Neurology. 2025 Apr 8;104(7_Supplement_1):4963. doi: 10.1212/WNL.0000000000212039. Epub 2025 Apr 7.

ABSTRACT

OBJECTIVE: To assess the potential of quantitative retinal vascular features as biomarkers of CADASIL.

BACKGROUND: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most common inherited cerebral small vessel disease, yet there is currently no biomarker for early detection. Given the anatomical and embryological similarities, the retina has been regarded as a window to cerebral microcirculation. We hypothesized that patients with CADASIL have different quantitative fundoscopic retinal features than matched controls.

DESIGN/METHODS: We conducted a cross-sectional, matched case-control study involving 49 CADASIL cases and 49 age- and sex-matched controls using genetic data from the UK Biobank between 2006 and 2010. Retinal fundoscopic images obtained from the UK Biobank were analyzed using AutoMorph, a deep learning-based tool that measures retinal vascular features, including fractal dimension, tortuosity, and vessel width. Baseline characteristics including age, sex, hypertension, diabetes, smoking status were compared using chi-square or Mann-Whitney-U test appropriately. Wilcoxon signed-rank test or paired-t test was used appropriately to compare these retinal features between cases and controls.

RESULTS: Our analysis included 49 CADASIL cases (mean age of 52.5 ± 7.9, 49% females) and 49 controls (mean age of 52.8 ± 7.9, 49% females). Vascular risk factors including hypertension, diabetes and smoking status were similar between the two groups (p>0.05). No statistically significant differences were observed between CADASIL cases and controls in fractal dimension (p=0.665), average width (p=0.104) or tortuosity measures like distance tortuosity (p=0.423), squared curvature tortuosity (p=0.925) and tortuosity density (p=0.870).

CONCLUSIONS: Quantitative retinal vascular features analyzed in this study did not significantly differentiate CADASIL cases from controls. This could reflect the potential inclusion of CADASIL patients in the early or asymptomatic stages, where retinal changes may not yet be apparent. Furthermore, healthy volunteer bias in the UK Biobank might have influenced these findings. Disclaimer: Abstracts were not reviewed by Neurology® and do not reflect the views of Neurology® editors or staff. Disclosure: Mr. Vallamchetla has nothing to disclose. Dr. badr has nothing to disclose. Mr. Abdelkader has nothing to disclose. Mr. Shourav has nothing to disclose. Xin Li has received personal compensation for serving as an employee of Arizona State University. Yalin Wang has nothing to disclose. The institution of Dr. Meschia has received research support from NINDS. The institution of Dr. Meschia has received research support from NINDS. Dr. Dumitrascu has nothing to disclose. Dr. Lin has nothing to disclose.

PMID:40193854 | DOI:10.1212/WNL.0000000000212039

Proc Natl Acad Sci U S A. 2025 Apr 15;122(15):e2425048122. doi: 10.1073/pnas.2425048122. Epub 2025 Apr 7.

ABSTRACT

The study of bacterial metabolism holds immense significance for improving human health and advancing agricultural practices. The prospective applications of genomically encoded bacterial metabolism present a compelling opportunity, particularly in the light of the rapid expansion of genomic sequencing data. Current metabolic inference tools face challenges in scaling with large datasets, leading to increased computational demands, and often exhibit limited inter-relatability and interoperability. Here, we introduce the Integrated Biosynthetic Inference Suite (IBIS), which employs deep learning models and a knowledge graph to facilitate rapid, scalable bacterial metabolic inference. This system leverages a series of Transformer based models to generate high quality, meaningful embeddings for individual enzymes, biosynthetic domains, and metabolic pathways. These embedded representations enable rapid, large-scale comparisons of metabolic proteins and pathways, surpassing the capabilities of conventional methodologies. The examination of evolutionary and functionally conserved metabolites across diverse bacterial species is facilitated by integrating the predictive capabilities of IBIS into a graph database enriched with comprehensive metadata. The consideration of both primary and specialized metabolism, combined with an embedding logic for enzyme discovery, uniquely positions IBIS to identify potential novel metabolic pathways. With the expansion of genomic data necessitating transformative approaches to advance molecular metabolism research, IBIS delivers an AI-driven holistic investigation of bacterial metabolism.

