Neurourol Urodyn. 2025 May 19. doi: 10.1002/nau.70057. Online ahead of print.

ABSTRACT

OBJECTIVE: We aimed to prospectively investigate whether bladder volume measured using deep learning artificial intelligence (AI) algorithms (AI-BV) is more accurate than that measured using conventional methods (C-BV) if using a portable ultrasound bladder scanner (PUBS).

PATIENTS AND METHODS: Patients who underwent filling cystometry because of lower urinary tract symptoms between January 2021 and July 2022 were enrolled. Every time the bladder was filled serially with normal saline from 0 mL to maximum cystometric capacity in 50 mL increments, C-BV was measured using PUBS. Ultrasound images obtained during this process were manually annotated to define the bladder contour, which was used to build a deep learning AI model. The true bladder volume (T-BV) for each bladder volume range was compared with C-BV and AI-BV for analysis.

RESULTS: We enrolled 250 patients (213 men and 37 women), and a deep learning AI model was established using 1912 bladder images. There was a significant difference between C-BV (205.5 ± 170.8 mL) and T-BV (190.5 ± 165.7 mL) (p = 0.001), but no significant difference between AI-BV (197.0 ± 161.1 mL) and T-BV (190.5 ± 165.7 mL) (p = 0.081). In bladder volume ranges of 101-150, 151-200, and 201-300 mL, there were significant differences in the percentage of volume differences between [C-BV and T-BV] and [AI-BV and T-BV] (p < 0.05), but no significant difference if converted to absolute values (p > 0.05). C-BV (R2 = 0.91, p < 0.001) and AI-BV (R2 = 0.90, p < 0.001) were highly correlated with T-BV. The mean difference between AI-BV and T-BV (6.5 ± 50.4) was significantly smaller than that between C-BV and T-BV (15.0 ± 50.9) (p = 0.001).

CONCLUSION: Following image pre-processing, deep learning AI-BV more accurately estimated true BV than conventional methods in this selected cohort on internal validation. Determination of the clinical relevance of these findings and performance in external cohorts requires further study.

TRIAL REGISTRATION: The clinical trial was conducted using an approved product for its approved indication, so approval from the Ministry of Food and Drug Safety (MFDS) was not required. Therefore, there is no clinical trial registration number.

PMID:40384598 | DOI:10.1002/nau.70057

Analyst. 2025 May 19. doi: 10.1039/d5an00253b. Online ahead of print.

ABSTRACT

Raman spectroscopy requires baseline correction to address fluorescence- and instrumentation-related distortions. The existing baseline correction methods can be broadly classified into traditional mathematical approaches and deep learning-based techniques. While traditional methods often require manual parameter tuning for different spectral datasets, deep learning methods offer greater adaptability and enhance automation. Recent research on deep learning-based baseline correction has primarily focused on optimizing existing methods or designing new network architectures to improve correction performance. This study proposes a novel deep learning network architecture to further enhance baseline correction effectiveness, building upon prior research. Experimental results demonstrate that the proposed method outperforms existing approaches by achieving superior correction accuracy, reducing computation time, and more effectively preserving peak intensity and shape.

PMID:40384579 | DOI:10.1039/d5an00253b

J Magn Reson Imaging. 2025 May 19. doi: 10.1002/jmri.29819. Online ahead of print.

ABSTRACT

BACKGROUND: Deep learning (DL) models for accurate renal tumor characterization may benefit from multi-center datasets for improved generalizability; however, data-sharing constraints necessitate privacy-preserving solutions like federated learning (FL).

PURPOSE: To assess the performance and reliability of FL for renal tumor segmentation and classification in multi-institutional MRI datasets.

STUDY TYPE: Retrospective multi-center study.

POPULATION: A total of 987 patients (403 female) from six hospitals were included for analysis. 73% (723/987) had malignant renal tumors, primarily clear cell carcinoma (n = 509). Patients were split into training (n = 785), validation (n = 104), and test (n = 99) sets, stratified across three simulated institutions.

