J Microbiol Biotechnol. 2025 Mar 26;35:e2411010. doi: 10.4014/jmb.2411.11010.

ABSTRACT

Group A Streptococcus (GAS) is a major pathogen with diverse clinical outcomes linked to its genetic variability, making accurate phenotype prediction essential. While previous studies have identified many GAS-associated genetic factors, translating these findings into predictive models remains challenging due to data complexity. The current study aimed to integrate deep learning models with genome-wide association study-derived genetic variants to predict pathogenic phenotypes in GAS. We evaluated the performance of several deep neural network models, including CNN, ResNet18, LSTM, and their ensemble approach in predicting GAS phenotypes. It was found that the ensemble model consistently achieved the highest prediction accuracy across phenotypes. Models trained on the full 4722-genotype set outperformed those trained on a reduced 175-genotype set, underscoring the importance of comprehensive variant data in capturing complex genotype-phenotype interactions. Performance changes in the reduced 175-genotype set compared to the full-set genotype scenarios revealed the impact of data dimensionality on model effectiveness, with CNN remaining robust, while ResNet18 and LSTM underperformed. Our findings emphasized the potential of deep learning in phenotype prediction and the critical role of data-model compatibility.

PMID:40147921 | DOI:10.4014/jmb.2411.11010

Zhonghua Kou Qiang Yi Xue Za Zhi. 2025 Mar 28;60(4):326-331. doi: 10.3760/cma.j.cn112144-20250116-00021. Online ahead of print.

ABSTRACT

Artificial intelligence (AI) has significantly enhanced the precision and efficiency of dental restoration design, smile analysis, and personalized treatment in the field of esthetic dentistry through technologies such as deep learning. This advancement provides patients with more accurate and efficient esthetic treatment solutions. However, its application still faces challenges such as technical limitations, ethical concerns, and insufficient data diversity. In the future, with the integration of high-quality data, optimization of real-time learning technologies, and the advancement of multidisciplinary collaboration, AI is expected to further promote the intelligent and human-centered development of esthetic dentistry, bringing profound and positive impacts to patients and clinical practice.

PMID:40147889 | DOI:10.3760/cma.j.cn112144-20250116-00021

AJNR Am J Neuroradiol. 2025 Mar 27. doi: 10.3174/ajnr.A8567. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Deep learning (DL)-based reconstruction enables improving the quality of MR images acquired with a short scan time. We aimed to prospectively compare the image quality and diagnostic performance in evaluating cervical degenerative spine diseases and myelopathy between conventional cervical MRI and accelerated cervical MRI with a commercially available vendor-neutral DL-based reconstruction.

MATERIALS AND METHODS: Fifty patients with degenerative cervical spine disease or myelopathy underwent both conventional cervical MRI and accelerated cervical MRI by using a DL-based reconstruction operating within the DICOM domain. The images were evaluated both quantitatively, based on SNR and contrast-to-noise ratio (CNR), and qualitatively, by using a 5-point scoring system for the overall image quality and clarity of anatomic structures on sagittal T1WI, sagittal contrast-enhanced (CE) T1WI, and axial/sagittal T2WI. Four radiologists assessed the sensitivity and specificity of the 2 protocols for detecting degenerative diseases and myelopathy.

RESULTS: The DL-based protocol reduced MRI acquisition time by 47%-48% compared with the conventional protocol. DL-reconstructed images demonstrated a higher SNR on sagittal T1WI (P = .046) and a higher CNR on sagittal T2WI (P = .03) than conventional images. The SNR on sagittal T2WI and the CNR on sagittal T1WI did not significantly differ (P > .05). DL-reconstructed images had better overall image quality on sagittal T1WI (P < .001), sagittal T2WI (Dixon in-phase or TSE) (P < .001), and sagittal T2WI (Dixon water-only) (P = .013) and similar image quality on axial T2WI and sagittal CE T1WI (P > .05). DL-reconstructed images had better clarity of anatomic structures (P values were < .001 for all structures, except for the neural foramen [P = .024]). DL-reconstructed images had a higher sensitivity for detecting neural foraminal stenosis (P = .005) and similar sensitivities for diagnosing other degenerative spinal diseases and myelopathy (P > .05). The specificities for diagnosing degenerative spinal diseases and myelopathy did not differ between the 2 images (P > .05).

