Orbit. 2025 Jun 3:1-9. doi: 10.1080/01676830.2025.2510587. Online ahead of print.

ABSTRACT

PURPOSE: To develop a deep learning model using orbital computed tomography (CT) imaging to accurately distinguish thyroid eye disease (TED) and orbital myositis, two conditions with overlapping clinical presentations.

METHODS: Retrospective, single-center cohort study spanning 12 years including normal controls, TED, and orbital myositis patients with orbital imaging and examination by an oculoplastic surgeon. A deep learning model employing a Visual Geometry Group-16 network was trained on various binary combinations of TED, orbital myositis, and controls using single slices of coronal orbital CT images.

RESULTS: A total of 1628 images from 192 patients (110 TED, 51 orbital myositis, 31 controls) were included. The primary model comparing orbital myositis and TED had accuracy of 98.4% and area under the receiver operating characteristic curve (AUC) of 0.999. In detecting orbital myositis, it had a sensitivity, specificity, and F1 score of 0.964, 0.994, and 0.984, respectively.

CONCLUSIONS: Deep learning models can differentiate TED and orbital myositis based on a single, coronal orbital CT image with high accuracy. Their ability to distinguish these conditions based not only on extraocular muscle enlargement but also other salient features suggests potential applications in diagnostics and treatment beyond these conditions.

PMID:40459922 | DOI:10.1080/01676830.2025.2510587

Bioinformatics. 2025 Jun 3:btaf331. doi: 10.1093/bioinformatics/btaf331. Online ahead of print.

ABSTRACT

MOTIVATION: Pinpointing the subcellular location of proteins is essential for studying protein function and related diseases. Advances in spatial proteomics have shown that automatic recognition of protein subcellular localization from images could highly facilitate protein translocation analysis and biomarker discovery, but existing machine learning works have been mostly limited to processing 2D images. By contrast, 3D images have higher spatial resolution and allow researchers to observe cellular structures in their natural context, but currently there are only a few studies of 3D image processing for protein distribution analysis due to the lack of data and complexity of modeling.

RESULTS: We develop a knowledge-enhanced protein subcellular localization model, KE3DLoc, which could recognize distribution patterns in 3D fluorescence microscope images using deep learning methods. The model designs an image feature extraction module that incorporates information from 3D and 2D projected cells, and implements an asymmetric loss and confidence weights to a data imbalance and weak cell annotation issues. Besides, considering that the biological knowledge in the Gene Ontology (GO) database can provide valuable support for protein location understanding, the KE3DLoc model incorporates a novel knowledge enhancement module that optimizes the protein representation by related knowledge graphs derived from the GO. Since the image module and the knowledge module calculate features from different levels, KE3DLoc designs protein ID aggregation to enhance the consistency of protein features across different cells. Experimental results on three public datasets have demonstrated that the KE3DLoc significantly outperforms existing methods and provides valuable insights for spatial proteomics research.

AVAILABILITY: All datasets and codes used in this study are available at GitHub: https://github.com/PRBioimages/KE3DLoc.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40459878 | DOI:10.1093/bioinformatics/btaf331

Eur Radiol. 2025 Jun 3. doi: 10.1007/s00330-025-11715-w. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate the effect of contrast enhancement on the diagnosis of interstitial lung abnormalities (ILA) in automatic quantitative CT measurement in patients with paired pre- and post-contrast scans.

MATERIALS AND METHODS: Patients who underwent chest CT for thoracic surgery between April 2017 and December 2020 were retrospectively analyzed. ILA quantification was performed using deep learning-based automated software. Cases were categorized as ILA or non-ILA according to the Fleischner Society's definition, based on the quantification results or radiologist assessment (reference standard). Measurement variability, agreement, and diagnostic performance between the pre- and post-contrast scans were evaluated.

