weights under increased salinity, whereas S. portulacastrum significantly reduced soil EC, pH, Na, and Cl ion contents compared to other species. Sesuvium portulacastrum reduced several soil salinity indicators significantly compared to other species, highlighting its potential for addressing soil and water salinity issues in affected environments. This study shows the potential of Ghana's halophytes in addressing soil salinity-related challenges.

PMID:40672803 | PMC:PMC12264084 | DOI:10.1002/pei3.70072

Front Plant Sci. 2025 Jul 2;16:1604382. doi: 10.3389/fpls.2025.1604382. eCollection 2025.

ABSTRACT

INTRODUCTION: The state monitoring of tobacco leaves during the curing process is crucial for process control and automation of tobacco agricultural production. While most of the existing research on tobacco leaves state recognition focused on the temporal state of the leaves, the morphological state was often neglected. Moreover, the previous research typically used a limited number of non-industrial images for training, creating a significant disparity with the images encountered in actual applications.

METHODS: To investigate the potential of deep learning algorithms in identifying the morphological states of tobacco leaves in real industrial scenarios, a comprehensive and large-scale dataset was developed in this study. This dataset focused on the states of tobacco leaves in actual bulk curing barn in multiple production areas in China, specifically recognizing the degrees of yellowing, browning, and drying. Then, an efficient deep learning method was proposed based on this dataset to enhance the predictive performance.

RESULTS: The prediction accuracy achieved for the yellowing degree, browning degree, and drying degree were 83.0%, 90.5%, and 75.6% respectively. The overall average accuracy, satisfied the requirements of practical application scenarios with a value of 83%.

DISCUSSION: Our proposed framework effectively enables morphological state recognition in industrial curing, supporting parameter optimization and enhanced tobacco quality.

PMID:40672565 | PMC:PMC12263583 | DOI:10.3389/fpls.2025.1604382

JCO Clin Cancer Inform. 2025 Jul;9:e2500051. doi: 10.1200/CCI-25-00051. Epub 2025 Jul 16.

ABSTRACT

PURPOSE: The present study aimed to investigate the role of body composition as an independent image-derived biomarker for clinical outcome prediction in a clinical trial cohort of patients with relapsed or refractory (rel/ref) diffuse large B-cell lymphoma (DLBCL) treated with loncastuximab tesirine.

MATERIALS AND METHODS: The imaging cohort consisted of positron emission tomography/computed tomography scans of 140 patients with rel/ref DLBCL treated with loncastuximab tesirine in the LOTIS-2 (ClinicalTrials.gov identifier: NCT03589469) trial. Body composition analysis was conducted using both manual and deep learning-based segmentation of three primary tissue compartments-skeletal muscle (SM), subcutaneous fat (SF), and visceral fat (VF)-at the L3 level from baseline CT scans. From these segmented compartments, body composition ratio indices, including SM*/VF*, SF*/VF*, and SM*/(VF*+SF*), were derived. Pearson's correlation analysis was used to examine the agreement between manual and automated segmentation. Logistic regression analyses were used to assess the association between the derived indices and treatment response. Cox regression analyses were used to determine the effect of body composition indices on time-to-event outcomes. Body composition indices were considered as continuous and binary variables defined by cut points. The Kaplan-Meier method was used to estimate progression-free survival (PFS) and overall survival (OS).

RESULTS: The manual and automated SM*/VF* indices, as dichotomized, were significant predictors in univariable and multivariable logistic models for failure to achieve complete metabolic response. The manual SM*/VF* index as dichotomized was significantly associated with PFS, but not OS, in univariable and multivariable Cox models.

CONCLUSION: The pretreatment SM*/VF* body composition index shows promise as a biomarker for patients with rel/ref DLBCL undergoing treatment with loncastuximab tesirine. The proposed deep learning-based approach for body composition analysis demonstrated comparable performance to the manual process, presenting a more cost-effective alternative to conventional methods.

PMID:40669032 | DOI:10.1200/CCI-25-00051