Transl Vis Sci Technol. 2025 Mar 3;14(3):6. doi: 10.1167/tvst.14.3.6.

ABSTRACT

Optical coherence tomography angiography (OCTA) is a signal processing and scan acquisition approach that enables OCT devices to clearly identify vascular tissue down to the capillary scale. As originally proposed, OCTA included several important limitations, including small fields of view relative to allied imaging modalities and the presence of confounding artifacts. New approaches, including both hardware and software, are solving these problems and can now produce high-quality angiograms from tissue throughout the retina and choroid. Image analysis tools have also improved, enabling OCTA data to be quantified at high precision and used to diagnose disease using deep learning models. This review highlights these advances and trends in OCTA technology, focusing on work produced since 2020.

PMID:40052848 | DOI:10.1167/tvst.14.3.6

J Proteome Res. 2025 Mar 7. doi: 10.1021/acs.jproteome.4c00972. Online ahead of print.

ABSTRACT

Mass spectrometry (MS)-based proteomics relies heavily on MS/MS (MS2) data, which do not fully exploit the available MS1 information. Traditional peptide identity propagation (PIP) methods, such as match-between-runs (MBR), are limited to similar runs, particularly with the same liquid chromatography (LC) gradients, thus potentially underutilizing available proteomics libraries. We introduce SWAPS, a novel and modular MS1-centric framework incorporating advances in peptide property prediction, extensive proteomics libraries, and deep-learning-based postprocessing to enable and explore PIP across more diverse experimental conditions and LC gradients. SWAPS substantially enhances precursor identification, especially in shorter gradients. On the example of 30, 15, and 7.5 min gradients, SWAPS achieves increases of 46.3, 86.2, and 112.1% on precursor level over MaxQuant's MS2-based identifications. Despite the inherent challenges in controlling false discovery rates (FDR) with MS1-based methods, SWAPS demonstrates strong efficacy in deconvoluting MS1 signals, offering powerful discrimination and deeper sequence exploration, while maintaining quantitative accuracy. By building on and applying peptide property predictions in practical contexts, SWAPS reveals that current models, while advanced, are still not fully comparable to experimental measurements, sparking the need for further research. Additionally, its modular design allows seamless integration of future improvements, positioning SWAPS as a forward-looking tool in proteomics.

PMID:40052690 | DOI:10.1021/acs.jproteome.4c00972

Eur Heart J Cardiovasc Imaging. 2025 Mar 7:jeaf081. doi: 10.1093/ehjci/jeaf081. Online ahead of print.

ABSTRACT

AIMS: Understanding determinants of thoracic aortic morphology is crucial for precise diagnostics and therapeutic approaches. This study aimed to automatically characterize ascending aortic morphology based on 3D non-contrast-enhanced magnetic resonance angiography (NC-MRA) data from the epidemiological cross-sectional German National Cohort (NAKO) and to investigate possible determinants of mid-ascending aortic diameter (mid-AAoD).

METHODS AND RESULTS: Deep learning (DL) automatically segmented the thoracic aorta and ascending aortic length, volume, and diameter was extracted from 25,073 NC-MRAs. Statistical analyses investigated relationships between mid-AAoD and demographic factors, hypertension, diabetes, alcohol, and tobacco consumption. Males exhibited significantly larger mid-AAoD than females (M:35.5±4.8mm, F:33.3±4.5mm). Age and body surface area (BSA) were positively correlated with mid-AAoD (age: male: r²=0.20, p<0.001, female: r²=0.16, p<0.001; BSA: male: r²=0.08, p<0.001, female: r²=0.05, p<0.001). Hypertensive and diabetic subjects showed higher mid-AAoD (ΔHypertension = 2.9 ± 0.5mm; ΔDiabetes = 1.5 ± 0.6mm). Hypertension was linked to higher mid-AAoD regardless of age and BSA, while diabetes and mid-AAoD were uncorrelated across age-stratified subgroups. Daily alcohol consumption (male: 37.4±5.1mm, female: 35.0±4.8mm) and smoking history exceeding 16.5 pack-years (male: 36.6±5.0mm, female: 33.9±4.3mm) exhibited highest mid-AAoD. Causal analysis (Peter-Clark algorithm) suggested that age, BSA, hypertension, and alcohol consumption are possibly causally related to mid-AAoD, while diabetes and smoking are likely spuriously correlated.