RESULTS: In 1134 included patients, post-contrast scans quantified a slightly larger volume of nonfibrotic ILA (mean difference: -0.2%), due to increased ground-glass opacity and reticulation volumes (-0.2% and -0.1%), whereas the fibrotic ILA volume remained unchanged (0.0%). ILA was diagnosed in 15 (1.3%), 22 (1.9%), and 40 (3.5%) patients by pre- and post-contrast scans and radiologists, respectively. The agreement between the pre- and post-contrast scans was substantial (κ = 0.75), but both pre-contrast (κ = 0.46) and post-contrast (κ = 0.54) scans demonstrated moderate agreement with the radiologist. The sensitivity for ILA (32.5% vs. 42.5%, p = 0.221) and specificity for non-ILA (99.8% vs. 99.5%, p = 0.248) were comparable between pre- and post-contrast scans. Radiologist's reclassification for equivocal ILA due to unilateral abnormalities increased the sensitivity for ILA (67.5% and 75.0%, respectively) in both pre- and post-contrast scans.

CONCLUSION: Applying automated quantification on post-contrast scans appears to be acceptable in terms of agreement and diagnostic performance; however, radiologists may need to improve sensitivity reclassifying equivocal ILA.

KEY POINTS: Question The effect of contrast enhancement on the automated quantification of interstitial lung abnormality (ILA) remains unknown. Findings Automated quantification measured slightly larger ground-glass opacity and reticulation volumes on post-contrast scans than on pre-contrast scans; however, contrast enhancement did not affect the sensitivity for interstitial lung abnormality. Clinical relevance Applying automated quantification on post-contrast scans appears to be acceptable in terms of agreement and diagnostic performance.

PMID:40459739 | DOI:10.1007/s00330-025-11715-w

Eur Radiol. 2025 Jun 3. doi: 10.1007/s00330-025-11697-9. Online ahead of print.

ABSTRACT

OBJECTIVES: To develop and validate a deep-learning-based automatic method for vessel walls and atherosclerotic plaques segmentation for quantitative evaluation in MR vessel wall images.

MATERIALS AND METHODS: A total of 193 patients (107 patients for training and validation, 39 patients for internal test, 47 patients for external test) with atherosclerotic plaque from five centers underwent T1-weighted MRI scans and were included in the dataset. The first step of the proposed method was constructing a purely learning-based convolutional neural network (CNN) named Vessel-SegNet to segment the lumen and the vessel wall. The second step is using the vessel wall priors (including manual prior and Tversky-loss-based automatic prior) to improve the plaque segmentation, which utilizes the morphological similarity between the vessel wall and the plaque. The Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), etc., were used to evaluate the similarity, agreement, and correlations.

RESULTS: Most of the DSCs for lumen and vessel wall segmentation were above 90%. The introduction of vessel wall priors can increase the DSC for plaque segmentation by over 10%, reaching 88.45%. Compared to dice-loss-based vessel wall priors, the Tversky-loss-based priors can further improve DSC by nearly 3%, reaching 82.84%. Most of the ICC values between the Vessel-SegNet and manual methods in the 6 quantitative measurements are greater than 85% (p-value < 0.001).

CONCLUSION: The proposed CNN-based segmentation model can quickly and accurately segment vessel walls and plaques for quantitative evaluation. Due to the lack of testing with other equipment, populations, and anatomical studies, the reliability of the research results still requires further exploration.

KEY POINTS: Question How can the accuracy and efficiency of vessel component segmentation for quantification, including the lumen, vessel wall, and plaque, be improved? Findings Improved CNN models, manual/automatic vessel wall priors, and Tversky loss can improve the performance of semi-automatic/automatic vessel components segmentation for quantification. Clinical relevance Manual segmentation of vessel components is a time-consuming yet important process. Rapid and accurate segmentation of the lumen, vessel walls, and plaques for quantification assessment helps patients obtain more accurate, efficient, and timely stroke risk assessments and clinical recommendations.

PMID:40459736 | DOI:10.1007/s00330-025-11697-9

Curr Allergy Asthma Rep. 2025 Jun 3;25(1):27. doi: 10.1007/s11882-025-01207-8.

