Ultramicroscopy. 2025 Apr 5;273:114137. doi: 10.1016/j.ultramic.2025.114137. Online ahead of print.

ABSTRACT

Precise alignment of the electron beam is critical for successful application of scanning transmission electron microscopes (STEM) to understanding materials at atomic level. Despite the success of aberration correctors, aberration correction is still a complex process. Here we approach aberration correction from the perspective of accelerator physics and show it is equivalent to minimizing the emittance growth of the beam, the span of the phase space distribution of the probe. We train a deep learning model to predict emittance growth from experimentally accessible Ronchigrams. Both simulation and experimental results show the model can capture the emittance variation with aberration coefficients accurately. We further demonstrate the model can act as a fast-executing function for the global optimization of the lens parameters. Our approach enables new ways to quickly quantify and automate aberration correction that takes advantage of the rapid measurements possible with high-speed electron cameras. In part II of the paper, we demonstrate how the emittance metric enables rapid online tuning of the aberration corrector using Bayesian optimization.

PMID:40222084 | DOI:10.1016/j.ultramic.2025.114137

Comput Biol Chem. 2025 Apr 5;118:108456. doi: 10.1016/j.compbiolchem.2025.108456. Online ahead of print.

ABSTRACT

The prevalent type of blood cancer is called leukemia, which is caused by the irregular production of immature malignant cells in the bone marrow. This dangerous condition weakens the immune system, making the body susceptible to infections, and can lead to death if not treated quickly. Thus, immediate treatments are necessary to detect leukemia at the initial stage to control abnormal cell growth. Leukemia detection from microscopic images of blood smears of malignant leukemia cells is a time-consuming and tedious task. Thus, a Tangent Sand Cat Swarm Optimization-Long Short-Term Memory-LeNet (TSCO-L-LeNet) with incremental learning is designed for the precise classification of acute lymphoblastic leukemia. The proposed model offers cheaper, faster and safer diagnosis service as the use of blood smear images reduces the diagnosis time and improves accuracy. Here, the input image is pre-processed using the adaptive median filter and the Scribble2label is used to segment the image. Later, the augmentation of segmented image is performed and the feature extraction process is employed to extract the necessary features from the augmented image. Finally, the L-LeNet with incremental learning is executed for acute lymphoblastic leukemia classification from the extracted features, where the TSCO approach is used to train the weights of L-LeNet. The experimental results show that TSCO-L-LeNet achieved maximum performance of 0.987 for accuracy, 0.977 for True Negative Rate (TNR), 0.967 for recall, 0.033 for False Negative rate, 0.023 for False Positive rate, and 0.979 for precision.

PMID:40222054 | DOI:10.1016/j.compbiolchem.2025.108456

Eur Radiol. 2025 Apr 13. doi: 10.1007/s00330-025-11574-5. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aims to assess diagnostic performance of high-resolution dynamic contrast-enhanced (DCE) MRI with deep learning-based compressed sensing and super-resolution (DLCS-SR) reconstruction for identifying microadenomas.

MATERIALS AND METHODS: This prospective study included 126 participants with suspected pituitary microadenomas who underwent DCE MRI between June 2023 and January 2024. Four image groups were derived from single-scan DCE MRI, which included 1.5-mm slice thickness images using DLCS-SR (1.5-mm DLCS-SR images), 1.5-mm slice thickness images with deep learning-based compressed sensing reconstruction (1.5-mm DLCS images), 1.5-mm routine images, and 3-mm slice thickness images using DLCS-SR (3-mm DLCS-SR images). Diagnostic criteria were established by incorporating laboratory findings, clinical symptoms, medical histories, previous imaging, and certain pathologic reports. Two readers assessed the diagnostic performance in identifying pituitary abnormalities and microadenomas. Diagnostic agreements were assessed using κ statistics, and intergroup comparisons for microadenoma detection were performed using the DeLong and McNemar tests.

RESULTS: The 1.5-mm DLCS-SR images (κ = 0.746-0.848) exhibited superior diagnostic agreement, outperforming 1.5-mm DLCS (κ = 0.585-0.687), 1.5-mm routine (κ = 0.449-0.487), and 3-mm DLCS-SR images (κ = 0.347-0.369) (p < 0.001 for all). Additionally, the performance of 1.5-mm DLCS-SR images in identifying microadenomas [area under the receiver operating characteristic curve (AUC), 0.89-0.94] surpassed that of 1.5-mm DLCS (AUC, 0.83-0.87; p = 0.042 and 0.011, respectively), 1.5-mm routine (AUC, 0.76-0.78; p < 0.001), and 3-mm DLCS-SR images (AUC, 0.72-0.74; p < 0.001).