CONCLUSIONS: The accelerated cervical MRI reconstructed with a vendor-neutral DL-based reconstruction algorithm did not compromise image quality and had higher or similar diagnostic performance for diagnosing cervical degenerative spine diseases and myelopathy compared with the conventional protocol.

PMID:40147833 | DOI:10.3174/ajnr.A8567

Semin Cancer Biol. 2025 Mar 25:S1044-579X(25)00052-5. doi: 10.1016/j.semcancer.2025.03.004. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is recognized as one of the most lethal malignancies, characterized by late-stage diagnosis and limited therapeutic options. Risk stratification has traditionally been performed using epidemiological studies and genetic analyses, through which key risk factors, including smoking, diabetes, chronic pancreatitis, and inherited predispositions, have been identified. However, the multifactorial nature of PDAC has often been insufficiently addressed by these methods, leading to limited precision in individualized risk assessments. Advances in artificial intelligence (AI) have been proposed as a transformative approach, allowing the integration of diverse datasets-spanning genetic, clinical, lifestyle, and imaging data into dynamic models capable of uncovering novel interactions and risk profiles. In this review, the evolution of PDAC risk stratification is explored, with classical epidemiological frameworks compared to AI-driven methodologies. Genetic insights, including genome-wide association studies and polygenic risk scores, are discussed, alongside AI models such as machine learning, radiomics, and deep learning. Strengths and limitations of these approaches are evaluated, with challenges in clinical translation, such as data scarcity, model interpretability, and external validation, addressed. Finally, future directions are proposed for combining classical and AI-driven methodologies to develop scalable, personalized predictive tools for PDAC, with the goal of improving early detection and patient outcomes.

PMID:40147701 | DOI:10.1016/j.semcancer.2025.03.004

Neuroimage. 2025 Mar 25:121151. doi: 10.1016/j.neuroimage.2025.121151. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) is a primary degenerative brain disorder commonly found in the elderly, Mild cognitive impairment (MCI) can be considered a transitional stage from normal aging to Alzheimer's disease. Therefore, distinguishing between normal aging and disease-induced neurofunctional impairments is crucial in clinical treatment. Although deep learning methods have been widely applied in Alzheimer's diagnosis, the varying data formats used by different methods limited their clinical applicability. In this study, based on the ADNI dataset and previous clinical diagnostic experience, we propose a method using oblique coronal MRI to assist in diagnosis. We developed an algorithm to extract oblique coronal slices from 3D MRI data and used these slices to train classification networks. To achieve subject-wise classification based on 2D slices, rather than image-wise classification, we employed ensemble learning methods. This approach fused classification results from different modality images or different positions of the same modality images, constructing a more reliable ensemble classification model. The experiments introduced various decision fusion and feature fusion schemes, demonstrating the potential of oblique coronal MRI slices in assisting diagnosis. Notably, the weighted voting from decision fusion strategy trained on oblique coronal slices achieved accuracy rates of 97.5% for CN vs. AD, 100% for CN vs. MCI, and 94.83% for MCI vs. AD across the three classification tasks.

PMID:40147601 | DOI:10.1016/j.neuroimage.2025.121151

Cell. 2025 Mar 24:S0092-8674(25)00213-2. doi: 10.1016/j.cell.2025.02.025. Online ahead of print.

ABSTRACT

Efforts to use genome-wide assays or brain scans to diagnose autism have seen diminishing returns. Yet the clinical intuition of healthcare professionals, based on longstanding first-hand experience, remains the gold standard for diagnosis of autism. We leveraged deep learning to deconstruct and interrogate the logic of expert clinician intuition from clinical reports to inform our understanding of autism. After pre-training on hundreds of millions of general sentences, we finessed large language models (LLMs) on >4,000 free-form health records from healthcare professionals to distinguish confirmed versus suspected autism cases. By introducing an explainability strategy, our extended language model architecture could pin down the most salient single sentences in what drives clinical thinking toward correct diagnoses. Our framework flagged the most autism-critical DSM-5 criteria to be stereotyped repetitive behaviors, special interests, and perception-based behaviors, which challenges today's focus on deficits in social interplay, suggesting necessary revision of long-trusted diagnostic criteria in gold-standard instruments.

PMID:40147442 | DOI:10.1016/j.cell.2025.02.025

Comput Med Imaging Graph. 2025 Mar 23;123:102527. doi: 10.1016/j.compmedimag.2025.102527. Online ahead of print.