ABSTRACT

PURPOSE OF REVIEW: This review explores the transformative potential of artificial intelligence (AI) and next-generation sequencing (NGS) in allergy diagnostics and treatment. It focuses on leveraging these technologies to enhance precision in biomarker discovery, patient stratification, and personalized management strategies for allergic diseases. RECENT FINDINGS: AI-driven algorithms, particularly machine learning and deep learning, have enabled the identification of complex molecular patterns and predictive markers in allergies, such as IgE levels and cytokine profiles. Integration with NGS techniques, including single-cell RNA sequencing, has uncovered unique immune response signatures, providing insights into molecular mechanisms driving allergic reactions. These innovations have advanced diagnostic accuracy, treatment personalization, and real-time monitoring capabilities, especially in allergen immunotherapy. Combining AI and NGS technologies represents a paradigm shift in allergy research and clinical practice. These advancements facilitate precision diagnostics and personalized treatments, ensuring safer and more effective interventions tailored to individual patient profiles. Despite data integration and clinical implementation challenges, these technologies promise improved outcomes and quality of life for allergy sufferers.

PMID:40459653 | DOI:10.1007/s11882-025-01207-8

Transl Vis Sci Technol. 2025 Jun 2;14(6):9. doi: 10.1167/tvst.14.6.9.

ABSTRACT

PURPOSE: To evaluate the agreement and correlation between manual and automated measurements of subfoveal choroidal thickness (SFCT) using enhanced depth imaging spectral-domain optical coherence tomography in an elderly population and to investigate the factors influencing measurement discrepancies.

METHODS: Based on the Beijing Eye Study, SFCT was measured manually using Heidelberg Eye Explorer software and automatically via a TransUNet-based deep learning model. Agreement between manual and automated SFCT measurements was assessed using Bland-Altman plots, intraclass correlation coefficients (ICC), and Pearson correlation coefficients.

RESULTS: Among 2896 participants, automated and manual measurements of SFCT demonstrated strong correlation (ICC = 0.971; 95% confidence interval [CI], 0.969-0.973; Pearson = 0.974, P < 0.001). Subgroup analyses showed similarly high correlation across participants aged ≥60 years (ICC = 0.954, Pearson = 0.974), aged <60 years (ICC = 0.971; Pearson = 0.953), with axial length ≥23 mm (ICC = 0.969; Pearson = 0.974), and axial length <23 mm (ICC = 0.959; Pearson = 0.963). Participants with SFCT <300 µm showed higher consistency (ICC = 0.942; Pearson = 0.944) compared to those with SFCT ≥300 µm (ICC = 0.867; Pearson = 0.868). Significant fixed and proportional biases were observed in all subgroups (P < 0.001), with manual measurements consistently lower than automated values.

CONCLUSIONS: Despite the presence of systematic biases, automated SFCT measurements showed excellent consistency and strong correlation with manual measurements across a large elderly population. These findings support the potential utility of AI-assisted SFCT measurement in clinical settings.

TRANSLATIONAL RELEVANCE: This study validates AI-based SFCT measurement in a large elderly cohort, enhancing diagnostic accuracy and bridging research with practice.

PMID:40459523 | DOI:10.1167/tvst.14.6.9

New Phytol. 2025 Jun 3. doi: 10.1111/nph.70250. Online ahead of print.

ABSTRACT

Podocarpus pollen morphology is shaped by both phylogenetic history and the environment. We analyzed the relationship between pollen traits quantified using deep learning and environmental factors within a comparative phylogenetic framework. We investigated the influence of mean annual temperature, annual precipitation, altitude, and solar radiation in driving morphological change. We used trait-environment regression models to infer the temperature tolerances of 31 Neotropical Podocarpidites fossils. Ancestral state reconstructions were applied to the Podocarpus phylogeny with and without the inclusion of fossils. Our results show that temperature and solar radiation influence pollen morphology, with thermal stress driving an increase in pollen size and higher ultraviolet B radiation selecting for thicker corpus walls. Fossil temperature tolerances inferred from trait-environment models aligned with paleotemperature estimates from global paleoclimate models. Incorporating fossils into ancestral state reconstructions revealed that early ancestral Podocarpus lineages were likely adapted to warm climates, with cool-temperature tolerance evolving independently in high-latitude and high-altitude species. Our results highlight the importance of deep learning-derived features in advancing our understanding of plant environmental adaptations over evolutionary timescales. Deep learning allows us to quantify subtle interspecific differences in pollen morphology and link these traits to environmental preferences through statistical and phylogenetic analyses.

