Radiol Artif Intell. 2025 Apr 9:e240459. doi: 10.1148/ryai.240459. Online ahead of print.

ABSTRACT

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate the predictive value of deep learning (DL)-based coronary artery disease (CAD) extent analysis for major adverse cardiac events (MACEs) in patients with acute chest pain presenting to the emergency department (ED). Materials and Methods This retrospective multicenter observational study included consecutive patients with acute chest pain who underwent coronary CT angiography (CCTA) at three institutional EDs from January 2018 to December 2022. Patients were classified as having no CAD, nonobstructive CAD, or obstructive CAD using a DL model. The primary outcome was MACEs during follow-up, defined as a composite of cardiac death, nonfatal myocardial infarction, and hospitalization for unstable angina. Cox proportional hazards regression models were used to evaluate the predictors of MACEs. Results The study included 408 patients (224 male; mean age, 59.4 ± 14.6 years). The DL model classified 162 (39.7%) patients as having no CAD, 94 (23%) as having nonobstructive CAD, and 152 (37.3%) as having obstructive CAD. Sixty-three (15.4%) patients experienced MACEs during follow-up. Patients with MACEs had a higher prevalence of obstructive CAD than those without (P < .001). In multivariate analysis model 1 (clinical risk factors), dyslipidemia (Hazard ratio [HR], 2.15 and elevated Troponin-T (HR 2.13) predicted MACEs (all P < .05). In model 2 (clinical risk factors + DL-based CAD extent), obstructive CAD detected by the DL model was the most significant independent predictor of MACEs (HR, 88.07, P < .001). Harrell's C-statistic showed that DL-based CAD extent enhanced the risk stratification beyond clinical risk factors (Harrell's C-statistics: 0.94 versus 0.80, P < .001). Conclusion DL-based detection of obstructive CAD demonstrated stronger predictive value than clinical risk factors for MACEs in patients with acute chest pain presenting to the ED. ©RSNA, 2025.

PMID:40202417 | DOI:10.1148/ryai.240459

Radiol Artif Intell. 2025 Apr 9:e240271. doi: 10.1148/ryai.240271. Online ahead of print.

ABSTRACT

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop an adaptive dual-task deep learning model (ThyNet-S) for detection and classification of thyroid lesions at US screening. Materials and Methods The retrospective study used a multicenter dataset comprising 35008 thyroid US images of 23294 individual examinations (mean age, 40.4 years ± 13.1[SD], 17587 female) from 7 medical centers during January 2009 and December 2021. Of these, 29004 images were used for model development and 6004 images for validation. The model determined cancer risk for each image and automatically triaged images with normal thyroid and benign nodules by dynamically integrating lesion detection through pixel-level feature analysis and lesion classification through deep semantic features analysis. Diagnostic performance of screening assisted by the model (ThyNet-S triaged screening) and traditional screening (radiologists alone) was assessed by comparing sensitivity, specificity, accuracy and AUC using McNemar's test and Delong test. The influence of ThyNet-S on radiologist workload and clinical decision-making was also assessed. Results ThyNet-S-assisted triaged screening achieved higher AUC than original screening in six senior and six junior radiologists (0.93 versus 0.91, and 0.92 versus 0.88, respectively, all P < .001). The model improved sensitivity for junior radiologists (88.2% versus 86.8%, P <.001). Notably, the model reduced radiologists' workload by triaging 60.4% of cases as not potentially malignant, which did not require further interpretation. The model simultaneously decreased unnecessary fine needle aspiration rate from 38.7% to 14.9% and 11.5% when used independently or in combination with Thyroid Imaging Reporting and Data System, respectively. Conclusion ThyNet-S improved efficiency of thyroid cancer screening and optimized clinical decision-making. ©RSNA, 2025.

PMID:40202416 | DOI:10.1148/ryai.240271

ChemSusChem. 2025 Apr 9:e202500282. doi: 10.1002/cssc.202500282. Online ahead of print.