FIELD STRENGTH/SEQUENCE: MRI was performed at 1.5 T and 3 T using T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequences.

ASSESSMENT: FL and non-FL approaches used nnU-Net for tumor segmentation and ResNet for its classification. FL-trained models across three simulated institutional clients with central weight aggregation, while the non-FL approach used centralized training on the full dataset.

STATISTICAL TESTS: Segmentation was evaluated using Dice coefficients, and classification between malignant and benign lesions was assessed using accuracy, sensitivity, specificity, and area under the curves (AUCs). FL and non-FL performance was compared using the Wilcoxon test for segmentation Dice and Delong's test for AUC (p < 0.05).

RESULTS: No significant difference was observed between FL and non-FL models in segmentation (Dice: 0.43 vs. 0.45, p = 0.202) or classification (AUC: 0.69 vs. 0.64, p = 0.959) on the test set. For classification, no significant difference was observed between the models in accuracy (p = 0.912), sensitivity (p = 0.862), or specificity (p = 0.847) on the test set.

DATA CONCLUSION: FL demonstrated comparable performance to non-FL approaches in renal tumor segmentation and classification, supporting its potential as a privacy-preserving alternative for multi-institutional DL models.

EVIDENCE LEVEL: 4.

TECHNICAL EFFICACY: Stage 2.

PMID:40384349 | DOI:10.1002/jmri.29819

Med Phys. 2025 May 19. doi: 10.1002/mp.17895. Online ahead of print.

ABSTRACT

BACKGROUND: Microvascular invasion (MVI) is strongly associated with the prognosis of patients with hepatocellular carcinoma (HCC).

PURPOSE: To evaluate the value of Transformer models with Sonazoid contrast-enhanced ultrasound (CEUS) in the preoperative prediction of MVI.

METHODS: This retrospective study included 164 HCC patients. Deep learning features and radiomic features were extracted from arterial and Kupffer phase images, alongside the collection of clinicopathological parameters. Normality was assessed using the Shapiro-Wilk test. The Mann‒Whitney U-test and least absolute shrinkage and selection operator algorithm were applied to screen features. Transformer, radiomic, and clinical prediction models for MVI were constructed with logistic regression. Repeated random splits followed a 7:3 ratio, with model performance evaluated over 50 iterations. The area under the receiver operating characteristic curve (AUC, 95% confidence interval [CI]), sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), decision curve, and calibration curve were used to evaluate the performance of the models. The DeLong test was applied to compare performance between models. The Bonferroni method was used to control type I error rates arising from multiple comparisons. A two-sided p-value of < 0.05 was considered statistically significant.

RESULTS: In the training set, the diagnostic performance of the arterial-phase Transformer (AT) and Kupffer-phase Transformer (KT) models were better than that of the radiomic and clinical (Clin) models (p < 0.0001). In the validation set, both the AT and KT models outperformed the radiomic and Clin models in terms of diagnostic performance (p < 0.05). The AUC (95% CI) for the AT model was 0.821 (0.72-0.925) with an accuracy of 80.0%, and the KT model was 0.859 (0.766-0.977) with an accuracy of 70.0%. Logistic regression analysis indicated that tumor size (p = 0.016) and alpha-fetoprotein (AFP) (p = 0.046) were independent predictors of MVI.

CONCLUSIONS: Transformer models using Sonazoid CEUS have potential for effectively identifying MVI-positive patients preoperatively.

PMID:40384312 | DOI:10.1002/mp.17895

J Phys Chem Lett. 2025 May 18:5244-5251. doi: 10.1021/acs.jpclett.5c00592. Online ahead of print.