PMID:40458972 | DOI:10.1111/nph.70250

Spine (Phila Pa 1976). 2025 Jun 3. doi: 10.1097/BRS.0000000000005414. Online ahead of print.

ABSTRACT

STUDY DESIGN: Retrospective study.

OBJECTIVE: To develop and validate a computed tomography-based deep learning(DL) model for diagnosing cervical spinal stenosis(CSS).

SUMMARY OF BACKGROUND DATA: Although magnetic resonance imaging (MRI) is widely used for diagnosing CSS, its inherent limitations, including prolonged scanning time, limited availability in resource-constrained settings, and contraindications for patients with metallic implants, make computed tomography (CT) a critical alternative in specific clinical scenarios. The development of CT-based DL models for CSS detection holds promise in transcending the diagnostic efficacy limitations of conventional CT imaging, thereby serving as an intelligent auxiliary tool to optimize healthcare resource allocation.

METHODS: Paired CT/MRI images were collected. CT images were divided into training, validation, and test sets in an 8:1:1 ratio. The two-stage model architecture employed: (1) a Faster R-CNN-based detection model for localization, annotation, and extraction of regions of interest (ROI); (2) comparison of 16 convolutional neural network (CNN) models for stenosis classification to select the best-performing model. The evaluation metrics included accuracy, F1-score, and Cohen's κ coefficient, with comparisons made against diagnostic results from physicians with varying years of experience.

RESULTS: In the multiclass classification task, four high-performing models (DL1-b0, DL2-121, DL3-101, and DL4-26d) achieved accuracies of 88.74%, 89.40%, 89.40%, and 88.08%, respectively. All models demonstrated >80% consistency with senior physicians and >70% consistency with junior physicians.In the binary classification task, the models achieved accuracies of 94.70%, 96.03%, 96.03%, and 94.70%, respectively. All four models demonstrated consistency rates slightly below 90% with junior physicians. However, when compared with senior physicians, three models (excluding DL4-26d) exhibited consistency rates exceeding 90%.

CONCLUSIONS: The DL model developed in this study demonstrated high accuracy in CT image analysis of CSS, with a diagnostic performance comparable to that of senior physicians.

PMID:40458958 | DOI:10.1097/BRS.0000000000005414

Mol Imaging Radionucl Ther. 2025 Jun 3;34(2):143-145. doi: 10.4274/mirt.galenos.2024.60320.

ABSTRACT

Integrins play crucial roles in the migration of tumor cells during angiogenesis and metastasis. Consequently, αvβ6-integrin-targeted positron emission tomography (PET) radiopharmaceuticals have been developed and tested in humans, with clinical trials highlighting their applications in idiopathic pulmonary fibrosis and carcinomas. However, data on integrins are limited, and the role of [68Ga]Ga-Trivehexin tomography/computed tomography (CT) PET/CT is not well-established. Some studies have suggested that [68Ga]Ga-Trivehexin PET/CT is more specific than 18F-fluorodeoxyglucose (18F-FDG) PET/CT, which can yield false-positive results. It has been shown to be more efficient in evaluating pancreatic lesions and head and neck tumors. The role of [68Ga]Ga Trivehexin PET/CT in neuroendocrine tumors is not yet clearly defined. In our case, integrin was negative in the pancreatic neuroendocrine tumor but positive in the breast lobular tumor. Additionally, we observed that the lobular carcinoma lesion in the right breast is somatostatin receptor+positive on [68Ga]Ga-DOTATATE PET/CT.

PMID:40458979 | DOI:10.4274/mirt.galenos.2024.60320

mBio. 2025 Jun 3:e0087225. doi: 10.1128/mbio.00872-25. Online ahead of print.