ABSTRACT

The parasitic hydrogen evolution reaction (HER) hinders electrolyte transport. It reduces the effective electrochemical surface area in the negative half-cell of vanadium redox flow batteries (VRFBs), resulting in substantial efficiency losses. We investigated the formation and evolution of hydrogen bubbles within VRFB electrodes through comprehensive experimental characterization and a detailed analysis of the resolved bubbles. The electrode was imaged using synchrotron X-ray tomography, and gas bubbles in the images were identified and characterized using a deep learning model combined with a morphological analysis tool. The HER intensity increases at more negative working electrode potentials, causing residual bubbles to grow and fuse in the electrode central region. In contrast, independent bubbles predominantly form at the electrode edges. Furthermore, the bubble growth leads to the gradual development of irregular shapes. These observations provide insights into bubble formation and evolution rules, contributing to a better understanding of the system.

PMID:40202080 | DOI:10.1002/cssc.202500282

J Pharm Anal. 2025 Mar;15(3):101045. doi: 10.1016/j.jpha.2024.101045. Epub 2024 Jul 18.

ABSTRACT

Targeted covalent inhibitors, primarily targeting cysteine residues, have attracted great attention as potential drug candidates due to good potency and prolonged duration of action. However, their discovery is challenging. In this research, a database-assisted liquid chromatography-tandem mass spectrometry (LC-MS/MS) strategy was developed to quickly discover potential cysteine-targeting compounds. First, compounds with potential reactive groups were selected and incubated with N-acetyl-cysteine in microsomes. And the precursor ions of possible cysteine-adducts were predicted based on covalent binding mechanisms to establish in-house database. Second, substrate-independent product ions produced from N-acetyl-cysteine moiety were selected. Third, multiple reaction monitoring scan was conducted to achieve sensitive screening for cysteine-targeting compounds. This strategy showed broad applicability, and covalent compounds with diverse structures were screened out, offering structural resources for covalent inhibitors development. Moreover, the screened compounds, norketamine and hydroxynorketamine, could modify synaptic transmission-related proteins in vivo, indicating their potential as covalent inhibitors. This experimental-based screening strategy provides a quick and reliable guidance for the design and discovery of covalent inhibitors.

PMID:40201900 | PMC:PMC11978337 | DOI:10.1016/j.jpha.2024.101045

Bioinform Adv. 2025 Feb 18;5(1):vbaf029. doi: 10.1093/bioadv/vbaf029. eCollection 2025.

ABSTRACT

MOTIVATION: Understanding which genes are significantly affected by drugs is crucial for drug repurposing, as drugs targeting specific pathways in one disease might be effective in another with similar genetic profiles. By analyzing gene expression changes in cells before and after drug treatment, we can identify the genes most impacted by drugs.

RESULTS: The Biologically-Aware Interpretable Differential Gene Expression Ranking (BADGER) model is an interpretable model designed to predict gene expression changes resulting from interactions between cancer cell lines and chemical compounds. The model enhances explainability through integration of prior knowledge about drug targets via pathway information, handles novel cancer cell lines through similarity-based embedding, and employs three attention blocks that mimic the cascading effects of chemical compounds. This model overcomes previous limitations of cell line range and explainability constraints in drug-cell response studies. The model demonstrates superior performance over baselines in both unseen cell and unseen pair split evaluations, showing robust prediction capabilities for untested drug-cell line combinations.

AVAILABILITY AND IMPLEMENTATION: This makes it particularly valuable for drug repurposing scenarios, especially in developing therapeutic plans for new or resistant diseases by leveraging similarities with other diseases. All code and data used in this study are available at https://github.com/dmis-lab/BADGER.git.

PMID:40201234 | PMC:PMC11978390 | DOI:10.1093/bioadv/vbaf029

Front Pharmacol. 2025 Mar 25;16:1502379. doi: 10.3389/fphar.2025.1502379. eCollection 2025.