CONCLUSION: The findings revealed superior diagnostic performance of 1.5-mm DLCS-SR images in identifying pituitary abnormalities and microadenomas, indicating the clinical-potential of high-resolution DCE MRI.

KEY POINTS: Question What strategies can overcome the resolution limitations of conventional dynamic contrast-enhanced (DCE) MRI, and which contribute to a high false-negative rate in diagnosing pituitary microadenomas? Findings Deep learning-based compressed sensing and super-resolution reconstruction applied to DCE MRI achieved high resolution while improving image quality and diagnostic efficacy. Clinical relevance DCE MRI with a 1.5-mm slice thickness and high in-plane resolution, utilizing deep learning-based compressed sensing and super-resolution reconstruction, significantly enhances diagnostic accuracy for pituitary abnormalities and microadenomas, enabling timely and effective patient management.

PMID:40221940 | DOI:10.1007/s00330-025-11574-5

Dentomaxillofac Radiol. 2025 Apr 12:twaf027. doi: 10.1093/dmfr/twaf027. Online ahead of print.

ABSTRACT

OBJECTIVES: This systematic review aimed to assess the performance of artificial intelligence (AI) in the imaging diagnosis of maxillary sinusitis (MS) compared to human analysis.

METHODS: Studies that presented radiographic images for the diagnosis of paranasal sinus diseases, as well as control groups for AI, were included. Articles that performed tests on animals, presented other conditions, surgical methods, didn't present data on the diagnosis of MS or on the outcomes of interest (area under the curve, sensitivity, specificity, and accuracy), compared the outcome only among different AIs, were excluded. Searches were conducted in five electronic databases and a gray literature. The risk of bias (RB) was assessed using the QUADAS-2 and the certainty of evidence by GRADE.

RESULTS: Six studies were included. The type of study considered was retrospective observational; with serious RB, and a considerable heterogeneity in methodologies. The IA presents similar results to humans, however, imprecision was assessed as serious for the outcomes and the certainty of evidence was classified as very low according to the GRADE approach. Furthermore, a dose-response effect was determined, as specialists demonstrate greater mastery of the diagnosis of MS when compared to resident professionals or general clinicians.

CONCLUSIONS: Considering the outcomes, the AI represents a complementary tool for diagnosing MS, especially considering professionals with less experience. Finally, performance analysis and definition of comparison parameters should be encouraged considering future research perspectives.

ADVANCES IN KNOWLEDGE: AI can be used as a complementary tool for diagnosing MS, however studies are still lacking methodological standardization.

PMID:40221848 | DOI:10.1093/dmfr/twaf027

Nat Commun. 2025 Apr 12;16(1):3496. doi: 10.1038/s41467-025-58437-7.

ABSTRACT

Preeclampsia is a major cause of maternal and perinatal mortality with no known cure. Delivery timing is critical to balancing maternal and fetal risks. We develop and externally validate PEDeliveryTime, a class of clinically informative models which resulted from deep-learning models, to predict the time from PE diagnosis to delivery using electronic health records. We build the models on 1533 PE cases from the University of Michigan and validate it on 2172 preeclampsia cases from the University of Florida. PEDeliveryTime full model contains only 12 features yet achieves high c-index of 0.79 and 0.74 on the Michigan and Florida data set respectively. For the early-onset preeclampsia subset, the full model reaches 0.76 and 0.67 on the Michigan and Florida test sets. Collectively, these models perform an early assessment of delivery urgency and might help to better prioritize medical resources.

PMID:40221413 | DOI:10.1038/s41467-025-58437-7

Nat Commun. 2025 Apr 12;16(1):3491. doi: 10.1038/s41467-025-58481-3.