PMID:40193601 | DOI:10.1073/pnas.2425048122

J Proteome Res. 2025 Apr 7. doi: 10.1021/acs.jproteome.4c00884. Online ahead of print.

ABSTRACT

Sodium transporters maintain cellular homeostasis by transporting ions, minerals, and nutrients across the membrane, and Na+/K+ ATPases facilitate the cotransport of solutes in neurons, muscle cells, and epithelial cells. Sodium transporters are important for many physiological processes, and their dysfunction leads to diseases such as hypertension, diabetes, neurological disorders, and cancer. The NA_mCNN computational method highlights the functional diversity and significance of sodium transporters in membrane proteins using protein language model embeddings (PLMs) and multiple-window scanning deep learning models. This work investigates PLMs that include Tape, ProtTrans, ESM-1b-1280, and ESM-2-128 to achieve more accuracy in sodium transporter classification. Five-fold cross-validation and independent testing demonstrate ProtTrans embedding robustness. In cross-validation, ProtTrans achieved an AUC of 0.9939, a sensitivity of 0.9829, and a specificity of 0.9889, demonstrating its ability to distinguish positive and negative samples. In independent testing, ProtTrans maintained a sensitivity of 0.9765, a specificity of 0.9991, and an AUC of 0.9975, which indicates its high level of discrimination. This study advances the understanding of sodium transporter diversity and function, as well as their role in human pathophysiology. Our goal is to use deep learning techniques and protein language models for identifying sodium transporters to accelerate identification and develop new therapeutic interventions.

PMID:40193588 | DOI:10.1021/acs.jproteome.4c00884

JNCI Cancer Spectr. 2025 Apr 7:pkaf037. doi: 10.1093/jncics/pkaf037. Online ahead of print.

ABSTRACT

BACKGROUND: Benign breast disease (BBD) is an important risk factor for breast cancer (BC) development. In this study, we analyzed hematoxylin and eosin-stained whole slide images (WSIs) from diagnostic BBD biopsies using different deep learning (DL) approaches to predict those who subsequently developed breast cancer (cases) and those who did not (controls).

METHODS: We randomly divided cases and controls from a nested case-control study of 946 women with BBD into training (331 cases, 331 controls) and test (142 cases, 142 controls) sets. We employed customized VGG-16 and AutoML models for image-only classification using WSIs; logistic regression for classification using only clinico-pathological characteristics; and a multimodal network combining WSIs and clinico-pathological characteristics for classification.

RESULTS: Both image-only (area under the receiver operating characteristic curve, AUROCs of 0.83 (standard error, SE: 0.001) and 0.78 (SE: 0.001) for customized VGG-16 and AutoML, respectively)) and multimodal (AUROC of 0.89 (SE: 0.03)) networks had high discriminatory accuracy for BC. The clinico-pathological characteristics only model had the lowest AUROC of 0.54 (SE: 0.03). Additionally, compared to the customized VGG-16 which performed better than AutoML, the multimodal network had improved accuracy, 0.89 (SE: 0.03) vs 0.83 (SE: 0.02), sensitivity, 0.93 (SE: 0.04) vs 0.83 (SE: 0.003), and specificity, namely 0.86 (SE: 0.03) vs 0.84 (SE: 0.003).

CONCLUSION: This study opens promising avenues for BC risk assessment in women with benign breast disease. Integrating whole slide images and clinico-pathological characteristics through a multimodal approach significantly improved predictive model performance. Future research will explore DL techniques to understand BBD progression to invasive BC.

PMID:40193520 | DOI:10.1093/jncics/pkaf037

PLoS One. 2025 Apr 7;20(4):e0320494. doi: 10.1371/journal.pone.0320494. eCollection 2025.