ABSTRACT

Machine learning (ML) approaches have become ubiquitous in the search for new materials in recent years. Bayesian optimization (BO) based on Gaussian processes (GPs) has become a widely recognized approach in material exploration. However, feature engineering has critical impacts on the efficiency of GP-based BO, because GPs cannot automatically generate descriptors. To address this limitation, this study applies deep kernel learning (DKL), which combines a neural network with a GP, to BO. The efficiency of the DKL model was comparable to or significantly better than that of a standard GP in a data set of 922 oxide data sets, covering band gaps, ionic dielectric constants, and effective masses of electrons, as well as in experimental data sets, the band gaps of 610 hybrid organic-inorganic perovskite alloys. When searching for the alloy with the highest Curie temperature among 4560 alloys, the standard GP outperformed the DKL model because a strongly correlated descriptor of the Curie temperature could be directly utilized. Additionally, DKL supports transfer learning, which further enhances its efficiency. Thus, we believe that BO based on DKL paves the way for exploring diverse material spaces more effectively than GPs.

PMID:40383929 | DOI:10.1021/acs.jpclett.5c00592

Protein Sci. 2025 Jun;34(6):e70155. doi: 10.1002/pro.70155.

ABSTRACT

The complex genotype-to-phenotype relationships in Mendelian diseases can be elucidated by mutation-induced disturbances to the networks of molecular interactions (interactomes) in human cells. Missense and nonsense mutations cause distinct perturbations within the human protein interactome, leading to functional and phenotypic effects with varying degrees of severity. Here, we structurally resolve the human protein interactome at atomic-level resolutions and perform structural and thermodynamic calculations to assess the biophysical implications of these mutations. We focus on a specific type of missense mutation, known as "quasi-null" mutations, which destabilize proteins and cause similar functional consequences (node removal) to nonsense mutations. We propose a "fold difference" quantification of deleteriousness, which measures the ratio between the fractions of node-removal mutations in datasets of Mendelian disease-causing and non-pathogenic mutations. We estimate the fold differences of node-removal mutations to range from 3 (for quasi-null mutations with folding ΔΔG ≥2 kcal/mol) to 20 (for nonsense mutations). We observe a strong positive correlation between biophysical destabilization and phenotypic deleteriousness, demonstrating that the deleteriousness of quasi-null mutations spans a continuous spectrum, with nonsense mutations at the extreme (highly deleterious) end. Our findings substantiate the disparity in phenotypic severity between missense and nonsense mutations and suggest that mutation-induced protein destabilization is indicative of the phenotypic outcomes of missense mutations. Our analyses of node-removal mutations allow for the potential identification of proteins whose removal or destabilization lead to harmful phenotypes, enabling the development of targeted therapeutic approaches, and enhancing comprehension of the intricate mechanisms governing genotype-to-phenotype relationships in clinically relevant diseases.

PMID:40384578 | DOI:10.1002/pro.70155

Nucleic Acids Res. 2025 May 19:gkaf406. doi: 10.1093/nar/gkaf406. Online ahead of print.

ABSTRACT

Base editing enables the high-throughput screening of genetic variants for phenotypic effects. Base editing screens require the design of single guide RNA (sgRNA) libraries to enable either gene- or variant-centric approaches. While computational tools supporting the design of sgRNAs exist, no solution offers versatile and scalable library design enabling all major use cases. Here, we introduce BEscreen, a comprehensive base editing guide design tool provided as a web server (bescreen.ostendorflab.org) and as a command line tool. BEscreen provides variant-, gene-, and region-centric modes to accommodate various screening approaches. The variant mode accepts genomic coordinates, amino acid changes, or rsIDs as input. The gene mode designs near-saturation libraries covering the entire coding sequence of given genes or transcripts, and the region mode designs all possible guides for given genomic regions. BEscreen enables selection of guides by biological consequence, it features comprehensive customization of base editor characteristics, and it offers optional annotation using Ensembl's Variant Effect Predictor. In sum, BEscreen is a highly versatile tool to design base editing screens for a wide range of use cases with seamless scalability from individual variants to large, near-saturation libraries.

PMID:40384567 | DOI:10.1093/nar/gkaf406

ACS Synth Biol. 2025 May 19. doi: 10.1021/acssynbio.5c00142. Online ahead of print.