ABSTRACT

Medical image registration is a critical task in aligning medical images from different time points, modalities, or individuals, essential for accurate diagnosis and treatment planning. Despite significant progress in deep learning-based registration methods, current approaches still face considerable challenges, such as insufficient capture of local details, difficulty in effectively modeling global contextual information, and limited robustness in handling complex deformations. These limitations hinder the precision of high-resolution registration, particularly when dealing with medical images with intricate structures. To address these issues, this paper presents a novel registration network (TCDE-Net), an unsupervised medical image registration method based on a dual-encoder architecture. The dual encoders complement each other in feature extraction, enabling the model to effectively handle large-scale nonlinear deformations and capture intricate local details, thereby enhancing registration accuracy. Additionally, the detail-enhancement attention module aids in restoring fine-grained features, improving the network's capability to address complex deformations such as those at gray-white matter boundaries. Experimental results on the OASIS, IXI, and Hammers-n30r95 3D brain MR dataset demonstrate that this method outperforms commonly used registration techniques across multiple evaluation metrics, achieving superior performance and robustness. Our code is available at https://github.com/muzidongxue/TCDE-Net.

PMID:40147215 | DOI:10.1016/j.compmedimag.2025.102527

Comput Biol Med. 2025 Mar 26;190:110074. doi: 10.1016/j.compbiomed.2025.110074. Online ahead of print.

ABSTRACT

Numerical simulations of fluid dynamics in tube-like structures are important to biomedical research to model flow in blood vessels and airways. It is further useful to some clinical applications, such as predicting arterial fractional flow reserves, and assessing vascular flow wall shear stresses to predict atherosclerosis disease progression. Traditionally, they are conducted via computational fluid dynamics (CFD) simulations, which, despite optimization, still take substantial time, limiting clinical adoption. To improve efficiency, we investigate the use of the multi-case Neural Network (NN) to enable real-time predictions of fluid dynamics (both steady and pulsatile flows) in a 3D curved tube (with a narrowing in the middle mimicking a stenosis) of any shape within a geometric range, using only geometric parameters and boundary conditions. We compare the unsupervised approach guided by physics governing equations (physics informed neural network or PINN) to the supervised approach of using mass CFD simulations to train the network (supervised network or SN). We find that multi-case PINN can generate accurate velocity, pressure and wall shear stress (WSS) results under steady flow (spatially maximum error < 2-5 %), but this requires a specific enhancement strategies: (1) estimating the curvilinear coordinate parameters via a secondary NN to use as inputs into PINN, (2) imposing no-slip wall boundary condition as a hard constraint, and (3) advanced strategy to better spatially propagate effects of boundary conditions. However, we cannot achieve reasonable accuracy for pulsatile flow with PINN. Conversely, SN provides very accurate velocity, pressure, and WSS predictions under both steady and pulsatile flow scenarios (spatially and/or temporally maximum error averaged over all geometries <1 %), and is much less computationally expensive to train. To achieve this, strategies (1) and (2) above and a spectral encoding strategy for pulsatile flow are necessary. Thus, interestingly, the use of physics constraints is not effective in our application.

PMID:40147188 | DOI:10.1016/j.compbiomed.2025.110074

Med Ultrason. 2025 Mar 8. doi: 10.11152/mu-4495. Online ahead of print.

ABSTRACT

AIM: This study aimed to develop a deep learning (DL) model for automatic detection and diagnosis of gouty arthritis (GA) in the first metatarsophalangeal joint (MTPJ) using ultrasound (US) images.

MATERIALS AND METHODS: A retrospective study included individuals who underwent first MTPJ ultrasonography between February and July 2023. A five-fold cross-validation method (training set = 4:1) was employed. A deep residual convolutional neural network (CNN) was trained, and Gradient-weighted Class Activation Mapping (Grad-CAM) was used for visualization. Different ResNet18 models with varying residual blocks (2, 3, 4, 6) were compared to select the optimal model for image classification. Diagnostic decisions were based on a threshold proportion of abnormal images, determined from the training set.

RESULTS: A total of 2401 US images from 260 patients (149 gout, 111 control) were analyzed. The model with 3 residual blocks performed best, achieving an AUC of 0.904 (95% CI: 0.887~0.927). Visualization results aligned with radiologist opinions in 2000 images. The diagnostic model attained an accuracy of 91.1% (95% CI: 90.4%~91.8%) on the testing set, with a diagnostic threshold of 0.328.

CONCLUSION: The DL model demonstrated excellent performance in automatically detecting and diagnosing GA in the first MTPJ.