ABSTRACT

The study aims to explore quality prediction in ceramic bearing grinding processing, with particular focus on the effect of grinding parameters on surface roughness. The study uses active learning regression model for model construction and optimization, and empirical analysis of surface quality under different grinding conditions. At the same time, various deep learning models are utilized to conduct experiments on quality prediction in grinding processing. The experimental setup covers a variety of grinding parameters, including grinding wheel linear speed, grinding depth and feed rate, to ensure the accuracy and reliability of the model under different conditions. According to the experimental results, when the grinding depth increases to 21 μm, the average training loss of the model further decreases to 0.03622, and the surface roughness Ra value significantly decreases to 0.1624 μm. In addition, the experiment also found that increasing the grinding wheel linear velocity and moderately adjusting the grinding depth can significantly improve the machining quality. For example, when the grinding wheel linear velocity is 45 m/s and the grinding depth is 0.015 mm, the Ra value drops to 0.1876 μm. The results of the study not only provide theoretical support for the grinding processing of ceramic bearings, but also provide a basis for the optimization of grinding parameters in actual production, which has an important industrial application value.

PMID:40193368 | DOI:10.1371/journal.pone.0320494

Hum Brain Mapp. 2025 Apr 1;46(5):e70201. doi: 10.1002/hbm.70201.

ABSTRACT

Tractography parcellation classifies streamlines reconstructed from diffusion MRI into anatomically defined fiber tracts for clinical and research applications. However, clinical scans often have incomplete fields of view (FOV) where brain regions are partially imaged, leading to partial, or truncated fiber tracts. To address this challenge, we introduce TractCloud-FOV, a deep learning framework that robustly parcellates tractography under conditions of incomplete FOV. We propose a novel training strategy, FOV-Cut Augmentation (FOV-CA), in which we synthetically cut tractograms to simulate a spectrum of real-world inferior FOV cutoff scenarios. This data augmentation approach enriches the training set with realistic truncated streamlines, enabling the model to achieve superior generalization. We evaluate the proposed TractCloud-FOV on both synthetically cut tractography and two real-life datasets with incomplete FOV. TractCloud-FOV significantly outperforms several state-of-the-art methods on all testing datasets in terms of streamline classification accuracy, generalization ability, tract anatomical depiction, and computational efficiency. Overall, TractCloud-FOV achieves efficient and consistent tractography parcellation in diffusion MRI with incomplete FOV.

PMID:40193105 | DOI:10.1002/hbm.70201

Jpn J Radiol. 2025 Apr 7. doi: 10.1007/s11604-025-01755-z. Online ahead of print.

ABSTRACT

PURPOSE: To compare the quality of standard 512-matrix, standard 1024-matrix, and Swin2SR-based 2048-matrix phantom images under different scanning protocols.

MATERIALS AND METHODS: The Catphan 600 phantom was scanned using a multidetector CT scanner under two protocols: 120 kV/100 mA (CT dose index volume = 3.4 mGy) to simulate low-dose CT, and 70 kV/40 mA (0.27 mGy) to simulate ultralow-dose CT. Raw data were reconstructed into standard 512-matrix images using three methods: filtered back projection (FBP), adaptive statistical iterative reconstruction at 40% intensity (ASIR-V), and deep learning image reconstruction at high intensity (DLIR-H). The Swin2SR super-resolution model was used to generate 2048-matrix images (Swin2SR-2048), while the super-resolution convolutional neural network (SRCNN) model generated 2048-matrix images (SRCNN-2048). The quality of 2048-matrix images generated by the two models (Swin2SR and SRCNN) was compared. Image quality was evaluated by ImQuest software (v7.2.0.0, Duke University) based on line pair clarity, task-based transfer function (TTF), image noise, and noise power spectrum (NPS).

RESULTS: At equivalent radiation doses and reconstruction method, Swin2SR-2048 images identified more line pairs than both standard-512 and standard-1024 images. Except for the 0.27 mGy/DLIR-H/standard kernel sequence, TTF-50% of Teflon increased after super-resolution processing. Statistically significant differences in TTF-50% were observed between the standard 512, 1024, and Swin2SR-2048 images (all p < 0.05). Swin2SR-2048 images exhibited lower image noise and NPSpeak compared to both standard 512- and 1024-matrix images, with significant differences observed in all three matrix types (all p < 0.05). Swin2SR-2048 images also demonstrated superior quality compared to SRCNN-2048, with significant differences in image noise (p < 0.001), NPSpeak (p < 0.05), and TTF-50% for Teflon (p < 0.05).