ABSTRACT

BACKGROUND: Dopamine replacement therapy is a cornerstone of Parkinson's disease treatment. In clinical practice, there is considerable variability in patients' responses, tolerability, and safety regarding anti-parkinsonian medications, which is largely influenced by genetic polymorphisms in pharmacokinetic and pharmacodynamic genes. However, the application of multigenetic pharmacogenomics-guided treatment (MPGT) to optimize therapeutic outcomes in Parkinson's disease (PD) remains under-explored. In this study, we conducted a prospective cohort investigation to evaluate the potential benefits of MPGT on motor symptoms in PD patients.

METHODS: A total of 28 patients with PD were followed for 4 weeks. Among them, 22 patients underwent multigenetic pharmacogenomic testing, with 13 receiving treatments based on the test results (MPGT group). The remaining 15 received standard care (TAU group). Baseline characteristics, as well as changes in Unified Parkinson's Disease Rating Scale (UPDRS) III scores and sub-scores, were compared between the two groups. Associations between various single nucleotide polymorphisms (SNPs) and treatment outcomes were analyzed using generalized linear models.

RESULTS: At the 4-week follow-up, the MPGT group showed significantly greater reductions in UPDRS III total scores (p < 0.05) and limb sub-scores (p < 0.01) compared to the TAU group. These differences remained significant after adjusting for increases in levodopa equivalent daily dose (p = 0.011 and p = 0.002, respectively) and piribedil use (p = 0.006 and p = 0.004, respectively). Patients homozygous for the major allele of rs4984241 (AA vs. AG+GG, p = 0.003), rs4680 (GG vs. GA+AA, p = 0.013), rs1076560/rs2283265 (CC vs. AC+AA, p = 0.039) and rs622342 (AA vs. AC, p = 0.043) showed greater improvement in total UPDRS III, postural instability and gait difficulty (PIGD), rigidity and tremor scores, respectively, compared to those carrying at least one minor allele.

CONCLUSION: MGPT demonstrates significant potential as a valuable tool for personalized treatment in PD patients. Additionally, we identified several SNPs associated with the responsiveness to chronic administration of multiple anti-parkinsonian drugs. However, to confirm these findings, well-designed studies with larger, well-characterized samples are necessary.

PMID:40201683 | PMC:PMC11975922 | DOI:10.3389/fphar.2025.1502379

Pharmacogenomics. 2025 Apr 9:1-5. doi: 10.1080/14622416.2025.2489916. Online ahead of print.

ABSTRACT

The development of polygenic risk scores (PRSs), which make use of genetic testing to assess an individual's risk of developing certain diseases or conditions based on collective genetic variant information, can be applied in drug development to enrich clinical trials or predict response to treatment. From querying documents submitted to the Food & Drug Administration, the landscape of use of PRSs across time shows increased use in guiding clinical trials. Of the clinical trial protocols submitted, most were in the therapeutic areas of neurology, radiology (imaging and diagnostic pharmaceuticals), psychiatry, and oncology. Use of PRSs in clinical trials is most frequent in early drug development (phase 1, phase 1/2, or phase 3) and generally supports secondary or exploratory analyses. Additionally, about half of the protocols developed novel PRSs, and the other half used preexisting PRSs. As researchers, regulators, and clinicians aim to understand the results and implications of PRSs in clinical trials, the continued use of PRSs, despite being less common, reinforces the need for further exploration.

PMID:40200755 | DOI:10.1080/14622416.2025.2489916

Stud Health Technol Inform. 2025 Apr 8;323:131-135. doi: 10.3233/SHTI250063.