CONCLUSIONS: This study demonstrates the potential of DL and causal analysis for understanding ascending aortic morphology. By disentangling observed correlations using causal analysis, this approach identifies possible causal determinants, such as age, BSA, hypertension, and alcohol consumption. These findings can inform targeted diagnostics and preventive strategies, supporting clinical decision-making for cardiovascular health.

PMID:40052574 | DOI:10.1093/ehjci/jeaf081

J Food Sci. 2025 Mar;90(3):e70116. doi: 10.1111/1750-3841.70116.

ABSTRACT

Chronic diseases such as obesity and hypertension due to malnutrition can be prevented by following the appropriate diet, correct diet intake with correct measuring portion size, and developing healthy eating habits. Having a system that can automatically measure food consumption is important to determine whether individual nutritional needs are being met in order to accurately diagnose and solve nutritional problems, act quickly, and minimize the risk of malnutrition due to the cross-cultural diversity of foods. In this study, a deep learning system has been developed and implemented for automatically grouping and classifying foods. Dishes from Turkish cuisine were chosen as a sample for application and testing. The deep learning method used in this system is convolutional neural network (CNN) models based on image recognition. This study developed and implemented a deep learning system using CNNs to classify food groups and estimate portion sizes of Turkish cuisine dishes, achieving accuracy rates of up to 80% for food group classification and 80.47% for portion estimation with the inclusion of data augmentation.

PMID:40052549 | DOI:10.1111/1750-3841.70116

Brief Bioinform. 2025 Mar 4;26(2):bbaf087. doi: 10.1093/bib/bbaf087.

ABSTRACT

Cancer neoantigens are peptides that originate from alterations in the genome, transcriptome, or proteome. These peptides can elicit cancer-specific T-cell recognition, making them potential candidates for cancer vaccines. The rapid advancement of proteomics technology holds tremendous potential for identifying these neoantigens. Here, we provided an up-to-date survey about database-based search methods and de novo peptide sequencing approaches in proteomics, and we also compared these methods to recommend reliable analytical tools for neoantigen identification. Unlike previous surveys on mass spectrometry-based neoantigen discovery, this survey summarizes the key advancements in de novo peptide sequencing approaches that utilize artificial intelligence. From a comparative study on a dataset of the HepG2 cell line and nine mixed hepatocellular carcinoma proteomics samples, we demonstrated the potential of proteomics for the identification of cancer neoantigens and conducted comparisons of the existing methods to illustrate their limits. Understanding these limits, we suggested a novel workflow for neoantigen discovery as perspectives.

PMID:40052441 | DOI:10.1093/bib/bbaf087

Comput Methods Biomech Biomed Engin. 2025 Mar 7:1-14. doi: 10.1080/10255842.2025.2470809. Online ahead of print.

ABSTRACT

Acute kidney injury (AKI) is a prevalent clinical syndrome that causes over one-fifth of hospitalized patients worldwide to suffer from AKI. We proposed the GCAT, which aims to identify high-risk AKI patients in the hospital settings using the MIMIC-III dataset. Firstly, it fully explores the similarity of attribute features among a large number of patients and calculates the attribute similarity values between patients to generate a node similarity matrix. Then, it selects nodes with high similarity to construct a patient feature similarity network (PFSN). Experiments demonstrate that the GCAT achieves an accuracy of 88.57%, its effectiveness is superior to state-of-the-art methods.

PMID:40052403 | DOI:10.1080/10255842.2025.2470809

Glob Chang Biol. 2025 Mar;31(3):e70118. doi: 10.1111/gcb.70118.