CONCLUSION: Transformer-enhanced 2048-matrix CT images improve spatial resolution and reduce image noise compared to standard-512 and -1024 matrix images.

PMID:40193009 | DOI:10.1007/s11604-025-01755-z

Rheumatol Int. 2025 Apr 7;45(4):91. doi: 10.1007/s00296-025-05825-3.

ABSTRACT

To analyse the types and applications of artificial intelligence (AI) technologies to predict treatment response in rheumatoid arthritis (RA) and spondyloarthritis (SpA). A comprehensive search in Medline, Embase, and Cochrane databases (up to August 2024) identified studies using AI to predict treatment response in RA and SpA. Data on study design, AI methodologies, data sources, and outcomes were extracted and synthesized. Findings were summarized descriptively. Of the 4257 articles identified, 89 studies met the inclusion criteria (74 on RA, 7 on SpA, 4 on Psoriatic Arthritis and 4 a mix of them). AI models primarily employed supervised machine learning techniques (e.g., random forests, support vector machines), unsupervised clustering, and deep learning. Data sources included electronic medical records, clinical biomarkers, genetic and proteomic data, and imaging. Predictive performance varied by methodology, with accuracy ranging from 60 to 70% and AUC values between 0.63 and 0.92. Multi-omics approaches and imaging-based models showed promising results in predicting responses to biologic DMARDs and JAK inhibitors but methodological heterogeneity limited generalizability. AI technologies exhibit substantial potential in predicting treatment responses in RA and SpA, enhancing personalized medicine. However, challenges such as methodological variability, data integration, and external validation remain. Future research should focus on refining AI models, ensuring their robustness across diverse patient populations, and facilitating their integration into clinical practice to optimize therapeutic decision-making in rheumatology.

PMID:40192881 | DOI:10.1007/s00296-025-05825-3

Insights Imaging. 2025 Apr 7;16(1):84. doi: 10.1186/s13244-025-01959-x.

ABSTRACT

OBJECTIVES: To address SPECT's radioactivity, complexity, and costliness in measuring renal function, this study employs artificial intelligence (AI) with non-contrast CT to estimate single-kidney glomerular filtration rate (GFR) and split renal function (SRF).

METHODS: 245 patients with atrophic kidney or hydronephrosis were included from two centers (Training set: 128 patients from Center I; Test set: 117 patients from Center II). The renal parenchyma and hydronephrosis regions in non-contrast CT were automatically segmented by deep learning. Radiomic features were extracted and combined with clinical characteristics using multivariable linear regression (MLR) to obtain a radiomics-clinical-estimated GFR (rcGFR). The relative contribution of single-kidney rcGFR to overall rcGFR, the percent renal parenchymal volume, and the percent renal hydronephrosis volume were combined by MLR to generate the estimation of SRF (rcphSRF). The Pearson correlation coefficient (r), mean absolute error (MAE), and Lin's concordance coefficient (CCC) were calculated to evaluate the correlations, differences, and agreements between estimations and SPECT-based measurements, respectively.

RESULTS: Compared to manual segmentation, deep learning-based automatic segmentation could reduce the average segmentation time by 434.6 times to 3.4 s. Compared to single-kidney GFR measured by SPECT, the rcGFR had a significant correlation of r = 0.75 (p < 0.001), MAE of 10.66 mL/min/1.73 m2, and CCC of 0.70. Compared to SRF measured by SPECT, the rcphSRF had a significant correlation of r = 0.92 (p < 0.001), MAE of 7.87%, and CCC of 0.88.

CONCLUSIONS: The non-contrast CT and AI methods are feasible to estimate single-kidney GFR and SRF in patients with atrophic kidney or hydronephrosis.