ABSTRACT

Optogenetic systems offer precise control over gene expression, but leaky activity in the dark limits their dynamic range and, consequently, their applicability. Here, we enhanced an optogenetic system based on a split T7 RNA polymerase fused to blue-light-inducible Magnets by incorporating a tet-controlled riboregulatory module. This module exploits the photosensitivity of anhydrotetracycline and the designability of synthetic small RNAs to digitize light-controlled gene expression, implementing a repressive action over the translation of a polymerase fragment gene that is relieved with blue light. Our engineered system exhibited 13-fold improvement in dynamic range upon blue light exposure, which even raised to 23-fold improvement when using cells preadapted to chemical induction. As a functional demonstration, we implemented light-controlled antibiotic resistance in bacteria. Such integration of regulatory layers represents a suitable strategy for engineering better circuits for light-based biotechnological applications.

PMID:40384364 | DOI:10.1021/acssynbio.5c00142

Trop Med Int Health. 2025 May 19. doi: 10.1111/tmi.14123. Online ahead of print.

ABSTRACT

OBJECTIVES: To determine, in a randomised placebo-controlled trial, if cutaneous adverse reactions during treatment (CARDT) with Benznidazole occur as often as with Nifurtimox, and whether the dose and duration of treatment change that frequency.

METHODS: We conducted the EQUITY trial (NCT02369978), allocating Trypanosoma cruzi seropositive adults with no apparent clinical disease to a 120-day, blinded treatment with Benznidazole, Nifurtimox, or Placebo (ratio 2:2:1). Active treatment groups included either 60-day conventional-dose (60CD) regimens (Benznidazole 300 mg/day or Nifurtimox 480 mg/day, followed or preceded by, 60 days of placebo) or 120-day half-dose (120HD) regimens (Benznidazole 150 mg/day or Nifurtimox 240 mg/day). CARDT had blinded adjudication as moderate to severe during the follow-up visits.

RESULTS: Among 307 participants, 42 CARDT (17.1%, 95% confidence interval [CI] 12.6-22.4) occurred in 246 receiving active treatment, compared to two CARDT (3.3%, 95% CI 0.0-11.3) in 61 participants receiving placebo. In 122 patients treated with Benznidazole, there were 31 CARDT (25.4%, including eight severe), compared to 11 CARDT (8.9%, including four severe) in 124 individuals treated with Nifurtimox (p < 0.001). Among the 125 participants assigned to the 120HD regimen, there were 26 CARDT (20.8%, including six severe), compared to 16 CARDT (13.2%, including six severe) among 121 in the 60CD group (p = 0.005). The agent-regime interaction was not significant (p = 0.443). Eleven participants (25%) with CARDT did not complete their treatment.

CONCLUSION: CARDT occurred more frequently with Benznidazole treatment, particularly with longer exposure despite the half-dose regimen. Clinicians should consider these differences when discussing treatment options with patients receiving nitro derivative agents.

PMID:40384408 | DOI:10.1111/tmi.14123

Carbohydr Polym. 2025 Jun 15;358:123478. doi: 10.1016/j.carbpol.2025.123478. Epub 2025 Mar 6.

ABSTRACT

Acute myeloid leukaemia (AML) accounts for 30 % of adult leukaemia cases, predominantly affecting individuals over 60. The standard "7 + 3" intensive chemotherapy regimen is unsuitable for many elderly patients, contributing to AML's poor prognosis. While progress in drug therapies has been made, breakthroughs remain limited, indication-specific, and slow to expand. Drug repurposing offers a faster route to therapy development, while nanocarrier encapsulation broadens the scope of viable drug candidates. Chlorpromazine (CPZ) is an antipsychotic which has been identified as a potential anti-leukaemic agent. Due to its ability to cross the blood-brain barrier, it is likely to cause central nervous system (CNS) effects. The drug-in-cyclodextrin-in-liposome (DCL) nanocarrier platform enables the formulation of CPZ encapsulated with cyclodextrins (CDs) such as HP-γ-CD, SBE-β-CD, and Sugammadex. The CD/CPZ formulations were equally, or more efficient than free CPZ in inducing AML cell death. Uptake of the DCL in AML cells quickly reached saturation, with minimal differences among formulations, except for SBE-β-CD. When injected intravenously in zebrafish larvae, the different DCLs did not differ in biodistribution, and no brain accumulation was observed at two days post-injection. These DCL-based CPZ formulations maintain anti-leukaemic activity, avoid CNS accumulation, and allow drug availability adjustments based on the included CD.