ABSTRACT

The three-dimensional organization of chromatin is essential for gene regulation and cellular function, with epigenome playing a key role. Hi-C methods have expanded our understanding of chromatin interactions, but their high cost and complexity limit their use. Existing models for predicting chromatin interactions rely on limited ChIP-seq inputs, reducing their accuracy and generalizability. In this work, we present a computational approach, EpiVerse, which leverages imputed epigenetic signals and advanced deep learning techniques. EpiVerse significantly improves the accuracy of cross-cell-type Hi-C prediction, while also enhancing model interpretability by incorporating chromatin state prediction within a multitask learning framework. Moreover, EpiVerse predicts Hi-C contact maps across an array of 39 human tissues, which provides a comprehensive view of the complex relationship between chromatin structure and gene regulation. Furthermore, EpiVerse facilitates unprecedented in silico perturbation experiments at the "epigenome-level" to unveil the chromatin architecture under specific conditions. EpiVerse is available on GitHub: https://github.com/jhhung/EpiVerse .

PMID:40221401 | DOI:10.1038/s41467-025-58481-3

Acad Radiol. 2025 Apr 11:S1076-6332(25)00291-0. doi: 10.1016/j.acra.2025.03.048. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: To construct a deep learning model (DL) based on high-resolution T2-weighted images for preoperative differentiation between T2 and T3 stage rectal cancer (RC), and to compare its performance with experienced radiologists.

METHODS: This retrospective study included 281 patients with pathologically confirmed RC from four centers (January 2017-December 2022). A DenseNet model was developed using 255 patients from three centers (training:validation ratio=8:2) and externally tested on 26 patients from a fourth center. Two experienced radiologists independently assessed T staging. Diagnostic performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

RESULTS: The DL model outperformed radiologists in differentiating T2 and T3 stages across all datasets. In the training set, the DL model achieved an AUC of 0.810, compared to 0.578 and 0.625 for radiologists A and B, respectively. In the external test set, the DL model maintained superior diagnostic performance (AUC=0.715) compared to radiologist A (AUC=0.549) and radiologist B (AUC=0.493). The DL model demonstrated higher accuracy for T2 staging (0.625-0.787) and T3 staging (0.611-0.814) compared to radiologists (0.373-0.526 for T2; 0.611-0.783 for T3), who showed a tendency to over-stage T2 tumors. Inter-observer agreement between radiologists was moderate (kappa=0.451).

CONCLUSION: The DenseNet-based DL model demonstrated superior accuracy and diagnostic efficiency than radiologists in preoperative differentiation between T2 and T3 stages RC. This automated approach could potentially improve staging accuracy and support clinical decision-making in RC treatment planning.

PMID:40221285 | DOI:10.1016/j.acra.2025.03.048

Radiol Clin North Am. 2025 May;63(3):477-490. doi: 10.1016/j.rcl.2024.11.005. Epub 2025 Jan 4.

ABSTRACT

While artificial intelligence (AI) has shown considerable progress in many areas of medical imaging, applications in abdominal imaging, particularly for the gastrointestinal (GI) system, have notably lagged behind advancements in other body regions. This article reviews foundational concepts in AI and highlights examples of AI applications in GI tract imaging. The discussion on AI applications includes acute & emergent GI imaging, inflammatory bowel disease, oncology, and other miscellaneous applications. It concludes with a discussion of important considerations for implementing AI tools in clinical practice, and steps we can take to accelerate future developments in the field.

PMID:40221188 | DOI:10.1016/j.rcl.2024.11.005

Neuroimage. 2025 Apr 10:121190. doi: 10.1016/j.neuroimage.2025.121190. Online ahead of print.

ABSTRACT

Diffusion MRI (dMRI) offers unique insights into the microstructure of fetal brain tissue in utero. Longitudinal and cross-sectional studies of fetal dMRI have the potential to reveal subtle but crucial changes associated with normal and abnormal neurodevelopment. However, these studies depend on precise spatial alignment of data across scans and subjects, which is particularly challenging in fetal imaging due to the low data quality, rapid brain development, and limited anatomical landmarks for accurate registration. Existing registration methods, primarily developed for superior-quality adult data, are not well-suited for addressing these complexities. To bridge this gap, we introduce FetDTIAlign, a deep learning approach tailored to fetal brain dMRI, enabling accurate affine and deformable registration. FetDTIAlign integrates a novel dual-encoder architecture and iterative feature-based inference, effectively minimizing the impact of noise and low resolution to achieve accurate alignment. Additionally, it strategically employs different network configurations and domain-specific image features at each registration stage, addressing the unique challenges of affine and deformable registration, enhancing both robustness and accuracy. We validated FetDTIAlign on a dataset covering gestational ages between 23 and 36 weeks, encompassing 60 white matter tracts. For all age groups, FetDTIAlign consistently showed superior anatomical correspondence and the best visual alignment in both affine and deformable registration, outperforming two classical optimization-based methods and a deep learning-based pipeline. Further validation on external data from the Developing Human Connectome Project demonstrated the generalizability of our method to data collected with different acquisition protocols. Our results show the feasibility of using deep learning for fetal brain dMRI registration, providing a more accurate and reliable alternative to classical techniques. By enabling precise cross-subject and tract-specific analyses, FetDTIAlign paves the way for new discoveries in early brain development. The code is available at https://gitlab.com/blibli/fetdtialign.