CRITICAL RELEVANCE STATEMENT: For patients with an atrophic kidney or hydronephrosis, non-contrast CT and artificial intelligence methods can be used to estimate single-kidney glomerular filtration rate and split renal function, which may minimize the radiation risk, enhance diagnostic efficiency, and reduce costs.

KEY POINTS: Renal function can be assessed using non-contrast CT and AI. Estimated renal function significantly correlated with the SPECT-based measurements. The efficiency of renal function estimation can be refined by the proposed method.

PMID:40192862 | DOI:10.1186/s13244-025-01959-x

Plant Mol Biol. 2025 Apr 7;115(2):56. doi: 10.1007/s11103-025-01582-w.

ABSTRACT

Precision agriculture methods can achieve the highest yield by applying the optimum amount of water, selecting appropriate pesticides, and managing crops in a way that minimises environmental impact. A rapidly emerging advanced research area, computer vision and deep learning, plays a significant role in effective crop management, such as superior genotype selection, plant classification, weed and pest detection, root localization, fruit counting and ripeness detection, and yield prediction. Also, phenotyping of plants involves analysing characteristics of plants such as chlorophyll content, leaf size, growth rate, leaf surface temperature, photosynthesis efficiency, leaf count, emergence time, shoot biomass, and germination time. This article presents an exhaustive study of recent techniques in computer vision and deep learning in plant science, with examples. The study provides the frequently used imaging parameters for plant image analysis with formulae, the most popular deep neural networks for plant classification and detection, object counting, and various applications. Furthermore, we discuss the publicly available plant image datasets for disease detection, weed control, and fruit detection with the evaluation metrics, tools and frameworks, future advancements and challenges in machine learning and deep learning models.

PMID:40192856 | DOI:10.1007/s11103-025-01582-w

Eur Radiol. 2025 Apr 7. doi: 10.1007/s00330-025-11554-9. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aimed to evaluate the performance of an artificial intelligence (AI)-based software for fracture detection in pediatric patients within a real-life clinical setting. Specifically, it sought to assess (1) the stand-alone AI performance in real-life cohort and in selected set of medicolegal relevant fractures and (2) its influence on the diagnostic performance of inexperienced emergency room physicians.

MATERIALS AND METHODS: The retrospective study involved 1672 radiographs of children under 18 years, obtained consecutively (real-life cohort) and selective (medicolegal cohort) in a tertiary pediatric emergency department. On these images, the stand-alone performance of a commercially available, deep learning-based software was determined. Additionally, three pediatric residents independently reviewed the radiographs before and after AI assistance, and the impact on their diagnostic accuracy was assessed.

RESULTS: In our cohort (median age 10.9 years, 59% male), the AI demonstrated a sensitivity of 92%, specificity of 83%, and accuracy of 87%. For medicolegally relevant fractures, the AI achieved a sensitivity of 100% for proximal tibia fractures, but only 68% for radial condyle fractures. AI assistance improved the residents' patient-wise sensitivity from 84 to 87%, specificity from 91 to 92%, and diagnostic accuracy from 88 to 90%. In 2% of cases, the readers, with the assistance of AI, erroneously discarded their correct diagnosis.

CONCLUSION: The AI exhibited strong stand-alone performance in a pediatric setting and can modestly enhance the diagnostic accuracy of inexperienced physicians. However, the economic implications must be weighed against the potential benefits in patient safety.

KEY POINTS: Question Does an artificial intelligence-based software for fracture detection influence inexperienced physicians in a real-life pediatric trauma population? Findings Addition of a well-performing artificial intelligence-based software led to a limited increase in diagnostic accuracy of inexperienced human readers. Clinical relevance Diagnosing fractures in children is especially challenging for less experienced physicians. High-performing artificial intelligence-based software as a "second set of eyes," enhances diagnostic accuracy in a common pediatric emergency room setting.

PMID:40192806 | DOI:10.1007/s00330-025-11554-9

Int J Legal Med. 2025 Apr 7. doi: 10.1007/s00414-025-03469-3. Online ahead of print.