ABSTRACT

Understanding the dynamics of soil respiration (Rs) and its environmental drivers is crucial for accurately modeling terrestrial carbon fluxes. However, current methodologies often lead to divergent estimates and rely on annual predictions that may overlook critical interactions occurring at seasonal scales. A critical knowledge gap lies in understanding how temporal resolution affects both Rs predictions and their environmental drivers. Here, we employ deep learning models to predict global Rs at monthly (MRM) and annual (ARM) scales from 1982 to 2018. We then consider three main drivers potentially affecting Rs, including temperature, precipitation, and a vegetation proxy (leaf area index; LAI). Our models demonstrate strong predictive capabilities with global Rs estimation of 79.4 ± 5.7 Pg C year-1 for the MRM and 78.3 ± 7.5 Pg C year-1 for ARM (mean ± SD). While the difference in global estimations between both models is small, there are notable disparities in the spatial contribution of dominant drivers. The MRM highlights an influence of both temperature and LAI, while the ARM emphasizes a dominant role of precipitation. These findings underscore the critical role of temporal resolution in capturing seasonal variations and identifying key Rs-environment relationships that annual models may obscure. High temporal resolution Rs predictions, such as those provided by the MRM, are essential for capturing nuanced seasonal interactions between Rs and its drivers, refining carbon flux models, detecting critical seasonal thresholds, and enhancing the reliability of future Earth system predictions. This work highlights the need for further research into monthly and seasonal Rs variations, as well as higher timescale resolutions, to advance our understanding of ecosystem carbon dynamics in a rapidly changing climate.

PMID:40052202 | DOI:10.1111/gcb.70118

Front Big Data. 2025 Feb 20;8:1515341. doi: 10.3389/fdata.2025.1515341. eCollection 2025.

ABSTRACT

Neurodegenerative diseases are chronic, progressive conditions that cause irreversible damage to the nervous system, particularly in aging populations. Early diagnosis is a critical challenge, as these diseases often develop slowly and without clear symptoms until significant damage has occurred. Recent advances in radiomics and genomics have provided valuable insights into the mechanisms of these diseases by identifying specific imaging features and genomic patterns. Radiogenomics enhances diagnostic capabilities by linking genomics with imaging phenotypes, offering a more comprehensive understanding of disease progression. The growing field of artificial intelligence (AI), including machine learning and deep learning, opens new opportunities for improving the accuracy and timeliness of these diagnoses. This review examines the application of AI-based radiogenomics in neurodegenerative diseases, summarizing key model designs, performance metrics, publicly available data resources, significant findings, and future research directions. It provides a starting point and guidance for those seeking to explore this emerging area of study.

PMID:40052173 | PMC:PMC11882605 | DOI:10.3389/fdata.2025.1515341

Front Oncol. 2025 Feb 20;15:1536039. doi: 10.3389/fonc.2025.1536039. eCollection 2025.

ABSTRACT

With the rapid development of the "Internet + Medical" model, artificial intelligence technology has been widely used in the analysis of medical images. Among them, the technology of using deep learning algorithms to identify features of ultrasound and pathological images and realize intelligent diagnosis of diseases has entered the clinical verification stage. This study is based on the application research of artificial intelligence technology in medical diagnosis and reviews the early screening and diagnosis of thyroid diseases. The cure rate of thyroid disease is high in the early stage, but once it deteriorates into thyroid cancer, the risk of death and treatment costs of the patient increase. At present, the early diagnosis of the disease still depends on the examination equipment and the clinical experience of doctors, and there is a certain misdiagnosis rate. Based on the above background, it is particularly important to explore a technology that can achieve objective screening of thyroid lesions in the early stages. This paper provides a comprehensive review of recent research on the early diagnosis of thyroid diseases using artificial intelligence technology. It integrates the findings of multiple studies and that traditional machine learning algorithms are widely used as research objects. The convolutional neural network model has a high recognition accuracy for thyroid nodules and thyroid pathological cell lesions. U-Net network model can significantly improve the recognition accuracy of thyroid nodule ultrasound images when used as a segmentation algorithm. This article focuses on reviewing the intelligent recognition technology of thyroid ultrasound images and pathological sections, hoping to provide researchers with research ideas and help clinicians achieve intelligent early screening of thyroid cancer.

PMID:40052126 | PMC:PMC11882420 | DOI:10.3389/fonc.2025.1536039

Front Sports Act Living. 2025 Feb 20;7:1570591. doi: 10.3389/fspor.2025.1570591. eCollection 2025.

ABSTRACT

[This corrects the article DOI: 10.3389/fspor.2024.1512010.].

PMID:40051920 | PMC:PMC11882509 | DOI:10.3389/fspor.2025.1570591

Int J Gen Med. 2025 Mar 1;18:1173-1180. doi: 10.2147/IJGM.S507100. eCollection 2025.