PMID:40221066 | DOI:10.1016/j.neuroimage.2025.121190

BMC Genomics. 2025 Apr 12;26(1):367. doi: 10.1186/s12864-025-11534-9.

ABSTRACT

BACKGROUND: The recent AI breakthrough of AlphaFold2 has revolutionized 3D protein structural modeling, proving crucial for protein design and variant effects prediction. However, intrinsically disordered regions-known for their lack of well-defined structure and lower sequence conservation-often yield low-confidence models. The latest Variant Effect Predictor (VEP), AlphaMissense, leverages AlphaFold2 models, achieving over 90% sensitivity and specificity in predicting variant effects. However, the effectiveness of tools for variants in disordered regions, which account for 30% of the human proteome, remains unclear.

RESULTS: In this study, we found that predicting pathogenicity for variants in disordered regions is less accurate than in ordered regions, particularly for mutations at the first N-Methionine site. Investigations into the efficacy of variant effect predictors on intrinsically disordered regions (IDRs) indicated that mutations in IDRs are predicted with lower sensitivity and the gap between sensitivity and specificity is largest in disordered regions, especially for AlphaMissense and VARITY.

CONCLUSIONS: The prevalence of IDRs within the human proteome, coupled with the increasing repertoire of biological functions they are known to perform, necessitated an investigation into the efficacy of state-of-the-art VEPs on such regions. This analysis revealed their consistently reduced sensitivity and differing prediction performance profile to ordered regions, indicating that new IDR-specific features and paradigms are needed to accurately classify disease mutations within those regions.

PMID:40221640 | DOI:10.1186/s12864-025-11534-9

Sci Rep. 2025 Apr 12;15(1):12552. doi: 10.1038/s41598-025-97074-4.

ABSTRACT

Landslides are among the geological disasters that frequently occur worldwide and significantly restrict the sustainable development of society. Therefore, it is of great practical significance to perform landslide susceptibility assessment. In addressing issues such as limited training samples, inadequate utilization of spatially effective features, and high computational costs associated with existing methods, we propose a landslide susceptibility assessment method (DS-DRN), which uses a lightweight dense residual network with emphasis on deep spatial features. To minimize computational costs, we design a depthwise separable residual module that optimizes traditional convolution on residual branches into depthwise separable convolution. Additionally, to prevent vanishing gradient and improve the reuse rate of landslide feature information, dense connections are employed to construct a deep feature extraction module. Finally, the output of the model is fed into the Softmax classifier for landslide susceptibility prediction. Taking Ya'an City in Sichuan Province as the study area, we compare the proposed DS-DRN method with three widely used deep learning methods: CNN, CPCNN-RF, and U-net. Evaluating model accuracy and performance, the DS-DRN method exhibits the highest prediction accuracy while also saving computational costs. Therefore, the proposed model can better fit the complex nonlinear relationship in landslide susceptibility, effectively mine deep spatial features, and address the high computational costs associated with complex networks.

PMID:40221608 | DOI:10.1038/s41598-025-97074-4

Sci Rep. 2025 Apr 12;15(1):12661. doi: 10.1038/s41598-025-97331-6.

ABSTRACT

Early screening for cancer has proven to improve the survival rate and spare patients from intensive and costly treatments due to late diagnosis. Cancer screening in the healthy population involves an initial risk stratification step to determine the screening method and frequency, primarily to optimize resource allocation by targeting screening towards individuals who draw most benefit. For most screening programs, age and clinical risk factors such as family history are part of the initial risk stratification algorithm. In this paper, we focus on developing a blood marker-based risk stratification approach, which could be used to identify patients with elevated cancer risk to be encouraged for taking a diagnostic test or participate in a screening program. We demonstrate that the combination of simple, widely available blood tests, such as complete blood count and complete metabolic panel, could potentially be used to identify patients at risk for colorectal, liver, and lung cancers with areas under the ROC curve of 0.76, 0.85, 0.78, respectively. Furthermore, we hypothesize that such an approach could not only be used as pre-screening risk assessment for individuals but also as population health management tool, for example to better interrogate the cancer risk in certain sub-populations.