PMID:40383608 | DOI:10.1016/j.carbpol.2025.123478

Sci Data. 2025 May 18;12(1):814. doi: 10.1038/s41597-025-05177-4.

ABSTRACT

Understanding human fertility requires dynamic and three-dimensional (3D) analysis of sperm movement, which extends beyond the capabilities of traditional datasets focused primarily on two-dimensional sperm motility or static morphological characteristics. To address this limitation, we introduce the 3D+t Multifocal Imaging Dataset of Human Sperm (3D-SpermVid), a repository comprising 121 multifocal video-microscopy hyperstacks of freely swimming sperm cells, incubated under non-capacitating conditions (NCC) and capacitating conditions (CC). This collection enables detailed observation and analysis of 3D sperm flagellar motility patterns over time, offering novel insights into the capacitation process and its implications for fertility. Data were captured using a multifocal imaging (MFI) system based on an optical microscope equipped with a piezoelectric device that adjusts focus at various heights, recording sperm movement in a volumetric space. By making this data publicly available, we aim to enable applications in deep learning and pattern recognition to uncover hidden flagellar motility patterns, fostering significant advancements in understanding 3D sperm morphology and dynamics, and developing new diagnostic tools for assessing male fertility, as well as assisting in the self-organizaton mechanisms driving spontaneous motility and navigation in 3D.

PMID:40383860 | DOI:10.1038/s41597-025-05177-4

Sci Rep. 2025 May 18;15(1):17263. doi: 10.1038/s41598-025-99858-0.

ABSTRACT

The widespread availability of miniaturized wearable fitness trackers has enabled the monitoring of various essential health parameters. Utilizing wearable technology for precise emotion recognition during human and computer interactions can facilitate authentic, emotionally aware contextual communication. In this paper, an emotion recognition system is proposed for the first time to conduct an experimental analysis of both discrete and dimensional models. An ensemble deep learning architecture is considered that consists of Long Short-Term Memory and Gated Recurrent Unit models to capture dynamic temporal dependencies within emotional data sequences effectively. The publicly available wearable devices EMOGNITION database is utilized to facilitate result reproducibility and comparison. The database includes physiological signals recorded using the Samsung Galaxy Watch, Empatica E4 wristband, and MUSE 2 Electroencephalogram (EEG) headband devices for a comprehensive understanding of emotions. A detailed comparison of all three dedicated wearable devices has been carried out to identify nine discrete emotions, exploring three different bio-signal combinations. The Samsung Galaxy and MUSE 2 devices achieve an average classification accuracy of 99.14% and 99.41%, respectively. The performance of the Samsung Galaxy device is examined for the 2D Valence-Arousal effective dimensional model. Results reveal average classification accuracy of 97.81% and 72.94% for Valence and Arousal dimensions, respectively. The acquired results demonstrate promising outcomes in emotion recognition when compared with the state-of-the-art methods.

PMID:40383809 | DOI:10.1038/s41598-025-99858-0

Sci Rep. 2025 May 18;15(1):17229. doi: 10.1038/s41598-025-02593-9.