ABSTRACT

Pharmacogenomics (PGx) enables personalized medication optimization, potentially improving clinical outcomes, particularly for older adults who frequently experience polypharmacy. CYP2D6 is an enzyme that plays a crucial role in the metabolism of many drugs. The prevalence of CYP2D6 variants and their impact on potential drug-gene interactions in patients with prostate cancer (PPC), who are mostly represented by older adults, have not been systematically investigated. In this study, we analyzed the genotypes and phenotypes for CYP2D6 gene and medical prescription of over 3000 PPC enrolled in the All of Us research program. We found that about 20% of the patients were identified as non-normal metabolizer types with increased or reduced function. 67% of the patients had at least one medication mainly or partly metabolized by CYP2D6. 13% of the patients had an actionable phenotype and prior exposure to an impacted medication. These findings suggest a potential roadmap for improvement of medication prescription in PPC using PGx guidelines in routine clinical practice.

PMID:40200460 | DOI:10.3233/SHTI250063

JAC Antimicrob Resist. 2025 Apr 8;7(2):dlaf053. doi: 10.1093/jacamr/dlaf053. eCollection 2025 Apr.

ABSTRACT

OBJECTIVES: Pseudomonas aeruginosa is one of the major drivers of morbidity and mortality in patients with chronic underlying diseases. Whereas cystic fibrosis (CF) P. aeruginosa strains have been well studied, non-CF bronchiectasis isolates have received less scientific attention.

METHODS: We determined the antibiotic susceptibility profiles of a collection of 100 P. aeruginosa isolates recovered from a total of 100 non-CF bronchiectasis patients attending a Catalonian hospital. All carbapenemase-producing isolates were characterized by WGS.

RESULTS: Twelve isolates were classified as MDR (12%) and six were found to be carbapenemase (VIM-2) producers (6%). Of note, two of the VIM-2-producing isolates were carbapenem susceptible due to the presence of inactivating mutations in MexAB-OprM efflux pump components. These isolates exhibited properties of chronic P. aeruginosa isolates, such as mutator or mucoid phenotypes that are associated with persistent infections despite intensive antibiotic therapies. The phylogenetic analysis evidenced that all VIM-2 isolates belonged to the high-risk clone ST235. Core-genome MLST analysis revealed 7-260 allelic differences, arguing against recent transmission but a common source of infection or an ancient interpatient transmission event could not be ruled out.

CONCLUSIONS: Altogether, these findings suggest that P. aeruginosa chronic respiratory infections can be an important and silent reservoir of transferable resistance determinants and P. aeruginosa high-risk clones, thus contributing to their increased resistance and worldwide dissemination.

PMID:40201539 | PMC:PMC11976719 | DOI:10.1093/jacamr/dlaf053

Small. 2025 Apr 9:e2410258. doi: 10.1002/smll.202410258. Online ahead of print.

ABSTRACT

The human intestine, a biomechanically active organ, generates cyclic mechanical forces crucial for maintaining its health and functions. Yet, the physiological impact of these forces on gut microbiota dynamics remains largely unexplored. In this study, we investigate how cyclic intestinal motility influences the dynamics of gut microbial communities within a 3D gut-like structure (µGut). To enable the study, a tunable flow Gut Microbiome-on-a-Chip (tfGMoC) is developed that recapitulates the cyclic expansion and compression of intestinal motility while allowing high-magnification imaging of microbial communities within a 3D stratified, biomimetic gut epithelium. Using deep learning-based microbial analysis, it is found that hydrodynamic forces organize microbial communities by promoting distinct spatial exploration behaviors in microorganisms with varying motility characteristics. Empirical evidence demonstrates the impact of gut motility forces in maintaining a balanced gut microbial composition, enhancing both the diversity and stability of the community - key factors for a healthy microbiome. This study, leveraging the new tfGMoC platform, uncovers previously unknown effects of intestinal motility on modulating gut microbial behaviors and community organizations. This will be critical for a deeper understanding of host-microbiome interactions in the emerging field of microbiome therapeutics.

PMID:40201941 | DOI:10.1002/smll.202410258

Head Neck Tumor Segm MR Guid Appl (2024). 2025;15273:204-213. doi: 10.1007/978-3-031-83274-1_15. Epub 2025 Mar 3.