PMID:40221571 | DOI:10.1038/s41598-025-97331-6

Sci Rep. 2025 Apr 12;15(1):12576. doi: 10.1038/s41598-025-97946-9.

ABSTRACT

The Green View Index (GVI) is utilized to evaluate urban street value and ecosystem services and to gauge public perceptions of street greening. This study investigates the spatial heterogeneity of the GVI and its influencing factors in Yuzhong District, Chongqing, a mountainous city in China. Deep learning algorithms were employed to calculate the green visibility of street view images, and Geographic Weighted Regression (GWR) and the Optimal Parameter-Based Geodetector (OPGD) were utilized to analyze the relationships between GVI and factors such as road physical attributes, the Normalized Difference Vegetation Index (NDVI), and topographic features. The results indicate that: (1) In Yuzhong District, 58.9% of streets have a GVI within a low to moderate range, suggesting room for improvement. Higher GVI levels are generally associated with elevated Digital Elevation Models (DEM), while slope, aspect, and terrain undulation have relatively minor overall impacts on GVI. (2) The GVI is highest in the western regions and lowest in the eastern regions, with streets along the riversides exhibiting lower GVI levels. (3) GWR analysis reveals that road type and NDVI significantly influence the GVI. Higher DEM values promote increased GVI, whereas high road density suppresses it. (4) The interaction between influencing factors drives the differentiated distribution of GVI within the study area. The interaction effects between Road type, NDVI, and DEM are particularly notable among these.

PMID:40221555 | DOI:10.1038/s41598-025-97946-9

Ann Surg Oncol. 2025 Apr 12. doi: 10.1245/s10434-025-17228-6. Online ahead of print.

ABSTRACT

BACKGROUND: Bladder cancer is one of the top ten cancers globally, with its incidence steadily rising in China. Early detection and prognosis risk assessment play a crucial role in guiding subsequent treatment decisions for bladder cancer. However, traditional diagnostic methods such as bladder endoscopy, imaging, or pathology examinations heavily rely on the clinical expertise and experience of clinicians, exhibiting subjectivity and poor reproducibility.

MATERIALS AND METHODS: With the rise of artificial intelligence, novel approaches, particularly those employing deep learning technology, have shown significant advancements in clinical tasks related to bladder cancer, including tumor detection, molecular subtyping identification, tumor staging and grading, prognosis prediction, and recurrence assessment.

RESULTS: Artificial intelligence, with its robust data mining capabilities, enhances diagnostic efficiency and reproducibility when assisting clinicians in decision-making, thereby reducing the risks of misdiagnosis and underdiagnosis. This not only helps alleviate the current challenges of talent shortages and uneven distribution of medical resources but also fosters the development of precision medicine.

CONCLUSIONS: This study provides a comprehensive review of the latest research advances and prospects of artificial intelligence technology in the precise diagnosis and treatment of bladder cancer.

PMID:40221553 | DOI:10.1245/s10434-025-17228-6

Sci Rep. 2025 Apr 12;15(1):12659. doi: 10.1038/s41598-025-97159-0.

ABSTRACT

In this study, we evaluate the performance of four deep learning models, EfficientNetB0, ResNet50, DenseNet121, and InceptionV3, for the classification of citrus diseases from images. Extensive experiments were conducted on a dataset of 759 images distributed across 9 disease classes, including Black spot, Canker, Greening, Scab, Melanose, and healthy examples of fruits and leaves. Both InceptionV3 and DenseNet121 achieved a test accuracy of 99.12%, with a macro average F1-score of approximately 0.986 and a weighted average F1-score of 0.991, indicating exceptional performance in terms of precision and recall across the majority of the classes. ResNet50 and EfficientNetB0 attained test accuracies of 84.58% and 80.18%, respectively, reflecting moderate performance in comparison. These research results underscore the promise of modern convolutional neural networks for accurate and timely detection of citrus diseases, thereby providing effective tools for farmers and agricultural professionals to implement proactive disease management, reduce crop losses, and improve yield quality.