ABSTRACT

Bone age assessment (BAA) means challenging tasks in forensic science especially in some extreme situations like only skulls found. This study aimed to develop an accurate three-dimensional deep learning (DL) framework at skull CT metadata for BAA and try to explore new skull markers. In this study, retrospective data of 385,175 Skull CT slices from 1,085 patients ranging from 16.32 to 90.56 years were obtained. The cohort was randomly split into a training set (90%, N = 976) and a test set (10%, N = 109). Additional 101 patients were collected from another center as an external validation set. Evaluations and comparisons with other state-of-the-art DL models and traditional machine learning (ML) models based on hand-crafted methods were hierarchically performed. The mean absolute error (MAE) was the primary parameter. A total of 1186 patients (mean age ± SD: 54.72 ± 14.91, 603 males & 583 females) were evaluated. Our method achieved the best MAE on the training set, test set and external validation set were 6.51, 5.70, and 8.86 years in males, while in females, the best MAE were 6.10, 7.84, and 10.56 years, respectively. In the test set, the MAE of other 2D or 3D models and ML methods based on manual features were ranged from 10.12 to 14.12. The model results showed a tendency of larger errors in the elderly group. The results suggested the proposed three-dimensional DL framework performed better than existing DL and manual methods. Furthermore, our framework explored new skeletal markers for BAA and could serve as a backbone for extracting features from three-dimensional skull CT metadata in a professional manner.

PMID:40192774 | DOI:10.1007/s00414-025-03469-3

Analyst. 2025 Apr 7. doi: 10.1039/d5an00075k. Online ahead of print.

ABSTRACT

Rapid and reliable detection of chemical warfare agents (CWAs) is essential for military defense and counter-terrorism operations. Although Raman spectroscopy provides a non-destructive method for on-site detection, existing methods show difficulty in coping with complex spectral overlap and concentration changes when analyzing mixtures containing trace components and highly complex mixtures. Based on the idea of convolutional neural networks and multi-layer perceptrons, this study proposes a qualitative and quantitative analysis algorithm of Raman spectroscopy based on deep learning (RS-MLP). The reference feature library is built from pure substance spectral features, while multi-head attention adaptively captures mixture weights. The MLP-Mixer then performs hierarchical feature matching for qualitative identification and quantitative analysis. The recognition rate of spectral data for the four types of combinations used for validation reached 100%, with an average root mean square error (RMSE) of less than 0.473% for the concentration prediction of three components. Furthermore, the model exhibited robust performance even under conditions of highly overlapping spectra. At the same time, the interpretability of the model is also enhanced. The model has excellent accuracy and robustness in component identification and concentration identification in complex mixtures and provides a practical solution for rapid and non-contact detection of persistent chemicals in complex environments.

PMID:40192710 | DOI:10.1039/d5an00075k

Eur Heart J Acute Cardiovasc Care. 2025 Apr 7:zuaf058. doi: 10.1093/ehjacc/zuaf058. Online ahead of print.

NO ABSTRACT

PMID:40192550 | DOI:10.1093/ehjacc/zuaf058

J Sports Sci. 2025 Apr 7:1-19. doi: 10.1080/02640414.2025.2488157. Online ahead of print.

ABSTRACT

Predicting high-run chases in cricket is a complex task influenced by various factors, including team rankings, match conditions, pitch behavior, and inning scores. This study evaluates the effectiveness of probabilistic machine learning models, namely Naïve Bayes (NB), Bayesian Network (BN), Bayesian Regularized Neural Network (BRNN), Hidden Naïve Bayes (HNB), Correlation Feature-Based Filter Weighting Naïve Bayes (CFWNB), and Class-Specific Attribute Weighted Naïve Bayes (CAWNB), in predicting high run chases in T20I cricket. Model performance was assessed using accuracy, precision, sensitivity, specificity, F1-score, AUC-ROC, and entropy, while Monte Carlo simulations ensured robustness across multiple iterations. Non-parametric statistical tests were employed due to the non-normal distribution of performance metrics, with the Friedman test revealing significant ranking variations among models. The results demonstrate that CAWNB consistently outperforms other models in terms of accuracy, precision, AUC, and F1-score, making it the most reliable choice for high-run chase prediction. Future research should explore hybrid Bayesian deep learning approaches, real-time data adaptation, and the application of these models to other cricket formats and sports analytics to further enhance predictive performance.