ABSTRACT

PURPOSE: This study aims to evaluate the performance of a deep learning-based artificial intelligence (AI) diagnostic system in the analysis of retinal diseases, assessing its consistency with expert diagnoses and its overall utility in screening applications.

METHODS: A total of 3076 patients attending our hospital underwent comprehensive ophthalmic examinations. Initial assessments were performed using the AI, the Comprehensive AI Retinal Expert (CARE) system, followed by thorough manual reviews to establish final diagnoses. A comparative analysis was conducted between the AI-generated results and the evaluations by senior ophthalmologists to assess the diagnostic reliability and feasibility of the AI system in the context of ophthalmic screening.

RESULTS: : The AI diagnostic system demonstrated a sensitivity of 94.12% and specificity of 98.60% for diabetic retinopathy (DR); 89.50% sensitivity and 98.33% specificity for age-related macular degeneration (AMD); 91.55% sensitivity and 97.40% specificity for suspected glaucoma; 90.77% sensitivity and 99.10% specificity for pathological myopia; 81.58% sensitivity and 99.49% specificity for retinal vein occlusion (RVO); 88.64% sensitivity and 99.18% specificity for retinal detachment; 83.33% sensitivity and 99.80% specificity for macular hole; 82.26% sensitivity and 99.23% specificity for epiretinal membrane; 94.55% sensitivity and 97.82% specificity for hypertensive retinopathy; 83.33% sensitivity and 99.74% specificity for myelinated fibers; and 75.00% sensitivity and 99.95% specificity for retinitis pigmentosa. Additionally, the system exhibited notable performance in screening for other prevalent conditions, including DR, suspected glaucoma, suspected glaucoma, pathological myopia, and hypertensive retinopathy.

CONCLUSIONS: : The AI-assisted screening system exhibits high sensitivity and specificity for a majority of retinal diseases, suggesting its potential as a valuable tool for screening practices. Its implementation is particularly beneficial for grassroots and community healthcare settings, facilitating initial diagnostic efforts and enhancing the efficacy of tiered ophthalmic care, with important implications for broader clinical adoption.

PMID:40051895 | PMC:PMC11882464 | DOI:10.2147/IJGM.S507100

Eur Heart J Imaging Methods Pract. 2025 Mar 6;2(4):qyaf011. doi: 10.1093/ehjimp/qyaf011. eCollection 2024 Oct.

ABSTRACT

AIMS: Assessment of cardiac function is essential for diagnosis and treatment planning in cardiovascular disease. Volume of cardiac regions and the derived measures of stroke volume (SV) and ejection fraction (EF) are most accurately calculated from imaging. This study aims to develop a fully automatic deep learning approach for calculation of cardiac function from computed tomography (CT).

METHODS AND RESULTS: Time-resolved CT data sets from 39 patients were used to train segmentation models for the left side of the heart including the left ventricle (LV), left atrium (LA), and left atrial appendage (LAA). We compared nnU-Net, 3D TransUNet, and UNETR. Dice Similarity Scores (DSS) were similar between nnU-Net (average DSS = 0.91) and 3D TransUNet (DSS = 0.89) while UNETR performed less well (DSS = 0.69). Intra-class correlation analysis showed nnU-Net and 3D TransUNet both accurately estimated LVSV (ICCnnU-Net = 0.95; ICC3DTransUNet = 0.94), LVEF (ICCnnU-Net = 1.00; ICC3DTransUNet = 1.00), LASV (ICCnnU-Net = 0.91; ICC3DTransUNet = 0.80), LAEF (ICCnnU-Net = 0.95; ICC3DTransUNet = 0.81), and LAASV (ICCnnU-Net = 0.79; ICC3DTransUNet = 0.81). Only nnU-Net significantly predicted LAAEF (ICCnnU-Net = 0.68). UNETR was not able to accurately estimate cardiac function. Time to convergence during training and time needed for inference were both faster for 3D TransUNet than for nnU-Net.

CONCLUSION: nnU-Net outperformed two different vision transformer architectures for the segmentation and calculation of function parameters for the LV, LA, and LAA. Fully automatic calculation of cardiac function parameters from CT using deep learning is fast and reliable.