PMID:40221550 | DOI:10.1038/s41598-025-97159-0

Sci Rep. 2025 Apr 12;15(1):12548. doi: 10.1038/s41598-025-85815-4.

ABSTRACT

Analyzing biomedical images is vital in permitting the highest-performing imaging and numerous medical applications. Determining the analysis of the disease is an essential stage in handling the patients. Similarly, the statistical value of blood tests, the personal data of patients, and an expert estimation are necessary to diagnose a disease. With the growth of technology, patient-related information is attained rapidly and in big sizes. Currently, numerous physical methods exist to evaluate and forecast blood cancer utilizing the microscopic health information of white blood cell (WBC) images that are stable for prediction and cause many deaths. Machine learning (ML) and deep learning (DL) have aided the classification and collection of patterns in data, foremost in the growth of AI methods employed in numerous haematology fields. This study presents a novel Computer-Aided Diagnosis of Haematologic Disorders Detection Based on Spatial Feature Learning Networks with Hybrid Model (CADHDD-SFLNHM) approach using Blood Cell Images. The main aim of the CADHDD-SFLNHM approach is to enhance the detection and classification of haematologic disorders. At first, the Sobel filter (SF) technique is utilized for preprocessing to improve the quality of blood cell images. Additionally, the modified LeNet-5 model is used in the feature extractor process to capture the essential characteristics of blood cells relevant to disorder classification. The convolutional neural network and bi-directional gated recurrent unit with attention (CNN-BiGRU-A) method is employed to classify and detect haematologic disorders. Finally, the CADHDD-SFLNHM model implements the pelican optimization algorithm (POA) method to fine-tune the hyperparameters involved in the CNN-BiGRU-A method. The experimental result analysis of the CADHDD-SFLNHM model was accomplished using a benchmark database. The performance validation of the CADHDD-SFLNHM model portrayed a superior accuracy value of 97.91% over other techniques.

PMID:40221445 | DOI:10.1038/s41598-025-85815-4

Sci Rep. 2025 Apr 12;15(1):12647. doi: 10.1038/s41598-025-93549-6.

ABSTRACT

A distributed system comprises several independent units, each planned to track its tasks without interconnecting with the rest of them, excluding messaging services. This indicates that a solitary point of failure can reduce a method incapable without caution since no single point can achieve all essential processes. Malicious node recognition is a crucial feature of safeguarding the safety and reliability of distributed methods. Numerous models, ranging from anomaly recognition techniques to machine learning (ML) methods, are used to examine node behaviour and recognize deviances from usual patterns that may designate malicious intent. Advanced cryptographic protocols and intrusion detection devices are often combined to improve the flexibility of these methods against attacks. Moreover, real-time observing and adaptive plans are vital in quickly identifying and answering emerging attacks, contributing to the complete sturdiness of safe distributed methods. This study designs a Hybrid Bald Eagle-Crow Search Algorithm and Deep Learning for Enhanced Malicious Node Detection (HBECSA-DLMND) technique in Secure Distributed Systems. The HBECSA-DLMND technique follows the concept of metaheuristic feature selection with DL-based detection of malicious nodes in distributed systems. To accomplish this, the HBECSA-DLMND technique performs data normalization using the linear scaling normalization (LSN) approach, and the ADASYN approach is employed to handle class imbalance data. Besides, the HBECSA-DLMND method utilizes the HBECSA technique to choose a better subset of features. Meanwhile, the convolutional sparse autoencoder (CSAE) model detects malicious nodes. Finally, the dung beetle optimization (DBO) method is employed for the parameter range of the CSAE method. The experimental evaluation of the HBECSA-DLMND methodology is examined on a benchmark WSN-DS database. The performance validation of the HBECSA-DLMND methodology illustrated a superior accuracy value of 98.99% over existing approaches.

PMID:40221436 | DOI:10.1038/s41598-025-93549-6

Sci Rep. 2025 Apr 12;15(1):12631. doi: 10.1038/s41598-025-92429-3.

