Urologiia. 2024 Dec;(6):5-11.

ABSTRACT

AIM: to explore the development of a computer-aided diagnosis (CAD) system based on deep learning (DL) neural networks aimed at minimizing human error in PI-RADS grading and supporting medical decision-making.

MATERIALS AND METHODS: This retrospective multicenter study included a cohort of 136 patients, comprising 108 cases of PCa (PI-RADS score 4-5) and 28 cases of benign conditions (PI-RADS score 1-2). The 3D U-Net architecture was applied to process T2-weighted images (T2W), diffusion-weighted images (DWI), and dynamic contrast-enhanced images (DCE). Statistical analysis was conducted using Python libraries to assess diagnostic performance, including sensitivity, specificity, Dice similarity coefficients, and the area under the receiver operating characteristic curve (AUC).

RESULTS: The DL-CAD system achieved an average accuracy of 78%, sensitivity of 60%, and specificity of 84% for detecting lesions in the prostate. The Dice similarity coefficient for prostate segmentation was 0.71, and the AUC was 81.16%. The system demonstrated high specificity in reducing false-positive results, which, after further optimization, could help minimize unnecessary biopsies and overtreatment.

CONCLUSION: The DL-CAD system shows potential in supporting clinical decision-making for patients with clinically significant PCa by improving diagnostic accuracy, particularly in minimizing intra- and inter-observer variability. Despite its high specificity, improvements in sensitivity and segmentation accuracy are needed, which could be achieved by using larger datasets and advanced deep learning techniques. Further multicenter validation is required for accelerated integration of this system into clinical practice.

PMID:40377545

Curr Med Imaging. 2025 May 15. doi: 10.2174/0115734056360665250506115221. Online ahead of print.

ABSTRACT

BACKGROUND: Due to differences in subjective experience and professional level among doctors, as well as inconsistent diagnostic criteria, there are issues with the accuracy and reliability of single imaging diagnosis results for knee joint injuries.

OBJECTIVE: To address these issues, magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound (US) are adopted in this article for ensemble learning, and deep learning (DL) is combined for automatic analysis.

METHODS: By steps such as image enhancement, noise elimination, and tissue segmentation, the quality of image data is improved, and then convolutional neural networks (CNN) are used to automatically identify and classify injury types. The experimental results show that the DL model exhibits high sensitivity and specificity in the diagnosis of different types of injuries, such as anterior cruciate ligament tear, meniscus injury, cartilage injury, and fracture.

RESULTS: The diagnostic accuracy of anterior cruciate ligament tear exceeds 90%, and the highest diagnostic accuracy of cartilage injury reaches 95.80%. In addition, compared with traditional manual image interpretation, the DL model has significant advantages in time efficiency, with a significant reduction in average interpretation time per case. The diagnostic consistency experiment shows that the DL model has high consistency with doctors' diagnosis results, with an overall error rate of less than 2%.

CONCLUSION: The model has high accuracy and strong generalization ability when dealing with different types of joint injuries. These data indicate that combining multiple imaging technologies and the DL algorithm can effectively improve the accuracy and efficiency of diagnosing sports injuries of knee joints.

PMID:40377156 | DOI:10.2174/0115734056360665250506115221

Nucleic Acids Res. 2025 May 16:gkaf392. doi: 10.1093/nar/gkaf392. Online ahead of print.

ABSTRACT

Antisense oligonucleotides (ASOs) are a promising class of gene therapies that can modulate the gene expression. However, designing ASOs manually is resource-intensive and time-consuming. To address this, we introduce a user-friendly web server for ASOptimizer, a deep learning-based computational framework for optimizing ASO sequences and chemical modifications. Given a user-provided ASO sequence, the web server systematically explores modification sites within the nucleic acid and returns a ranked list of promising modification patterns. With an intuitive interface requiring no expertise in deep learning tools, the platform makes ASOptimizer easily accessible to the broader research community. The web server is freely available at https://asoptimizer.s-core.ai/.

PMID:40377084 | DOI:10.1093/nar/gkaf392

Small. 2025 May 16:e2502328. doi: 10.1002/smll.202502328. Online ahead of print.