ABSTRACT

Proteases regulate important biological functions. Here, we present the structural and functional characterization of three previously uncharacterized aspartic proteases in Pseudomonas aeruginosa. We show that these proteases have structural hallmarks of retropepsin peptidases and play redundant roles for cell survival under hypoosmotic stress conditions. Consequently, we named them retropepsin-like osmotic stress tolerance peptidases (Rlo). Our research shows that while Rlo proteases are homologous to RimB, an aspartic peptidase involved in rhizosphere colonization and plant infection, they contain N-terminal signal peptides and perform distinct biological functions. Mutants lacking all three secreted Rlo peptidases show defects in antibiotic resistance, biofilm formation, and cell morphology. These defects are rescued by mutations in the inactive transglutaminase transmembrane protein RloB and the cytoplasmic ATP-grasp protein RloC, two previously uncharacterized genes in the same operon as one of the Rlo proteases. These studies identify Rlo proteases and rlo operon products as critical factors in clinically relevant processes, making them appealing targets for therapeutic strategies against Pseudomonas infections.IMPORTANCEBacterial infections have become harder to treat due to the ability of pathogens to adapt to different environments and the rise of antimicrobial resistance. This has led to longer illnesses, increased medical costs, and higher mortality rates. The opportunistic pathogen Pseudomonas aeruginosa is particularly problematic because of its inherent resistance to many antibiotics and its capacity to form biofilms, structures that allow bacteria to withstand hostile conditions. Our study uncovers a new class of retropepsin-like proteases in P. aeruginosa that are required for biofilm formation and bacterial survival under stress conditions, including antibiotic exposure. By identifying critical factors that determine bacterial fitness and adaptability, our research lays the foundation for developing new therapeutic strategies against bacterial infections.

PMID:40459290 | DOI:10.1128/mbio.00872-25

Elife. 2025 Jun 3;13:RP100071. doi: 10.7554/eLife.100071.

ABSTRACT

Targeted monotherapies for cancer often fail due to inherent or acquired drug resistance. By aiming at multiple targets simultaneously, drug combinations can produce synergistic interactions that increase drug effectiveness and reduce resistance. Computational models based on the integration of omics data have been used to identify synergistic combinations, but predicting drug synergy remains a challenge. Here, we introduce Drug synergy Interaction Prediction (DIPx), an algorithm for personalized prediction of drug synergy based on biologically motivated tumor- and drug-specific pathway activation scores (PASs). We trained and validated DIPx in the AstraZeneca-Sanger (AZS) DREAM Challenge human cell-line dataset using two separate test sets: Test Set 1 comprised the combinations already present in the training set, while Test Set 2 contained combinations absent from the training set, thus indicating the model's ability to handle novel combinations. The Spearman's correlation coefficients between predicted and observed drug synergy were 0.50 (95% CI: 0.47-0.53) in Test Set 1 and 0.26 (95% CI: 0.22-0.30) in Test Set 2, compared to 0.38 (95% CI: 0.34-0.42) and 0.18 (95% CI: 0.16-0.20), respectively, for the best performing method in the Challenge. We show evidence that higher synergy is associated with higher functional interaction between the drug targets, and this functional interaction information is captured by PAS. We illustrate the use of PAS to provide a potential biological explanation in terms of activated pathways that mediate the synergistic effects of combined drugs. In summary, DIPx can be a useful tool for personalized prediction of drug synergy and exploration of activated pathways related to the effects of combined drugs.

PMID:40459126 | DOI:10.7554/eLife.100071

New Phytol. 2025 Jun 3. doi: 10.1111/nph.70227. Online ahead of print.