ABSTRACT

Personalized recommendation systems are vital for enhancing user satisfaction and reducing information overload, especially in data-sparse environments like e-commerce platforms. This paper introduces a novel hybrid framework that combines Long Short-Term Memory (LSTM) with a modified Split-Convolution (SC) neural network (LSTM-SC) and an advanced sampling technique-Self-Inspected Adaptive SMOTE (SASMOTE). Unlike traditional SMOTE, SASMOTE adaptively selects "visible" nearest neighbors and incorporates a self-inspection strategy to filter out uncertain synthetic samples, ensuring high-quality data generation. Additionally, Quokka Swarm Optimization (QSO) and Hybrid Mutation-based White Shark Optimizer (HMWSO) are employed for optimizing sampling rates and hyperparameters, respectively. Experiments conducted on the goodbooks-10k and Amazon review datasets demonstrate significant improvements in RMSE, MAE, and R² metrics, proving the superiority of the proposed model over existing deep learning and collaborative filtering techniques. The framework is scalable, interpretable, and applicable across diverse domains, particularly in e-commerce and electronic publishing.

PMID:40383722 | DOI:10.1038/s41598-025-02593-9

Clin Nutr ESPEN. 2025 May 16:S2405-4577(25)00319-5. doi: 10.1016/j.clnesp.2025.05.022. Online ahead of print.

ABSTRACT

Enteral feeding needs secure access to the upper gastrointestinal tract, an evaluation of the gastric function to detect gastrointestinal intolerance, and a nutritional target to reach the patient's needs. Only in the last decades has progress been accomplished in techniques allowing an appropriate placement of the nasogastric tube, mainly reducing pulmonary complications. These techniques include point-of-care ultrasound (POCUS), electromagnetic sensors, real-time video-assisted placement, impedance sensors, and virtual reality. Again, POCUS is the most accessible tool available to evaluate gastric emptying, with antrum echo density measurement. Automatic measurements of gastric antrum content supported by deep learning algorithms and electric impedance provide gastric volume. Intragastric balloons can evaluate motility. Finally, advanced technologies have been tested to improve nutritional intake: Stimulation of the esophagus mucosa inducing contraction mimicking a contraction wave that may improve enteral nutrition efficacy, impedance sensors to detect gastric reflux and modulate the rate of feeding accordingly have been clinically evaluated. Use of electronic health records integrating nutritional needs, target, and administration is recommended.

PMID:40383254 | DOI:10.1016/j.clnesp.2025.05.022

J Cardiovasc Magn Reson. 2025 May 16:101913. doi: 10.1016/j.jocmr.2025.101913. Online ahead of print.

ABSTRACT

BACKGROUND: Image reconstruction from highly undersampled 4D flow MRI data can be very time consuming and may result in significant underestimation of velocities depending on regularization, thereby limiting the applicability of the method. The objective of the present work was to develop a generalizable self-supervised deep learning-based framework for fast and accurate reconstruction of highly undersampled 4D flow MRI and to demonstrate the utility of the framework for aortic and cerebrovascular applications.

METHODS: The proposed deep-learning-based framework, called FlowMRI-Net, employs physics-driven unrolled optimization using a complex-valued convolutional recurrent neural network and is trained in a self-supervised manner. The generalizability of the framework is evaluated using aortic and cerebrovascular 4D flow MRI acquisitions acquired on systems from two different vendors for various undersampling factors (R=8,16,24) and compared to compressed sensing (CS-LLR) reconstructions. Evaluation includes an ablation study and a qualitative and quantitative analysis of image and velocity magnitudes.

RESULTS: FlowMRI-Net outperforms CS-LLR for aortic 4D flow MRI reconstruction, resulting in significantly lower vectorial normalized root mean square error and mean directional errors for velocities in the thoracic aorta. Furthermore, the feasibility of FlowMRI-Net's generalizability is demonstrated for cerebrovascular 4D flow MRI reconstruction. Reconstruction times ranged from 3 to 7minutes on commodity CPU/GPU hardware.

CONCLUSION: FlowMRI-Net enables fast and accurate reconstruction of highly undersampled aortic and cerebrovascular 4D flow MRI, with possible applications to other vascular territories.

PMID:40383184 | DOI:10.1016/j.jocmr.2025.101913

Comput Biol Med. 2025 May 17;192(Pt B):110322. doi: 10.1016/j.compbiomed.2025.110322. Online ahead of print.