ABSTRACT

As one of the important indicators of soybean seed quality identification, the appearance of soybeans has always been of great concern to people, and in traditional detection, it is mainly through the naked eye to check whether there are defects on its surface. The field of machine learning, particularly deep learning technology, has undergone rapid advancements and development, making it possible to detect the defects of soybean seeds using deep learning technology. This method can effectively replace the traditional detection methods in the past and reduce the human resources consumption in this work, leading to decreased expenses associated with agricultural activities. In this paper, we propose a Yolov9-c-ghost-Forward model improved by introducing GhostConv, a lightweight convolutional module in GhostNet, which enhances the recognition of soybean seed images through grayscale conversion, filtering processing, image segmentation, morphological operations, etc. and greatly reduces the noise in them, to separate the soybean seeds from the original images. Based on the Yolov9 network, the soybean seed features are extracted, and the defects of soybean seeds are detected. Based on the experiments' findings, the recall rate can reach 98.6%, and the mAP0.5 can reach 99.2%. This shows that the model can provide a solid theoretical foundation and technical support for agricultural breeding screening and agricultural development.

PMID:40221419 | DOI:10.1038/s41598-025-92429-3

Sci Data. 2025 Apr 12;12(1):615. doi: 10.1038/s41597-025-04467-1.

ABSTRACT

Inter-modal image registration (IMIR) and image segmentation with abdominal Ultrasound (US) data have many important clinical applications, including image-guided surgery, automatic organ measurement, and robotic navigation. However, research is severely limited by the lack of public datasets. We propose TRUSTED (the Tridimensional Renal Ultra Sound TomodEnsitometrie Dataset), comprising paired transabdominal 3DUS and CT kidney images from 48 human patients (96 kidneys), including segmentation, and anatomical landmark annotations by two experienced radiographers. Inter-rater segmentation agreement was over 93% (Dice score), and gold-standard segmentations were generated using the STAPLE algorithm. Seven anatomical landmarks were annotated, for IMIR systems development and evaluation. To validate the dataset's utility, 4 competitive Deep-Learning models for kidney segmentation were benchmarked, yielding average DICE scores from 79.63% to 90.09% for CT, and 70.51% to 80.70% for US images. Four IMIR methods were benchmarked, and Coherent Point Drift performed best with an average Target Registration Error of 4.47 mm and Dice score of 84.10%. The TRUSTED dataset may be used freely to develop and validate segmentation and IMIR methods.

PMID:40221416 | DOI:10.1038/s41597-025-04467-1

Neurol Sci. 2025 Apr 12. doi: 10.1007/s10072-025-08167-x. Online ahead of print.

ABSTRACT

BACKGROUND: Alzheimer's disease is the most prevalent kind of age-associated dementia among older adults globally. Traditional diagnostic models for predicting Alzheimer's disease risks primarily rely on demographic and clinical data to develop policies and assess probabilities. However, recent advancements in machine learning (ML) and other artificial intelligence (AI) have shown promise in developing personalized risk models. These models use specific patient data from medical imaging and related reports. In this systematic review, different studies comprehensively examined the use of ML in magnetic resonance imaging (MRI), genetics, radiomics, and medical data for Alzheimer's disease risk assessment. I highlighted the results of our rigorous analysis of this research and emphasized the exciting potential of ML methods for Alzheimer's disease risk prediction. We also looked at current research projects and possible uses of AI-driven methods to enhance Alzheimer's disease risk prediction and enable more efficient investigating and individualized risk mitigation strategies.

AIM AND METHODS: This review integrates both conventional and AI-based models to thoroughly analyze neuroimaging and non-neuroimaging features used in Alzheimer's disease prediction. This study examined factors related to imaging, radiomics, genetics, and clinical aspects. In addition, this study comprehensively presented machine learning for predicting the risk of Alzheimer's disease detection to benefit both beginner and expert researchers.

RESULTS: A total of 700 publications from 2000 and 2024, were initially retrieved, out of which 120 studies met the inclusion criteria and were elected for review. The diagnosis of neurological disorders, along with the application of deep learning (DL) and machine learning (ML) were central themes in studies on the subject. When analyzing the medical implementation or design of innovative models, various machine learning models applied to neuroimaging and non-neuroimaging data may help researchers and clinicians become more informed. This review provides an extensive guide to the state of Alzheimer's disease risk assessment with artificial AI.

CONCLUSION: By integrating diverse neuroimaging and non-neuroimaging data sources, this study provides researchers with an alternative viewpoint on the application of AI in Alzheimer's disease risk prediction emphasizing its potential to improve early diagnosis and personalized intervention strategies.

PMID:40220257 | DOI:10.1007/s10072-025-08167-x