ABSTRACT

Plant host cell-cycle hyperactivation is essential for nematode feeding site (NFS) ontogenesis, but the balanced mitotic and endoreplication cycles must occur for homeostasis. Alterations in core cell cycle gene expression are well known to disturb root-knot and cyst-NFS development. Herein, our investigation focused on the activity of E2FA and E2FB transcription factors in root-knot nematode-induced galls in Arabidopsis thaliana controlling both the mitotic and endocycles through the activation of S-phase cell cycle genes. The roles of the two plant E2F activators during cell cycle progression in galls were compared with syncytia induced by cyst nematodes. E2FA and E2FB transcripts were highly expressed in both galls and syncytia. Loss-of-function analysis revealed that the absence of E2FA and E2FB impaired feeding-site development, resulting in significantly reduced gall development and nematode reproduction. Transcript analysis of galls upon E2FA and E2FB loss-of-function compared with that of wild-type revealed differential expression of selected target genes operating during S phase. Although our results imply the functional interplay of E2FA and E2FB for gall development, we recognize that E2FA alone commands and sustains cell division as well as the endocycle in galls and syncytia, whereas E2FB is likely partaking in nematode-induced gall initiation.

PMID:40459000 | DOI:10.1111/nph.70227

Comput Struct Biotechnol J. 2025 May 1;27:1784-1799. doi: 10.1016/j.csbj.2025.04.042. eCollection 2025.

ABSTRACT

Epstein-Barr virus (EBV) is linked to various cancers like gastric carcinoma, nasopharyngeal carcinoma, and Burkitt's lymphoma, leading to around 200,000 deaths annually. Despite efforts, FDA-approved drugs to combat EBV infection are lacking. In this endeavor, we have developed an AI/ML based predictive algorithm "Anti-EBV" to find potential antivirals against EBV. We utilized small molecules from the ChEMBL database, which were experimentally tested for antiviral activity against EBV in lytic phase, in terms of IC50 /EC50 values. 17,968 molecular fingerprints and descriptors were computed for each molecule. Further, the best-performing 150 descriptors were used in the predictive model development. The molecules were then split into training/testing (T315) and independent validation (V35) datasets, followed by 10-fold cross validation to develop robust models. Various machine-learning techniques (MLTs) namely SVM, KNN, ANN, DNN, RF and XGBoost were used for predictive models development. SVM model achieved the best performance with Pearson's correlation coefficient (PCC) of 0.91 on T315 dataset and 0.95 on V35 dataset, respectively. These models were found to be robust by applicability domain, decoy dataset and chemical clustering analyses. The top-performing model was used to screen approved drugs from DrugBank, identifying potential repurposed drugs namely arzoxifene, succimer, abemaciclib and many more. To further validate these findings, top compounds were docked against key lytic proteins BZLF1 and BHRF1, demonstrating strong binding affinities for compounds like fluspirilene and suvorexant. This model is accessible as the "Anti-EBV" web server http://bioinfo.imtech.res.in/manojk/antiebv/ for antiviral prediction, making it the first AI/ML-based study for antiviral identification against EBV in lytic phase.

PMID:40458637 | PMC:PMC12127599 | DOI:10.1016/j.csbj.2025.04.042

J Antimicrob Chemother. 2025 Jun 3:dkaf173. doi: 10.1093/jac/dkaf173. Online ahead of print.

NO ABSTRACT

PMID:40458036 | DOI:10.1093/jac/dkaf173

World Allergy Organ J. 2025 May 5;18(5):101056. doi: 10.1016/j.waojou.2025.101056. eCollection 2025 May.

ABSTRACT

Asthma and obesity are both chronic diseases. Obesity is a common comorbidity and a risk factor of severe asthma, associated with increased asthma exacerbation risk, poorer asthma control and reduced quality of life. However, the responsible mechanisms are poorly understood. The aim of this study was to detect parameters associated with obesity in patients with severe asthma in order to check different pattern of inflammation in obese asthmatics. Baseline data from the Severe Asthma Network in Italy (SANI) registry were analysed in 1922 patients with severe asthma. Demographic, clinical and functional features were compared, according to body mass index (BMI). The prevalence of overweight and obesity among severe asthma patients was 34,8 and 20,3, respectively. Females were more prevalent in the obese cluster (p < 0.001). Asthma onset age in overweight and obese patients was higher than in normal population (p < 0.001). Obese subjects reported less frequently chronic rhinosinusitis with nasal polyposis (CRSwNP) and more frequently impaired sleep quality, cardiovascular disease, and type-2 diabetes (p < 0.001). Severe asthma patients with obesity had lower predicted FVC values (89.0 ± 19.2 vs 93.5 ± 20.2; p 0.002) and higher FEV1/FVC ratio (69.9 ± 11.5 vs 66.9 ± 12.4; p < 0.001) than patients without obesity. Obese asthmatics had lower blood eosinophilic count, and fractional exhaled nitric oxide (FeNO) levels than non-obese asthmatics. Asthma control test (ACT) was significantly poorer in obese patients (17, IQR 12-21) than other subgroups. Regarding treatment, overweight and obese patients were more likely to receive a GINA-Step 5 therapy (p 0.023), with more than 20 of obese asthmatics having frequent exacerbations requiring oral corticosteroid (OCS). Patients with severe asthma and obesity presented different characteristics that support the existence of distinct asthma phenotype in obese patients.