PMID:40051867 | PMC:PMC11883084 | DOI:10.1093/ehjimp/qyaf011

Chronic Dis Transl Med. 2024 Jun 9;11(1):1-21. doi: 10.1002/cdt3.137. eCollection 2025 Mar.

ABSTRACT

Chronic diseases such as heart disease, cancer, and diabetes are leading drivers of mortality worldwide, underscoring the need for improved efforts around early detection and prediction. The pathophysiology and management of chronic diseases have benefitted from emerging fields in molecular biology like genomics, transcriptomics, proteomics, glycomics, and lipidomics. The complex biomarker and mechanistic data from these "omics" studies present analytical and interpretive challenges, especially for traditional statistical methods. Machine learning (ML) techniques offer considerable promise in unlocking new pathways for data-driven chronic disease risk assessment and prognosis. This review provides a comprehensive overview of state-of-the-art applications of ML algorithms for chronic disease detection and prediction across datasets, including medical imaging, genomics, wearables, and electronic health records. Specifically, we review and synthesize key studies leveraging major ML approaches ranging from traditional techniques such as logistic regression and random forests to modern deep learning neural network architectures. We consolidate existing literature to date around ML for chronic disease prediction to synthesize major trends and trajectories that may inform both future research and clinical translation efforts in this growing field. While highlighting the critical innovations and successes emerging in this space, we identify the key challenges and limitations that remain to be addressed. Finally, we discuss pathways forward toward scalable, equitable, and clinically implementable ML solutions for transforming chronic disease screening and prevention.

PMID:40051825 | PMC:PMC11880127 | DOI:10.1002/cdt3.137

Front Psychiatry. 2025 Feb 20;16:1532256. doi: 10.3389/fpsyt.2025.1532256. eCollection 2025.

ABSTRACT

INTRODUCTION: Long-time mobile phone use (LTMPU) has been linked to emotional issues such as anxiety and depression while the enlarged perivascular spaces (EPVS), as marker of neuroinflammation, is closely related with mental disorders. In the current study, we aim to develop a predictive model utilizing MRI-quantified EPVS metrics and machine learning algorithms to assess the severity of anxiety and depression symptoms in patients with LTMPU.

METHODS: Eighty-two participants with LTMPU were included, with 37 suffering from anxiety and 44 suffering from depression. Deep learning algorithms were used to segment EPVS lesions and extract quantitative metrics. Comparison and correlation analyses were performed to investigate the relationship between EPVS and self-reported mood states. Training and testing datasets were randomly assigned in the ratio of 8:2 to perform radiomics analysis, where EPVS metrics combined with sex and age were used to select the most valuable features to construct machine learning models for predicting the severity of anxiety and depression.

RESULTS: Several EPVS features were significantly different between the two comparisons. For classifying anxiety status, eight features were selected to construct a logistic regression model, with an AUC of 0.819 (95%CI 0.573-1.000) in the testing dataset. For classifying depression status, eight features were selected to construct a K nearest neighbors model with an AUC value of 0.931 (95%CI 0.814-1.000) in the testing dataset.

DISCUSSION: The utilization of MRI-quantified EPVS metrics combined with machine-learning algorithms presents a promising method for evaluating severity of anxiety and depression symptoms in patients with LTMPU, which might introduce a non-invasive, objective, and quantitative approach to enhance diagnostic efficiency and guide personalized treatment strategies.

PMID:40051766 | PMC:PMC11882520 | DOI:10.3389/fpsyt.2025.1532256

Front Med (Lausanne). 2025 Feb 20;12:1497651. doi: 10.3389/fmed.2025.1497651. eCollection 2025.

ABSTRACT

BACKGROUND: The prognostic prediction of patients with hypercapnic respiratory failure holds significant clinical value. The objective of this study was to develop and validate a predictive model for predicting survival in patients with hypercapnic respiratory failure.