PMID:40192186 | DOI:10.1080/02640414.2025.2488157

Eur J Clin Invest. 2025 Apr;55 Suppl 1:e70002. doi: 10.1111/eci.70002.

ABSTRACT

BACKGROUND: Recent advancements in deep learning (DL), a subset of artificial intelligence, have shown the potential to automate and improve disease recognition, phenotyping and prediction of disease onset and outcomes by analysing various sources of medical data. The electrocardiogram (ECG) is a valuable tool for diagnosing and monitoring cardiovascular conditions.

METHODS: The implementation of DL in ECG analysis has been used to detect and predict rhythm abnormalities and conduction abnormalities, ischemic and structural heart diseases, with performance comparable to physicians. However, despite promising development of DL algorithms for automatic ECG analysis, the integration of DL-based ECG analysis and deployment of medical devices incorporating these algorithms into routine clinical practice remains limited.

RESULTS: This narrative review highlights the applications of DL in 12-lead ECG analysis. Furthermore, we review randomized controlled trials that assess the clinical effectiveness of these DL tools. Finally, it addresses different key barriers to widespread implementation in clinical practice, including regulatory hurdles, algorithm transparency and data privacy concerns.

CONCLUSIONS: By outlining both the progress and the obstacles in this field, this review aims to provide insights into how DL could shape the future of ECG analysis and enhance cardiovascular care in daily clinical practice.

PMID:40191935 | DOI:10.1111/eci.70002

Eur J Clin Invest. 2025 Apr;55 Suppl 1:e14370. doi: 10.1111/eci.14370.

ABSTRACT

BACKGROUND: The management of cardiotoxicity related to cancer therapies has emerged as a significant clinical challenge, prompting the rapid growth of cardio-oncology. As cancer treatments become more complex, there is an increasing need to enhance diagnostic and therapeutic strategies for managing their cardiovascular side effects.

OBJECTIVE: This review investigates the potential of artificial intelligence (AI) to revolutionize cardio-oncology by integrating diverse data sources to address the challenges of cardiotoxicity management.

METHODS: We explore applications of AI in cardio-oncology, focusing on its ability to leverage multiple data sources, including electronic health records, electrocardiograms, imaging modalities, wearable sensors, and circulating serum biomarkers.

RESULTS: AI has demonstrated significant potential in improving risk stratification and longitudinal monitoring of cardiotoxicity. By optimizing the use of electrocardiograms, non-invasive imaging, and circulating biomarkers, AI facilitates earlier detection, better prediction of outcomes, and more personalized therapeutic interventions. These advancements are poised to enhance patient outcomes and streamline clinical decision-making.

CONCLUSIONS: AI represents a transformative opportunity in cardio-oncology by advancing diagnostic and therapeutic capabilities. However, successful implementation requires addressing practical challenges such as data integration, model interpretability, and clinician training. Continued collaboration between clinicians and AI developers will be essential to fully integrate AI into routine clinical workflows.

PMID:40191923 | DOI:10.1111/eci.14370

ACS Nano. 2025 Apr 7. doi: 10.1021/acsnano.5c02004. Online ahead of print.