TRIAL REGISTRATION: Trial registry: ClinicalTrials.gov. ID: NCT06625216. Retrospectively registered October 3, 2024.

PMID:40458738 | PMC:PMC12127536 | DOI:10.1016/j.waojou.2025.101056

Clin Pharmacol Ther. 2025 Jun 2. doi: 10.1002/cpt.3733. Online ahead of print.

ABSTRACT

The Joint Heads of Medicines Agencies and European Medicines Agency's (HMA/EMA) big data initiative paves the way for better integration of real-world data, including data from patient registries, into regulatory decisions on medicines. This article focuses on the outcome of a two-day multistakeholder workshop organized by EMA in 2024, which explored ways to optimize the EMA qualification procedure for patient registries, and to establish the value and enable the use of these data across the full spectrum of research questions. Key recommendations include the need to clarify the aim, scope, and added value of the qualification of registries, coupled with a review of the procedural steps to ensure the process is fit-for-purpose to evaluate the use of registries in specific regulatory contexts. Further recommendations focused on strengthening interactions between stakeholders, as well as providing them with enhanced support by increasing awareness of publicly available tools that could leverage the potential of registry data, together with existing guidance. The European Medicines Regulatory Network is now working together with all relevant stakeholders, including the EMA scientific committees and working parties, the Joint HMA/EMA Network Data Steering Group and existing focus groups with external partners, to implement concrete actions that will address these recommendations. Among others, the update of existing guidance, the development of templates and Questions & Answers documents, and the design of appropriate communication and stakeholder engagement plans will aid in achieving the common goal of making optimal use of patient registry data to support public health in the European Union.

PMID:40457718 | DOI:10.1002/cpt.3733

Head Neck. 2025 Jun 3. doi: 10.1002/hed.28200. Online ahead of print.

ABSTRACT

BACKGROUND: Evaluating the minimum distance (dTICA) between the internal carotid artery (ICA) and tonsillar tumors (TT) on imaging is essential for preoperative planning; we propose a tool to automatically extract dTICA.

METHODS: CT scans of 96 patients with TT were selected from the cancer imaging archive. nnU-Net, a deep learning framework, was implemented to automatically segment both the TT and ICA from these scans. Dice similarity coefficient (DSC) and average hausdorff distance (AHD) were used to evaluate the performance of the nnU-Net. Thereafter, an automated tool was built to calculate the magnitude of dTICA from these segmentations.

RESULTS: The average DSC and AHD were 0.67, 2.44 mm, and 0.83, 0.49 mm for the TT and ICA, respectively. The mean dTICA was 6.66 mm and statistically varied by tumor T stage (p = 0.00456).

CONCLUSION: The proposed pipeline can accurately and automatically capture dTICA, potentially assisting clinicians in preoperative evaluation.

PMID:40458868 | DOI:10.1002/hed.28200

Neuroradiol J. 2025 Jun 3:19714009251346477. doi: 10.1177/19714009251346477. Online ahead of print.