PMID:40146981 | DOI:10.11152/mu-4495

JMIR Diabetes. 2025 Mar 27;10:e66831. doi: 10.2196/66831.

ABSTRACT

Type 2 diabetes mellitus has seen a continuous rise in prevalence in recent years, and a similar trend has been observed in the increased availability of glucose-lowering drugs. There is a need to understand the variation in treatment response to these drugs to be able to predict people who will respond well or poorly to a drug. Electronic health records, clinical trials, and observational studies provide a huge amount of data to explore predictors of drug response. The use of artificial intelligence (AI), which includes machine learning and deep learning techniques, has the capacity to improve the prediction of treatment response in patients. AI can assist in the analysis of vast datasets to identify patterns and may provide valuable information on selecting an effective drug. Predicting an individual's response to a drug can aid in treatment selection, optimizing therapy, exploring new therapeutic options, and personalized medicine. This viewpoint highlights the growing evidence supporting the potential of AI-based methods to predict drug response with accuracy. Furthermore, the methods highlight a trend toward using ensemble methods as preferred models in drug response prediction studies.

PMID:40146874 | DOI:10.2196/66831

IEEE Trans Neural Netw Learn Syst. 2025 Mar 27;PP. doi: 10.1109/TNNLS.2025.3549816. Online ahead of print.

ABSTRACT

This article introduces the deep learning-based nonlinear model predictive controller with scene dynamics (DL-NMPC-SD) method for autonomous navigation. DL-NMPC-SD uses an a priori nominal vehicle model in combination with a scene dynamics model learned from temporal range sensing information. The scene dynamics model is responsible for estimating the desired vehicle trajectory, as well as to adjust the true system model used by the underlying model predictive controller. We propose to encode the scene dynamics model within the layers of a deep neural network, which acts as a nonlinear approximator for the high-order state space of the operating conditions. The model is learned based on temporal sequences of range-sensing observations and system states, both integrated by an Augmented Memory component. We use inverse reinforcement learning (IRL) and the Bellman optimality principle to train our learning controller with a modified version of the deep Q-learning (DQL) algorithm, enabling us to estimate the desired state trajectory as an optimal action-value function. We have evaluated DL-NMPC-SD against the baseline dynamic window approach (DWA), as well as against two state-of-the-art End2End and RL methods, respectively. The performance has been measured in three experiments: 1) in our GridSim virtual environment; 2) on indoor and outdoor navigation tasks using our RovisLab autonomous mobile test unit (AMTU) platform; and 3) on a full-scale autonomous test vehicle driving on public roads.

PMID:40146653 | DOI:10.1109/TNNLS.2025.3549816

Mol Inform. 2025 Mar;44(3):e202400325. doi: 10.1002/minf.202400325.

ABSTRACT

The blood brain barrier (BBB) is an endothelial-derived structure which restricts the movement of certain molecules between the general somatic circulatory system to the central nervous system (CNS). While the BBB maintains homeostasis by regulating the molecular environment induced by cerebrovascular perfusion, it also presents significant challenges in developing therapeutics intended to act on CNS targets. Many drug development practices rely partly on extensive cell and animal models to predict, to an extent, whether prospective therapeutic molecules can cross the BBB. In interest to reduce costs and improve prediction accuracy, many propose using advanced computational modeling of BBB permeability profiles leveraging empirical data. Given the scale of growth in machine learning and deep learning, we review the most recent machine learning approaches in predicting BBB permeability.

PMID:40146590 | DOI:10.1002/minf.202400325

Ophthalmol Ther. 2025 Mar 27. doi: 10.1007/s40123-025-01127-w. Online ahead of print.

ABSTRACT

INTRODUCTION: Existing deep learning (DL) algorithms lack the capability to accurately identify patients in immediate need of treatment for proliferative diabetic retinopathy (PDR). We aimed to develop a DL segmentation model to detect active PDR in six-field retinal images by the annotation of new retinal vessels and preretinal hemorrhages.