ABSTRACT

The application of AI for predicting critical heart failure endpoints using echocardiography is a promising avenue to improve patient care and treatment planning. However, fully supervised training of deep learning models in medical imaging requires a substantial amount of labelled data, posing significant challenges due to the need for skilled medical professionals to annotate image sequences. Our study addresses this limitation by exploring the potential of self-supervised learning, emphasising interpretability, robustness, and safety as crucial factors in cardiac imaging analysis. We leverage self-supervised learning on a large unlabelled dataset, facilitating the discovery of features applicable to a various downstream tasks. The backbone model not only generates informative features for training smaller models using simple techniques but also produces features that are interpretable by humans. The study employs a modified Self-supervised Transformer with Energy-based Graph Optimisation (STEGO) network on top of self-DIstillation with NO labels (DINO) as a backbone model, pre-trained on diverse medical and non-medical data. This approach facilitates the generation of self-segmented outputs, termed "parcels", which identify distinct anatomical sub-regions of the heart. Our findings highlight the robustness of these self-learned parcels across diverse patient profiles and phases of the cardiac cycle phases. Moreover, these parcels offer high interpretability and effectively encapsulate clinically relevant cardiac substructures. We conduct a comprehensive evaluation of the proposed self-supervised approach on publicly available datasets, demonstrating its adaptability to a wide range of requirements. Our results underscore the potential of self-supervised learning to address labelled data scarcity in medical imaging, offering a path to improve cardiac imaging analysis and enhance the efficiency and interpretability of diagnostic procedures, thus positively impacting patient care and clinical decision-making.

PMID:40383057 | DOI:10.1016/j.compbiomed.2025.110322

Epilepsy Behav. 2025 May 17;170:110470. doi: 10.1016/j.yebeh.2025.110470. Online ahead of print.

ABSTRACT

This study aimed to classify patients' focal (frontal, temporal, parietal, occipital), multifocal, and generalized epileptiform activities based on EEG findings using artificial intelligence models. The study included 575 patients followed in the Neurology Epilepsy Polyclinics of Adana City Training and Research Hospital between June 2021 and July 2024. Patient history, examination findings, seizure characteristics and EEG results were retrospectively reviewed to create a comprehensive database. Initially, machine learning architectures were applied to differentiate generalized and focal epilepsy. Subsequently, EEG findings were categorized into eight subgroups, and machine learning methods were utilized for classification. Three AI models-Multilayer Perceptron (MLP), Random Forest, and Support Vector Machine (SVM)-were employed. The dataset was further improved through data augmentation with SMOTE. The initial deep learning model achieved 89 % accuracy, recall, and F1 scores. Then, Optuna framework was incorporated into model to optimize hyperparameters, thus the accuracy reached 96 %. In comparison, the proposed ensemble model combining MLP, SVM and XGBoost achieved the highest accuracy of 98 %. The study demonstrates that data augmentation and ensemble AI models can provide robust decision support for physicians in classifying epilepsy types.

PMID:40382997 | DOI:10.1016/j.yebeh.2025.110470

Neural Netw. 2025 May 16;189:107573. doi: 10.1016/j.neunet.2025.107573. Online ahead of print.

ABSTRACT

In the field of healthcare, the acquisition and annotation of medical images present significant challenges, resulting in a scarcity of trainable samples. This data limitation hinders the performance of deep learning models, creating bottlenecks in clinical applications. To address this issue, we construct a framework (EScarcityS) aimed at enhancing the success rate of disease diagnosis in scarcity of trainable medical image scenarios. Firstly, considering that Transformer-based deep learning networks rely on a large amount of trainable data, this study takes into account the unique characteristics of pathological regions. By extracting the feature representations of all particles in medical images at different granularities, a multi-granularity Transformer network (MGVit) is designed. This network leverages additional prior knowledge to assist the Transformer network during training, thereby reducing the data requirement to some extent. Next, the importance maps of particles at different granularities, generated by MGVit, are fused to construct disease probability maps corresponding to the images. Based on these maps, a disease probability map-guided diffusion generation model is designed to generate more realistic and interpretable synthetic data. Subsequently, authentic and synthetical data are mixed and used to retrain MGVit, aiming to enhance the accuracy of medical image classification in scarcity of trainable medical image scenarios. Finally, we conducted detailed experiments on four real medical image datasets to validate the effectiveness of EScarcityS and its specific modules.