ABSTRACT

The objective of this study was to assess the performance of an artificial intelligence (AI) algorithm in detecting intracranial haemorrhages (ICHs) on non-contrast CT scans (NCCT). Another objective was to gauge the department's acceptance of said algorithm. Surveys conducted at three and nine months post-implementation revealed an increase in radiologists' acceptance of the AI tool with an increasing performance. However, a significant portion still preferred an additional physician given comparable cost. Our findings emphasize the importance of careful software implementation into a robust IT architecture.

PMID:40458857 | DOI:10.1177/19714009251346477

Ophthalmol Sci. 2025 Mar 27;5(5):100775. doi: 10.1016/j.xops.2025.100775. eCollection 2025 Sep-Oct.

ABSTRACT

OBJECTIVE: To develop an attention-based deep learning (DL) model based on eye movements acquired during a simple visual fixation task to detect amblyopic subjects across different types and severity from controls.

DESIGN: An observational study.

SUBJECTS: We recruited 40 controls and 95 amblyopic subjects (anisometropic = 32; strabismic = 29; and mixed = 34) at the Cleveland Clinic from 2020 to 2024.

METHODS: Binocular horizontal and vertical eye positions were recorded using infrared video-oculography during binocular and monocular viewing. Amblyopic subjects were classified as those without nystagmus (n = 42) and those with nystagmus with fusion maldevelopment nystagmus (FMN) or nystagmus that did not meet the criteria of FMN or infantile nystagmus syndrome (n = 53). A multihead attention-based transformer encoder model was trained and cross-validated on deblinked and denoised eye position data acquired during fixation.

MAIN OUTCOME MEASURES: Detection of amblyopia across types (anisometropia, strabismus, or mixed) and severity (treated, mild, moderate, or severe) and subjects with and without nystagmus was evaluated with area under the receiver-operator characteristic curves, area under the precision-recall curve (AUPRC), and accuracy.

RESULTS: Area under the receiver-operator characteristic curves for classification of subjects per type were 0.70 ± 0.16 for anisometropia (AUPRC: 0.72 ± 0.08), 0.78 ± 0.15 for strabismus (AUPRC: 0.81 ± 0.16), and 0.80 ± 0.13 for mixed (AUPRC: 0.82 ± 0.15). Area under the receiver-operator characteristic curves for classification of amblyopia subjects per severity were 0.77 ± 0.12 for treated/mild (AUPRC: 0.76 ± 0.18), and 0.78 ± 0.09 for moderate/severe (AUPRC: 0.79 ± 0.16). Th area under the receiver-operator characteristic curve for classification of subjects with nystagmus was 0.83 ± 0.11 (AUPRC: 0.81 ± 0.18), and the area under the receiver-operator characteristic curve for those without nystagmus was 0.75 ± 0.15 (AUPRC: 0.76 ± 0.09).

CONCLUSIONS: The multihead transformer DL model classified amblyopia subjects regardless of the type, severity, and presence of nystagmus. The model's ability to identify amblyopia using eye movements alone demonstrates the feasibility of using eye-tracking data in clinical settings to perform objective classifications and complement traditional amblyopia evaluations.

FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

PMID:40458668 | PMC:PMC12127649 | DOI:10.1016/j.xops.2025.100775

Front Med (Lausanne). 2025 May 19;12:1555893. doi: 10.3389/fmed.2025.1555893. eCollection 2025.

ABSTRACT

Artificial intelligence (AI) has recently garnered significant public attention. Among the various fields where AI can be applied, medicine stands out as one with immense potential. In particular, AI is transforming precision oncology by providing innovative approaches to customize cancer treatments for individual patients. This article examines the latest developments in AI-powered tools designed to improve cancer diagnosis accuracy and predict treatment outcomes. The integration of AI into precision oncology is transforming cancer care by enabling more personalized and effective treatments, minimizing treatment-related toxicities, and enhancing patient survival rates. As AI advances, it will be pivotal in developing more targeted and successful cancer therapies. The field is still in its early stages, and future progress will benefit from establishing standards and guidelines to promote rigorous methodological design and uphold ethical principles. This research highlights the transformative potential of AI in addressing the challenges posed by cancer heterogeneity.

PMID:40458648 | PMC:PMC12127379 | DOI:10.3389/fmed.2025.1555893