ABSTRACT

Radiation therapy is one of the most frequently applied cancer treatments worldwide, especially in the context of head and neck cancer. Today, MRI-guided radiation therapy planning is becoming increasingly popular due to good soft tissue contrast, lack of radiation dose delivered to the patient, and the capability of performing functional imaging. However, MRI-guided radiation therapy requires segmenting of the cancer both before and during radiation therapy. So far, the segmentation was often performed manually by experienced radiologists, however, recent advances in deep learning-based segmentation suggest that it may be possible to perform the segmentation automatically. Nevertheless, the task is arguably more difficult when using MRI compared to e.g. PET-CT because even manual segmentation of head and neck cancer in MRI volumes is challenging and time-consuming. The importance of the problem motivated the researchers to organize the HNTSMRG challenge with the aim of developing the most accurate segmentation methods, both before and during MRI-guided radiation therapy. In this work, we benchmark several different state-of-the-art segmentation architectures to verify whether the recent advances in deep encoder-decoder architectures are impactful for low data regimes and low-contrast tasks like segmenting head and neck cancer in magnetic resonance images. We show that for such cases the traditional residual UNetbased method outperforms (DSC = 0.775/0.701) recent advances such as UNETR (DSC = .617/0.657), SwinUNETR (DSC = 0.757/0.700), or SegMamba (DSC = 0.708/0.683). The proposed method (lWM team) achieved a mean aggregated Dice score on the closed test set at the level of 0.771 and 0.707 for the pre- and mid-therapy segmentation tasks, scoring 14th and 6th place, respectively. The results suggest that proper data preparation, objective function, and preprocessing are more influential for the segmentation of head and neck cancer than deep network architecture.

PMID:40201773 | PMC:PMC11977277 | DOI:10.1007/978-3-031-83274-1_15

Head Neck Tumor Segm MR Guid Appl (2024). 2025;15273:259-273. doi: 10.1007/978-3-031-83274-1_21. Epub 2025 Mar 3.

ABSTRACT

Automated segmentation of gross tumor volumes (GTVp) and lymph nodes (GTVn) in head and neck cancer using MRI presents a critical challenge with significant potential to enhance radiation oncology workflows. In this study, we developed a deep learning pipeline based on the SegResNet architecture, integrated into the Auto3DSeg framework, to achieve fully-automated segmentation on pre-treatment (pre-RT) and mid-treatment (mid-RT) MRI scans as part of the DLaBella29 team submission to the HNTS-MRG 2024 challenge. For Task 1, we used an ensemble of six SegResNet models with predictions fused via weighted majority voting. The models were pre-trained on both pre-RT and mid-RT image-mask pairs, then fine-tuned on pre-RT data, without any pre-processing. For Task 2, an ensemble of five SegResNet models was employed, with predictions fused using majority voting. Pre-processing for Task 2 involved setting all voxels more than 1 cm from the registered pre-RT masks to background (value 0), followed by applying a bounding box to the image. Post-processing for both tasks included removing tumor predictions smaller than 175-200 mm3 and node predictions under 50-60 mm3. Our models achieved testing DSCagg scores of 0.72 and 0.82 for GTVn and GTVp in Task 1 (pre-RT MRI) and testing DSCagg scores of 0.81 and 0.49 for GTVn and GTVp in Task 2 (mid-RT MRI). This study underscores the feasibility and promise of deep learning-based auto-segmentation for improving clinical workflows in radiation oncology, particularly in adaptive radiotherapy. Future efforts will focus on refining mid-RT segmentation performance and further investigating the clinical implications of automated tumor delineation.

PMID:40201772 | PMC:PMC11978229 | DOI:10.1007/978-3-031-83274-1_21

Head Neck Tumor Segm MR Guid Appl (2024). 2025;15273:36-49. doi: 10.1007/978-3-031-83274-1_2. Epub 2025 Mar 3.