METHODS: We identified six-field retinal images classified at level 4 of the International Clinical Diabetic Retinopathy Disease Severity Scale collected at the Island of Funen from 2009 to 2019 as part of the Danish screening program for diabetic retinopathy (DR). A certified grader (grader 1) manually dichotomized the images into active or inactive PDR, and the images were then reassessed by two independent certified graders. In cases of disagreement, the final classification decision was made in collaboration between grader 1 and one of the secondary graders. Overall, 637 images were classified as active PDR. We then applied our pre-established DL segmentation model to annotate nine lesion types before training the algorithm. The segmentations of new vessels and preretinal hemorrhages were corrected for any inaccuracies before training the DL algorithm. After the classification and pre-segmentation phases the images were divided into training (70%), validation (10%), and testing (20%) datasets. We added 301 images with inactive PDR to the testing dataset.

RESULTS: We included 637 images of active PDR and 301 images of inactive PDR from 199 individuals. The training dataset had 1381 new vessel and preretinal hemorrhage lesions, while the validation dataset had 123 lesions and the testing dataset 374 lesions. The DL system demonstrated a sensitivity of 90% and a specificity of 70% for annotation-assisted classification of active PDR. The negative predictive value was 94%, while the positive predictive value was 57%.

CONCLUSIONS: Our DL segmentation model achieved excellent sensitivity and acceptable specificity in distinguishing active from inactive PDR.

PMID:40146482 | DOI:10.1007/s40123-025-01127-w

MAGMA. 2025 Mar 27. doi: 10.1007/s10334-025-01242-6. Online ahead of print.

ABSTRACT

OBJECTIVES: Despite prevalent use of chemical exchange saturation transfer (CEST) MRI, standardization remains elusive. Imaging depends heavily on parameters dictating radiofrequency (RF) events, gradients, and apparent diffusion coefficient (ADC). We present the Pulseq-CEST Library, a repository of CEST preparation and simulation definitions, including example data and evaluations, that provides a common basis for reproducible research, rapid prototyping, and in silico deep learning training data generation.

MATERIALS AND METHODS: A Pulseq-CEST experiment requires (i) a CEST preparation sequence, (ii) a Bloch-McConnell parameter set, (iii) a Bloch-McConnell simulation, and (iv) an evaluation script. Pulseq-CEST utilizes the Bloch-McConnell equations to model in vitro and in vivo conditions. Using this model, a candidate sequence or environment can be held constant while varying other inputs, enabling robust testing.

RESULTS: Data were compared for amide proton transfer weighted (APTw) and water shift and B1 (WASABI) protocols using a five-tube phantom and simulated environments. Real and simulated data matched anticipated spectral shapes and local peak characteristics. The Pulseq-CEST Library supports similar experiments with common sequences and environments to assess new protocols and sample data.

DISCUSSION: The Pulseq-CEST Library provides a flexible mechanism for standardizing and prototyping CEST sequences, facilitating collaborative development. With the capability for expansion, including open-source incorporation of new sequences and environments, the library accelerates the invention and spread of novel CEST and other saturation transfer approaches, such as relayed NOEs (rNOEs) and semisolid magnetization transfer contrast (MTC) methods.

PMID:40146474 | DOI:10.1007/s10334-025-01242-6

Brain Imaging Behav. 2025 Mar 27. doi: 10.1007/s11682-025-00998-8. Online ahead of print.

ABSTRACT

The morphological fingerprint in the brain is capable of identifying the uniqueness of an individual. However, whether such individual patterns are present in perinatal brains, and which morphological attributes or cortical regions better characterize the individual differences of neonates remain unclear. In this study, we proposed a deep learning framework that projected three-dimensional spherical meshes of three morphological features (i.e., cortical thickness, mean curvature, and sulcal depth) onto two-dimensional planes through quasi-conformal mapping, and employed the ResNet18 and contrastive learning for individual identification. We used the cross-sectional structural MRI data of 461 infants, incorporating with data augmentation, to train the model and fine-tuned the parameters based on 41 infants who had longitudinal scans. The model was validated on a fold of 20 longitudinal scanned infant data, and remarkable Top1 and Top5 accuracies of 85.90% and 92.20% were achieved, respectively. The sensorimotor and visual cortices were recognized as the most contributive regions in individual identification. Moreover, morphological fingerprints successfully predicted the long-term development of cognition and behavior. Furthermore, the folding morphology demonstrated greater discriminative capability than the cortical thickness. These findings provided evidence for the emergence of morphological fingerprints in the brain at the beginning of the third trimester, which may hold promising implications for understanding the formation of individual uniqueness, and predicting long-term neurodevelopmental risks in the brain during early development.

PMID:40146450 | DOI:10.1007/s11682-025-00998-8

Interdiscip Sci. 2025 Mar 27. doi: 10.1007/s12539-025-00694-7. Online ahead of print.