METHODS: The study enrolled a total of 697 patients with hypercapnic respiratory failure, including 565 patients from the First People's Hospital of Yancheng in the modeling group and 132 patients from the People's Hospital of Jiangsu Province in the external validation group. The three selected models were random survival forest (RSF), DeepSurv, a deep learning-based survival prediction algorithm, and Cox Proportional Risk (CoxPH). The model's predictive performance was evaluated using the C-index and Brier score. Receiver operating characteristic curve (ROC), area under ROC curve (AUC), and decision curve analysis (DCA) were employed to assess the accuracy of predicting the prognosis for survival at 6, 12, 18, and 24 months.

RESULTS: The RSF model (c-index: 0.792) demonstrated superior predictive ability for the prognosis of patients with hypercapnic respiratory failure compared to both the traditional CoxPH model (c-index: 0.699) and DeepSurv model (c-index: 0.618), which was further validated on external datasets. The Brier Score of the RSF model demonstrated superior performance, consistently measuring below 0.25 at the 6-month, 12-month, 18-month, and 24-month intervals. The ROC curve confirmed the superior discrimination of the RSF model, while DCA demonstrated its optimal clinical net benefit in both the modeling group and the external validation group.

CONCLUSION: The RSF model offered distinct advantages over the CoxPH and DeepSurv models in terms of clinical evaluation and monitoring of patients with hypercapnic respiratory failure.

PMID:40051730 | PMC:PMC11882423 | DOI:10.3389/fmed.2025.1497651

BMC Med Inform Decis Mak. 2025 Mar 6;25(1):113. doi: 10.1186/s12911-025-02924-w.

ABSTRACT

BACKGROUND: Dementia is a neurological syndrome marked by cognitive decline. Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the common forms of dementia, each with distinct progression patterns. Early and accurate diagnosis of dementia cases (AD and FTD) is crucial for effective medical care, as both conditions have similar early-symptoms. EEG, a non-invasive tool for recording brain activity, has shown potential in distinguishing AD from FTD and mild cognitive impairment (MCI).

METHODS: This study aims to develop a deep learning-based classification system for dementia by analyzing EEG derived scout time-series signals from deep brain regions, specifically the hippocampus, amygdala, and thalamus. Scout time series extracted via the standardized low-resolution brain electromagnetic tomography (sLORETA) technique are utilized. The time series is converted to image representations using continuous wavelet transform (CWT) and fed as input to deep learning models. Two high-density EEG datasets are utilized to validate the efficacy of the proposed method: the online BrainLat dataset (128 channels, comprising 16 AD, 13 FTD, and 19 healthy controls (HC)) and the in-house IITD-AIIA dataset (64 channels, including subjects with 10 AD, 9 MCI, and 8 HC). Different classification strategies and classifier combinations have been utilized for the accurate mapping of classes in both data sets.

RESULTS: The best results were achieved using a product of probabilities from classifiers for left and right subcortical regions in conjunction with the DenseNet model architecture. It yield accuracies of 94.17 % and 77.72 % on the BrainLat and IITD-AIIA datasets, respectively.

CONCLUSIONS: The results highlight that the image representation-based deep learning approach has the potential to differentiate various stages of dementia. It pave the way for more accurate and early diagnosis, which is crucial for the effective treatment and management of debilitating conditions.

PMID:40050853 | DOI:10.1186/s12911-025-02924-w

BMC Med Imaging. 2025 Mar 6;25(1):77. doi: 10.1186/s12880-025-01616-1.

ABSTRACT

Automated segmentation of retinal blood vessels in fundus images plays a key role in providing ophthalmologists with critical insights for the non-invasive diagnosis of common eye diseases. Early and precise detection of these conditions is essential for preserving vision, making vessel segmentation crucial for identifying vascular diseases that pose a threat to vision. However, accurately segmenting blood vessels in fundus images is challenging due to factors such as significant variability in vessel scale and appearance, occlusions, complex backgrounds, variations in image quality, and the intricate branching patterns of retinal vessels. To overcome these challenges, the Unified Gated Swin Transformer with Multi-Feature Full Fusion (UGS-M3F) model has been developed as a powerful deep learning framework tailored for retinal vessel segmentation. UGS-M3F leverages its Unified Multi-Context Feature Fusion (UM2F) and Gated Boundary-Aware Swin Transformer (GBS-T) modules to capture contextual information across different levels. The UM2F module enhances the extraction of detailed vessel features, while the GBS-T module emphasizes small vessel detection and ensures extensive coverage of large vessels. Extensive experimental results on publicly available datasets, including FIVES, DRIVE, STARE, and CHAS_DB1, show that UGS-M3F significantly outperforms existing state-of-the-art methods. Specifically, UGS-M3F achieves a Dice Coefficient (DC) improvement of 2.12% on FIVES, 1.94% on DRIVE, 2.52% on STARE, and 2.14% on CHAS_DB1 compared to the best-performing baseline. This improvement in segmentation accuracy has the potential to revolutionize diagnostic techniques, allowing for more precise disease identification and management across a range of ocular conditions.