ABSTRACT

Radiation therapy (RT) is a vital part of treatment for head and neck cancer, where accurate segmentation of gross tumor volume (GTV) is essential for effective treatment planning. This study investigates the use of pre-RT tumor regions and local gradient maps to enhance mid-RT tumor segmentation for head and neck cancer in MRI-guided adaptive radiotherapy. By leveraging pre-RT images and their segmentations as prior knowledge, we address the challenge of tumor localization in mid-RT segmentation. A gradient map of the tumor region from the pre-RT image is computed and applied to mid-RT images to improve tumor boundary delineation. Our approach demonstrated improved segmentation accuracy for both primary GTV (GTVp) and nodal GTV (GTVn), though performance was limited by data constraints. The final DSC agg scores from the challenge's test set evaluation were 0.534 for GTVp, 0.867 for GTVn, and a mean score of 0.70. This method shows potential for enhancing segmentation and treatment planning in adaptive radiotherapy. Team: DCPT-Stine's group.

PMID:40201771 | PMC:PMC11977786 | DOI:10.1007/978-3-031-83274-1_2

BJR Artif Intell. 2024 Nov 13;1(1):ubae016. doi: 10.1093/bjrai/ubae016. eCollection 2024 Jan.

ABSTRACT

Breast cancer is one of the most common and deadly cancers in women. Triple-negative breast cancer (TNBC) accounts for approximately 10%-15% of breast cancer diagnoses and is an aggressive molecular breast cancer subtype associated with important challenges in its diagnosis, treatment, and prognostication. This poses an urgent need for developing more effective and personalized imaging biomarkers for TNBC. Towards this direction, artificial intelligence (AI) for radiologic imaging holds a prominent role, leveraging unique advantages of radiologic breast images, being used routinely for TNBC diagnosis, staging, and treatment planning, and offering high-resolution whole-tumour visualization, combined with the immense potential of AI to elucidate anatomical and functional properties of tumours that may not be easily perceived by the human eye. In this review, we synthesize the current state-of-the-art radiologic imaging applications of AI in assisting TNBC diagnosis, treatment, and prognostication. Our goal is to provide a comprehensive overview of radiomic and deep learning-based AI developments and their impact on advancing TNBC management over the last decade (2013-2024). For completeness of the review, we start with a brief introduction of AI, radiomics, and deep learning. Next, we focus on clinically relevant AI-based diagnostic, predictive, and prognostic models for radiologic breast images evaluated in TNBC. We conclude with opportunities and future directions for AI towards advancing diagnosis, treatment response predictions, and prognostic evaluations for TNBC.

PMID:40201726 | PMC:PMC11974408 | DOI:10.1093/bjrai/ubae016

Data Brief. 2025 Mar 3;59:111434. doi: 10.1016/j.dib.2025.111434. eCollection 2025 Apr.

ABSTRACT

The development of advanced coffee bean classification techniques depends on the availability of high quality datasets. Coffee bean quality is influenced by various factors, including bean size, shape, colour, and defects such as fungal damage, full black, full sour, broken beans, and insect damage. Constructing an accurate and reliable ground truth dataset for coffee bean classification is a challenging and labour intensive process. To address this need, we introduce the Coffee Beans Dataset (CBD) which contains 450 high-resolution images sampled across 9 distinct coffee bean grades A, AA, AAA, AB, C, PB-I, PB-II, BITS and BULK with 50 images per class. These samples were sourced from Wayanad, Kerala, reflecting the region's diverse coffee bean quality .This dataset is specifically designed to support machine learning and deep learning models for coffee bean classification and grading. By providing a comprehensive and diverse dataset, we aim to address key challenges in coffee quality assessment and improvement in classification accuracy. When tested using the EfficientNet-B0 model, the model achieved a high accuracy of 100%, demonstrating its potential to enhance automated coffee bean grading systems. The CBD serves as a valuable resource for researchers and industry professionals, promot-ing innovation in coffee quality monitoring and classification algorithms.