ABSTRACT

circRNAs are a type of single-stranded non-coding RNA molecules, and their unique feature is their closed circular structure. The interaction between circRNAs and RNA-binding proteins (RBPs) plays a key role in biological functions and is crucial for studying post-transcriptional regulatory mechanisms. The genome-wide circRNA binding event data obtained by cross-linking immunoprecipitation sequencing technology provides a foundation for constructing efficient computational model prediction methods. However, in existing studies, although machine learning techniques have been applied to predict circRNA-RBP interaction sites, these methods still have room for improvement in accuracy and lack interpretability. We propose CR-deal, which is an interpretable joint deep learning network that predicts the binding sites of circRNA and RBP through genome-wide circRNA data. CR-deal utilizes a graph attention network to unify sequence and structural features into the same view, more effectively utilizing structural features to improve accuracy. It can infer marker genes in the binding site through integrated gradient feature interpretation, thereby inferring functional structural regions in the binding site. We conducted benchmark tests on CR-deal on 37 circRNA datasets and 7 lncRNA datasets, respectively, and obtained the interpretability of CR-deal and discovered functional structural regions through 5 circRNA datasets. We believe that CR-deal can help researchers gain a deeper understanding of the functions and mechanisms of circRNA in living organisms and its critical role in the occurrence and development of diseases. The source code of CR-deal is provided free of charge on https://github.com/liuliwei1980/CR .

PMID:40146403 | DOI:10.1007/s12539-025-00694-7

Insights Imaging. 2025 Mar 27;16(1):74. doi: 10.1186/s13244-025-01951-5.

ABSTRACT

OBJECTIVES: To develop and validate a deep learning radiopathomics model (DLRPM) integrating radiological and pathological imaging data to predict biliary cyclooxygenase-2 (COX-2) expression in children with pancreaticobiliary maljunction (PBM), and to compare its performance with single-modality radiomics, deep learning radiomics (DLR), and pathomics models.

METHODS: This retrospective study included 219 PBM patients, divided into a training set (n = 104; median age, 2.8 years, 75.0% females) and internal test set (n = 71; median age, 2.2 years, 83.1% females) from center I, and an external test set (n = 44; median age, 3.4 years, 65.9% females) from center II. Biliary COX-2 expression was detected using immunohistochemistry. Radiomics, DLR, and pathomics features were extracted from portal venous-phase CT images and H&E-stained histopathological slides, respectively, to build individual single-modality models. These were then integrated to develop the DLRPM, combining three predictive signatures. Model performance was evaluated using AUC, net reclassification index (NRI, for assessing improvement in correct classification) and integrated discrimination improvement (IDI).

RESULTS: The DLRPM demonstrated the highest performance, with AUCs of 0.851 (95% CI, 0.759-0.942) in internal test set and 0.841 (95% CI, 0.721-0.960) in external test set. In comparison, AUCs for the radiomics, DLR, and pathomics models were 0.532-0.602, 0.658-0.660, and 0.787-0.805, respectively. The DLRPM significantly outperformed three single-modality models, as demonstrated by the NRI and IDI tests (all p < 0.05).

CONCLUSION: The multimodal DLRPM could accurately and robustly predict COX-2 expression, facilitating risk stratification and personalized postoperative management in PBM. However, prospective multicenter studies with larger cohorts are needed to further validate its generalizability.

CRITICAL RELEVANCE STATEMENT: Our proposed deep learning radiopathomics model, integrating CT and histopathological images, provides a novel and cost-effective approach to accurately predict biliary cyclooxygenase-2 expression, potentially advancing individualized risk stratification and improving long-term outcomes for pediatric patients with pancreaticobiliary maljunction.

KEY POINTS: Predicting biliary COX-2 expression in pancreaticobiliary maljunction (PBM) is critical but challenging. A deep learning radiopathomics model achieved high predictive accuracy for COX-2. The model supports patient stratification and personalized postoperative management in PBM.

PMID:40146354 | DOI:10.1186/s13244-025-01951-5

Transl Vis Sci Technol. 2025 Mar 3;14(3):26. doi: 10.1167/tvst.14.3.26.

ABSTRACT

PURPOSE: The purpose of this study was to develop a robust and general purpose artificial intelligence (AI) system that allows the identification of retinal optical coherence tomography (OCT) volumes with pathomorphological manifestations not present in normal eyes in screening programs and large retrospective studies.