PMID:40050753 | DOI:10.1186/s12880-025-01616-1

BMC Med Imaging. 2025 Mar 6;25(1):78. doi: 10.1186/s12880-025-01610-7.

ABSTRACT

Deep learning methods have been migrated to rectal cancer staging as a classification process based on magnetic resonance images (MRIs). Typical approaches suffer from the imperceptible variation of images from different stage. The data augmentation also introduces scale invariance and rotation consistency problems after converting MRIs to 2D visible images. Moreover, the correctly labeled images are inadequate since T-staging requires pathological examination for confirmation. It is difficult for classification model to characterize the distinguishable features with limited labeled data. In this article, Laplace of Gaussian (LoG) filter is used to enhance the texture details of converted MRIs and we propose a new method named LoG-staging to predict the T stages of rectal cancer patients. We first use the LoG operator to clarify the fuzzy boundaries of rectal cancer cell proliferation. Then, we propose a new feature clustering method by leveraging the maximization of mutual information (MMI) mechanism which jointly learns the parameters of a neural network and the cluster assignments of features. The assignments are used as labels for the next round of training, which compensate the inadequacy of labeled training data. Finally, we experimentally verify that the LoG-staging is more accurate than the nonlinear dimensionality reduction in predicting the T stages of rectal cancer. We innovatively implement information bottleneck (IB) method in T-staging of rectal cancer based on image classification and impressive results are obtained.

PMID:40050741 | DOI:10.1186/s12880-025-01610-7

NPJ Digit Med. 2025 Mar 6;8(1):144. doi: 10.1038/s41746-025-01483-8.

ABSTRACT

The DESTINY-Breast04 trial has recently demonstrated survival benefits of trastuzumab-deruxtecan (T-DXd) in metastatic breast cancer patients with low Human Epidermal Growth Factor Receptor 2 (HER2) expression. Accurate differentiation of HER2 scores has now become crucial. However, visual immunohistochemistry (IHC) scoring is labour-intensive and prone to high interobserver variability, and artificial intelligence (AI) has emerged as a promising tool in diagnostic medicine. We conducted a diagnostic meta-analysis to evaluate AI's performance in classifying HER2 IHC scores, demonstrating high accuracy in predicting T-DXd eligibility, with a pooled sensitivity of 0.97 [95% CI 0.96-0.98] and specificity of 0.82 [95% CI 0.73-0.88]. Meta-regression revealed better performance with deep learning and patch-based analysis, while performance declined in externally validated and those utilising commercially available algorithms. Our findings indicate that AI holds promising potential in accurately identifying HER2-low patients and excels in distinguishing 2+ and 3+ scores.

PMID:40050686 | DOI:10.1038/s41746-025-01483-8

Sci Rep. 2025 Mar 6;15(1):7817. doi: 10.1038/s41598-025-92582-9.

ABSTRACT

This study presents a novel hybrid deep learning model for arrhythmia classification from electrocardiogram signals, utilizing the stockwell transform for feature extraction. As ECG signals are time-series data, they are transformed into the frequency domain to extract relevant features. Subsequently, a CNN is employed to capture local patterns, while a transformer architecture learns long-term dependencies. Unlike traditional CNN-based models that require R-peak detection, the proposed model operates without it and demonstrates superior accuracy and efficiency. The findings contribute to enhancing the accuracy of ECG-based arrhythmia diagnosis and are applicable to real-time monitoring systems. Specifically, the model achieves an accuracy of 97.8% on the Icentia11k dataset using four arrhythmia classes and 99.58% on the MIT-BIH dataset using five arrhythmia classes.

PMID:40050678 | DOI:10.1038/s41598-025-92582-9