PMID:40201542 | PMC:PMC11978365 | DOI:10.1016/j.dib.2025.111434

Front Radiol. 2025 Mar 25;5:1503625. doi: 10.3389/fradi.2025.1503625. eCollection 2025.

ABSTRACT

INTRODUCTION: Lumbar spine magnetic resonance imaging (MRI) plays a critical role in diagnosing and planning treatment for spinal conditions such as degenerative disc disease, spinal canal stenosis, and disc herniation. Measuring the cross-sectional area of the dural sac (DSCA) is a key factor in evaluating the severity of spinal canal narrowing. Traditionally, radiologists perform this measurement manually, which is both time-consuming and susceptible to errors. Advances in deep learning, particularly convolutional neural networks (CNNs) like the U-Net architecture, have demonstrated significant potential in the analysis of medical images. This study evaluates the efficacy of deep learning models for automating DSCA measurements in lumbar spine MRIs to enhance diagnostic precision and alleviate the workload of radiologists.

METHODS: For algorithm development and assessment, we utilized two extensive, anonymized online datasets: the "Lumbar Spine MRI Dataset" and the SPIDER-MRI dataset. The combined dataset comprised 683 lumbar spine MRI scans for training and testing, with an additional 50 scans reserved for external validation. We implemented and assessed three deep learning models-U-Net, Attention U-Net, and MultiResUNet-using 5-fold cross-validation. The models were trained on T1-weighted axial MRI images and evaluated on metrics such as accuracy, precision, recall, F1-score, and mean absolute error (MAE).

RESULTS: All models exhibited a high correlation between predicted and actual DSCA values. The MultiResUNet model achieved superior results, with a Pearson correlation coefficient of 0.9917 and an MAE of 23.7032 mm2 on the primary dataset. This high precision and reliability were consistent in external validation, where the MultiResUNet model attained an accuracy of 99.95%, a recall of 0.9989, and an F1-score of 0.9393. Bland-Altman analysis revealed that most discrepancies between predicted and actual DSCA values fell within the limits of agreement, further affirming the robustness of these models.

DISCUSSION: This study demonstrates that deep learning models, particularly MultiResUNet, offer high accuracy and reliability in the automated segmentation and calculation of DSCA in lumbar spine MRIs. These models hold significant potential for improving diagnostic accuracy and reducing the workload of radiologists. Despite some limitations, such as the restricted dataset size and reliance on T1-weighted images, this study provides valuable insights into the application of deep learning in medical imaging. Future research should include larger, more diverse datasets and additional image weightings to further validate and enhance the generalizability and clinical utility of these models.

PMID:40201339 | PMC:PMC11975661 | DOI:10.3389/fradi.2025.1503625

Front Med (Lausanne). 2025 Mar 25;12:1563153. doi: 10.3389/fmed.2025.1563153. eCollection 2025.

ABSTRACT

OBJECTIVE: Establish a deep learning ultrasound radiomics model based on destruction-replenishment contrast-enhanced ultrasound (DR-CEUS) for the early prediction of acute kidney injury (SA-AKI).

METHOD: This paper proposes a deep learning ultrasound radiomics model (DLUR). Deep learning models were separately established using ResNet18, ResNet50, ResNext18, and ResNext50 networks. Based on the features extracted from the fully connected layers of the optimal model, a deep learning ultrasound radiomics model (DLUR) was established using three classification models (built with 3 classifiers). The predictive performance of the best DLUR model was compared with the visual assessments of two groups of ultrasound physicians with varying levels of experience. The performance of each model and the ultrasound physicians was evaluated by assessing the receiver operating characteristic (ROC) curves. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were subsequently calculated.