METHODS: An unsupervised anomaly detection deep learning approach for the screening of retinal OCTs with any pathomorphological manifestations via Teacher-Student knowledge distillation is developed. The system is trained with only normal cases without any additional manual labeling. At test time, it scores how anomalous a sample is and produces localized anomaly maps with regions of interest in a B-scan. Fovea-centered OCT scans acquired with Spectralis (Heidelberg Engineering) were considered. A total of 3358 patients were used for development and testing. The detection performance was evaluated in a large data cohort with different pathologies including diabetic macular edema (DME) and the multiple stages of age-related macular degeneration (AMD) and on external public datasets with various disease biomarkers.

RESULTS: The volume-wise anomaly detection receiver operating characteristic (ROC) area under the curve (AUC) was 0.94 ± 0.05 in the test set. Pathological B-scan detection on external datasets varied between 0.81 and 0.87 AUC. Qualitatively, the derived anomaly maps pointed toward diagnostically relevant regions. The behavior of the system across the datasets was similar and consistent.

CONCLUSIONS: Anomaly detection constitutes a valid complement to supervised systems aimed at improving the success of vision preservation and eye care, and is an important step toward more efficient and generalizable screening tools.

TRANSLATIONAL RELEVANCE: Deep learning approaches can enable an automated and objective screening of a wide range of pathological retinal conditions that deviate from normal appearance.

PMID:40146150 | DOI:10.1167/tvst.14.3.26

Adv Sci (Weinh). 2025 Mar 27:e2500581. doi: 10.1002/advs.202500581. Online ahead of print.

ABSTRACT

Protein carbonylation refers to the covalent modification of proteins through the attachment of carbonyl groups, which arise from oxidative stress. This modification is biologically significant, as it can elicit modifications in protein functionality, signaling cascades, and cellular homeostasis. Accurate prediction of carbonylation sites offers valuable insights into the mechanisms underlying protein carbonylation and the pathogenesis of related diseases. Notably, carbonylation sites and ligand interaction sites, both functional sites, exhibit numerous similarities. The survey reveals that current computation-based approaches tend to make excessive cross-predictions for ligand interaction sites. To tackle this unresolved challenge, selective carbonylation sites (SCANS) is introduced, a novel deep learning-based framework. SCANS employs a multilevel attention strategy to capture both local (segment-level) and global (protein-level) features, utilizes a tailored loss function to penalize cross-predictions (residue-level), and applies transfer learning to augment the specificity of the overall network by leveraging knowledge from pretrained model. These innovative designs have been shown to successfully boost predictive performance and statistically outperforms current methods. Particularly, results on benchmark testing dataset demonstrate that SCANS consistently achieves low false positive rates, including low rates of cross-predictions. Furthermore, motif analyses and interpretations are conducted to provide novel insights into the protein carbonylation sites from various perspectives.

PMID:40145846 | DOI:10.1002/advs.202500581

Curr Opin Ophthalmol. 2025 May 1;36(3):205-209. doi: 10.1097/ICU.0000000000001135. Epub 2025 Mar 3.

ABSTRACT

PURPOSE OF REVIEW: Epiretinal membrane (ERM) surgery has undergone significant investigation over the last 2 years including assessment of novel surgical techniques and regarding the necessity of internal limiting membrane (ILM) peeling. This review seeks to highlight the latest literature in regards to ERM surgery from unique ERM profiles to clinical trials of surgical approach.

RECENT FINDINGS: The summative literature highlight that peeling of ILM may reduce recurrence compared to solely peeling ERM; however, these recurrences tend to be mild and nonvisually significant. Optical coherence tomography (OCT) has been leveraged preoperatively, intra-operatively, and postoperatively to enrich knowledge regarding risk factors for worse visual outcomes and deep learning models that are able to predict the anatomic outcome of ERM surgery after review of the preoperative OCT. There is no significant difference in outcomes between sequential and concurrent ERM surgery with cataract surgery. In uveitis evaluations related to ERM, posterior and intermediate uveitis were most associated with ERM, while in pediatric ERM, extent of diffuseness of central ERM correlated with surgical visual improvements.

SUMMARY: The latest ERM research has richly expanded the literature, allowing surgeons to better predict visual improvements postoperatively. This includes using OCT imaging biomarkers, but there remains a litany of unresolved questions about best surgical practices that are actively undergoing assessment.

PMID:40145317 | DOI:10.1097/ICU.0000000000001135