PMID:40382989 | DOI:10.1016/j.neunet.2025.107573

Water Res. 2025 May 13;283:123801. doi: 10.1016/j.watres.2025.123801. Online ahead of print.

ABSTRACT

Microalgae-driven nutrient recovery represents a promising technology for phosphorus removal from wastewater while simultaneously generating biomass that can be valorized to offset treatment costs. As full-scale processes come online, system parameters including biomass composition must be carefully monitored to optimize performance and prevent culture crashes. In this study, flow imaging microscopy (FIM) was leveraged to characterize microalgal community composition in near real-time at a full-scale municipal wastewater treatment plant (WWTP) in Wisconsin, USA, and population and morphotype dynamics were examined to identify relationships between water chemistry, biomass composition, and system performance. Two FIM technologies, FlowCam and ARTiMiS, were evaluated as monitoring tools. ARTiMiS provided a more accurate estimate of total system biomass, and estimates derived from particle area as a proxy for biovolume yielded better approximations than particle counts. Deep learning classification models trained on annotated image libraries demonstrated equivalent performance between FlowCam and ARTiMiS, and convolutional neural network (CNN) classifiers proved significantly more accurate when compared to feature table-based dense neural network (DNN) models. Across a two-year study period, Scenedesmus spp. appeared most important for phosphorus removal, and were negatively impacted by elevated temperatures and increase in nitrite/nitrate concentrations. Chlorella and Monoraphidium also played an important role in phosphorus removal. For both Scenedesmus and Chlorella, smaller morphological types were more often associated with better system performance, whereas larger morphotypes likely associated with stress response(s) correlated with poor phosphorus recovery rates. These results demonstrate the potential of FIM as a critical technology for high-resolution characterization of industrial microalgal processes.

PMID:40382876 | DOI:10.1016/j.watres.2025.123801

Comput Methods Programs Biomed. 2025 May 12;268:108812. doi: 10.1016/j.cmpb.2025.108812. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Diabetes is a chronic disease characterised by a high risk of developing diabetic nephropathy. The early identification of individuals at heightened risk of such complications or their exacerbation can be crucial to set a correct course of treatment. However, there are currently no widely accepted predictive tools for this task and, additionally, most of these models rely only on information at a single baseline visit. Considering this, we investigate the potential predictive role of patients' clinical history over multiple levels of renal disease severity while, at the same time, developing an effective predictive model.

METHODS: From the data collected in the DARWIN-Renal (DApagliflozin Real-World evIdeNce-Renal) study, a nationwide multicentre retrospective real-world study, we develop four different types of machine learning models, namely, logistic regression, random forest, Cox proportional hazards regression, and a deep learning model based on recurrent neural network to predict the crossing of 5 clinically relevant glomerular filtration rate thresholds for patients with type 2 diabetes.

RESULTS: The predictive performance of all models is satisfactory for all outcomes, even without the introduction of information referring to past visits, with AUROC and C-index between 0.69 and 0.98 and average precision well above the random model. The introduction of past information results into a clear improvement in performance for all the models, with percentage increases of up to 12% for both AUROC and C-index and 300% for average precision. The usefulness of past information is further corroborated by a feature importance analysis.

CONCLUSIONS: Incorporating data from the patients' clinical history into the predictive models greatly improves their performance, particularly for recurrent neural network where the full sequence of values for dynamic variables is provided compared to synthetic indicators of past history.

PMID:40382871 | DOI:10.1016/j.cmpb.2025.108812