RESULTS: Compared to the ResNet18 model, the DLUR model based on logistic regression (DLUR-LR) demonstrated the best predictive performance, showing a Net Reclassification Improvement (NRI) value of 0.210 (p < 0.05). The Integrated Discrimination Improvement (IDI) value for the corresponding stage was 0.169 (p < 0.05). Additionally, the performance of the DLUR-LR model also surpassed that of senior ultrasound physicians (AUC, 0.921 vs. 0.829, p < 0.05).

CONCLUSION: By combining deep learning and ultrasound radiomics, a deep learning ultrasound radiomics model with outstanding predictive efficiency and robustness has demonstrated excellent capability in the early prediction of acute kidney injury (SA-AKI).

PMID:40201329 | PMC:PMC11975892 | DOI:10.3389/fmed.2025.1563153

Front Med (Lausanne). 2025 Mar 25;12:1555676. doi: 10.3389/fmed.2025.1555676. eCollection 2025.

ABSTRACT

BACKGROUND: Thyroid disease continues to be one of the most prevalent disease groups worldwide, with its frequency and distribution being impacted by numerous factors. Significant progress has been achieved in recent years in thyroid nodules, largely due to the advent of novel detection and diagnostic techniques. This study aims to scrutinize the top 100 most frequently cited articles in thyroid nodule research, utilizing bibliometric analysis to identify trends, highlight critical focal points, and lay a groundwork for forthcoming investigations.

METHODS: A comprehensive literature search was carried out using the SCI-E database, and all the recorded results were downloaded in plain text format for detailed analysis. The key terms analyzed with VOSviewer 1.6.18, CiteSpace 6.3r1, bibliometrix in R Studio (v.4.4.1), and Microsoft Excel 2021 software include country, institution, author, journal, and keywords.

RESULTS: The publication timeframe extends from 1 January 2003 to 31 December 2021, reaching a peak citation count of 9,100. Notably, the United States leads in the number of published articles, with Harvard University standing out as a prestigious institution. These articles were featured in 45 diverse journals, with THYROID leading in publication volume. Nikiforov Yuri E. was the most prolific first author, appearing 10 times. Keyword analysis highlighted traditional research themes such as "fine needle aspiration," "carcinogens," and "management." However, "deep learning" has surfaced as a significant area of focus in recent studies.

CONCLUSION: This study has extracted the bibliometric characteristics of the top 100 most-cited articles pertaining to TNs, providing an invaluable reference for upcoming studies. Through meticulous analysis, it has been determined that the primary research concentrations encompass the diagnosis of benign or malignant TNs, the management of TNs, and the subsequent monitoring of TNs, with deep learning emerging as a pivotal area of exploration.

PMID:40201321 | PMC:PMC11975563 | DOI:10.3389/fmed.2025.1555676

J Neurotrauma. 2025 Apr 9. doi: 10.1089/neu.2024.0301. Online ahead of print.

ABSTRACT

An extensive library of symptom inventories has been developed over time to measure clinical symptoms of traumatic brain injury (TBI), but this variety has led to several long-standing issues. Most notably, results drawn from different settings and studies are not comparable. This creates a fundamental problem in TBI diagnostics and outcome prediction, namely that it is not possible to equate results drawn from distinct tools and symptom inventories. Here, we present an approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories by ranking item text similarities according to their conceptual likeness. We tested the ability of four pretrained deep learning models to screen thousands of symptom description pairs for related content-a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. Correlation and factor analysis found the properties of the scales were broadly preserved under conversion. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding broad gains for the harmonization of TBI assessment.

PMID:40200899 | DOI:10.1089/neu.2024.0301

Heliyon. 2025 Mar 14;11(6):e42984. doi: 10.1016/j.heliyon.2025.e42984. eCollection 2025 Mar 20.

ABSTRACT

[This corrects the article DOI: 10.1016/j.heliyon.2023.e21538.].

PMID:40201525 | PMC:PMC11948527 | DOI:10.1016/j.heliyon.2025.e42984