ABSTRACT

Drug co-assemblies have attracted extensive attention due to their advantages of easy preparation, adjustable performance and drug component co-delivery. However, the lack of a clear and reasonable co-assembly strategy has hindered the wide application and promotion of drug-co assembly. This paper introduces a deep learning-based sonosensitizer-drug interaction (SDI) model to predict the particle size of the drug mixture. To analyze the factors influencing the particle size after mixing, the graph neural network is employed to capture the atomic, bond, and structural features of the molecules. A multi-scale cross-attention mechanism is designed to integrate the feature representations of different scale substructures of the two drugs, which not only improves prediction accuracy but also allows for the analysis of the impact of molecular structures on the predictions. Ablation experiments evaluate the impact of molecular properties, and comparisons with other machine and deep learning methods show superiority, achieving 90.00% precision, 96.00% recall, and 91.67% F1-score. Furthermore, the SDI predicts the co-assembly of the chemotherapy drug methotrexate (MET) and the sonosensitizer emodin (EMO) to form the nanomedicine NanoME. This prediction is further validated through experiments, demonstrating that NanoME can be used for fluorescence imaging of liver cancer and sonodynamic/chemotherapy anticancer therapy.

PMID:40376918 | DOI:10.1002/smll.202502328

Digit Health. 2025 May 14;11:20552076251341092. doi: 10.1177/20552076251341092. eCollection 2025 Jan-Dec.

ABSTRACT

OBJECTIVE: Early and accurate detection of COVID-19 and pneumonia through medical imaging is critical for effective patient management. This study aims to develop a robust framework that integrates synthetic image augmentation with advanced deep learning (DL) models to address dataset imbalance, improve diagnostic accuracy, and enhance trust in artificial intelligence (AI)-driven diagnoses through Explainable AI (XAI) techniques.

METHODS: The proposed framework benchmarks state-of-the-art models (InceptionV3, DenseNet, ResNet) for initial performance evaluation. Synthetic images are generated using Feature Interpolation through Linear Mapping and principal component analysis to enrich dataset diversity and balance class distribution. YOLOv8 and InceptionV3 models, fine-tuned via transfer learning, are trained on the augmented dataset. Grad-CAM is used for model explainability, while large language models (LLMs) support visualization analysis to enhance interpretability.

RESULTS: YOLOv8 achieved superior performance with 97% accuracy, precision, recall, and F1-score, outperforming benchmark models. Synthetic data generation effectively reduced class imbalance and improved recall for underrepresented classes. Comparative analysis demonstrated significant advancements over existing methodologies. XAI visualizations (Grad-CAM heatmaps) highlighted anatomically plausible focus areas aligned with clinical markers of COVID-19 and pneumonia, thereby validating the model's decision-making process.

CONCLUSION: The integration of synthetic data generation, advanced DL, and XAI significantly enhances the detection of COVID-19 and pneumonia while fostering trust in AI systems. YOLOv8's high accuracy, coupled with interpretable Grad-CAM visualizations and LLM-driven analysis, promotes transparency crucial for clinical adoption. Future research will focus on developing a clinically viable, human-in-the-loop diagnostic workflow, further optimizing performance through the integration of transformer-based language models to improve interpretability and decision-making.

PMID:40376574 | PMC:PMC12078974 | DOI:10.1177/20552076251341092

Digit Health. 2025 May 14;11:20552076251333195. doi: 10.1177/20552076251333195. eCollection 2025 Jan-Dec.

ABSTRACT

In recent times, appropriate diagnosis of brain tumour is a crucial task in medical system. Therefore, identification of a potential brain tumour is challenging owing to the complex behaviour and structure of the human brain. To address this issue, a deep learning-driven framework consisting of four pre-trained models viz DenseNet169, VGG-19, Xception, and EfficientNetV2B2 is developed to classify potential brain tumours from medical resonance images. At first, the deep learning models are trained and fine-tuned on the training dataset, obtained validation scores of trained models are considered as model-wise weights. Then, trained models are subsequently evaluated on the test dataset to generate model-specific predictions. In the weight-aware decision module, the class-bucket of a probable output class is updated with the weights of deep models when their predictions match the class. Finally, the bucket with the highest aggregated value is selected as the final output class for the input image. A novel weight-aware decision mechanism is a key feature of this framework, which effectively deals tie situations in multi-class classification compared to conventional majority-based techniques. The developed framework has obtained promising results of 98.7%, 97.52%, and 94.94% accuracy on three different datasets. The entire framework is seamlessly integrated into an end-to-end web-application for user convenience. The source code, dataset and other particulars are publicly released at https://github.com/SaiSanthosh1508/Brain-Tumour-Image-classification-app [Rishik Sai Santhosh, "Brain Tumour Image Classification Application," https://github.com/SaiSanthosh1508/Brain-Tumour-Image-classification-app] for academic, research and other non-commercial usage.