ABSTRACT

Swimming kinematics and macroscale mechanical testing have shown that the vertebral column of sharks acts as a biological spring, storing and releasing energy during locomotion. Using synchrotron X-ray nanotomography and deep-learning image segmentation, we studied the ultrastructure and deformation mechanism of mineralized shark vertebrae from Carcharhinus limbatus (Blacktip shark). The vertebral centrum con regions: the corpus calcareum, a hypermineralized double cone, and the intermediale, blocks of mineralized cartilage interspersed by unmineralized arches. At the micron scale, mineralized cartilage has previously been described as a 3D network of interconnected mineral plates that vary in thickness and spacing. The corpus calcareum consists of stacked, interconnected, curved mineralized planes permeated by a network of organic occlusions. The mineral network in the intermedialia resembles trabecular bone, including thicker struts in the direction opposite to the predominant biological strain. We characterized collagenous fiber elements winding around lacunar spaces in the intermedialia, and we hypothesize the swirling arrangement and elasticity of the fibers to be distributing stress. With little permanent deformation detected in mineralized structures, it is likely that the soft organic matrix is crucial for absorbing energy through deformation, irreversible damage, and viscoelastic behavior. In the corpus calcareum, cracks typically terminate toward thick struts along the mineral planes, resembling the microscale crack deflection and arrest mechanism found in other staggered biocomposites, such as nacre or bone. Using transmission electron microscopy (TEM), we observed preferentially oriented, needlelike bioapatite crystallites and d-band patterns of collagen type-II fibrils resulting from intrafibrillar mineralization.

PMID:40191917 | DOI:10.1021/acsnano.5c02004

Front Aging Neurosci. 2025 Mar 21;17:1532470. doi: 10.3389/fnagi.2025.1532470. eCollection 2025.

ABSTRACT

Conventional computer-aided diagnostic techniques for Alzheimer's disease (AD) predominantly rely on magnetic resonance imaging (MRI) in isolation. Genetic imaging methods, by establishing the link between genes and brain structures in disease progression, facilitate early prediction of AD development. While deep learning methods based on MRI have demonstrated promising results for early AD diagnosis, the limited dataset size has led most AD studies to lean on statistical approaches within the realm of imaging genetics. Existing deep-learning approaches typically utilize pre-defined regions of interest and risk variants from known susceptibility genes, employing relatively straightforward feature fusion methods that fail to fully capture the relationship between images and genes. To address these limitations, we proposed a multi-modal deep learning classification network based on MRI and single nucleotide polymorphism (SNP) data for AD diagnosis and mild cognitive impairment (MCI) progression prediction. Our model leveraged a convolutional neural network (CNN) to extract whole-brain structural features, a Transformer network to capture genetic features, and employed a cross-transformer-based network for comprehensive feature fusion. Furthermore, we incorporated an attention-map-based interpretability method to analyze and elucidate the structural and risk variants associated with AD and their interrelationships. The proposed model was trained and evaluated using 1,541 subjects from the ADNI database. Experimental results underscored the superior performance of our model in effectively integrating and leveraging information from both modalities, thus enhancing the accuracy of AD diagnosis and prediction.

PMID:40191788 | PMC:PMC11968703 | DOI:10.3389/fnagi.2025.1532470

J Med Signals Sens. 2025 Feb 28;15:6. doi: 10.4103/jmss.jmss_46_24. eCollection 2025.

ABSTRACT

BACKGROUND: Gastroesophageal reflux disease (GERD) is a prevalent digestive disorder that impacts millions of individuals globally. Multichannel intraluminal impedance-pH (MII-pH) monitoring represents a novel technique and currently stands as the gold standard for diagnosing GERD. Accurately characterizing reflux events from MII data are crucial for GERD diagnosis. Despite the initial introduction of clinical literature toward software advancements several years ago, the reliable extraction of reflux events from MII data continues to pose a significant challenge. Achieving success necessitates the seamless collaboration of two key components: a reflux definition criteria protocol established by gastrointestinal experts and a comprehensive analysis of MII data for reflux detection.

METHOD: In an endeavor to address this challenge, our team assembled a dataset comprising 201 MII episodes. We meticulously crafted precise reflux episode definition criteria, establishing the gold standard and labels for MII data.

RESULT: A variety of signal-analyzing methods should be explored. The first Isfahan Artificial Intelligence Competition in 2023 featured formal assessments of alternative methodologies across six distinct domains, including MII data evaluations.

DISCUSSION: This article outlines the datasets provided to participants and offers an overview of the competition results.

PMID:40191685 | PMC:PMC11970833 | DOI:10.4103/jmss.jmss_46_24