PMID:40376570 | PMC:PMC12078957 | DOI:10.1177/20552076251333195

Digit Health. 2025 May 14;11:20552076241310060. doi: 10.1177/20552076241310060. eCollection 2025 Jan-Dec.

ABSTRACT

Diagnosis and treatment of endometrial diseases are crucial for women's health. Over the past decade, ultrasound has emerged as a non-invasive, safe, and cost-effective imaging tool, significantly contributing to endometrial disease diagnosis and generating extensive datasets. The introduction of artificial intelligence has enabled the application of machine learning and deep learning to extract valuable information from these datasets, enhancing ultrasound diagnostic capabilities. This paper reviews the progress of artificial intelligence in ultrasound image analysis for endometrial diseases, focusing on applications in diagnosis, decision support, and prognosis analysis. We also summarize current research challenges and propose potential solutions and future directions to advance ultrasound artificial intelligence technology in endometrial disease diagnosis, ultimately improving women's health through digital tools.

PMID:40376569 | PMC:PMC12078975 | DOI:10.1177/20552076241310060

Biophys Rev. 2025 Mar 18;17(2):335-345. doi: 10.1007/s12551-025-01301-1. eCollection 2025 Apr.

ABSTRACT

Despite its long history and widespread use, conventional bright-field optical microscopy has received recent attention as an excellent option to perform accurate, label-free, imaging of biological objects. As with any imaging system, bright-field produces an ill-defined representation of the specimen, in this case characterized by intertwined phase and amplitude in image formation, invisibility of phase objects at exact focus, and both positive and negative contrast present in images. These drawbacks have prevented the application of bright-field to the accurate imaging of unlabeled specimens. To address these challenges, a variety of methods using hardware, software or both have been developed, with the goal of providing solutions to the inverse imaging problem set in bright-field. We revise the main operating principles and characteristics of bright-field microscopy, followed by a discussion of the solutions (and potential limitations) to reconstruction in two dimensions (2D). We focus on methods based on conventional optics, including defocusing microscopy, transport of intensity, ptychography and deconvolution. Advances to achieving three-dimensional (3D) bright-field imaging are presented, including methods that exploit multi-view reconstruction, physical modeling, deep learning and conventional digital image processing. Among these techniques, optical sectioning in bright-field microscopy (OSBM) constitutes a direct approach that captures z-image stacks using a standard microscope and applies digital filters in the spatial domain, yielding inverse-imaging solutions in 3D. Finally, additional techniques that expand the capabilities of bright-field are discussed. Label-free, inverse imaging in conventional optical microscopy thus emerges as a powerful biophysical tool for accurate 2D and 3D imaging of biological samples.

PMID:40376420 | PMC:PMC12075049 | DOI:10.1007/s12551-025-01301-1

Prostate Cancer. 2025 May 8;2025:2019841. doi: 10.1155/proc/2019841. eCollection 2025.

ABSTRACT

Introduction: Prostate cancer is the second most common cancer among men worldwide. This cancer has become extremely noticeable due to the increase of prostate cancer in Iranian men in recent years due to the lack of marriage and sexual intercourse, as well as the abuse of hormones in sports without any standards. Methods: The histopathology images from a treatment center to diagnose prostate cancer are used with the help of deep learning methods, considering the two characteristics of Tile and Grad-CAM. The approach of this research is to present a prostate cancer diagnosis model to achieve proper performance from histopathology images with the help of a developed deep learning method based on the manifold model. Results: Similarly, in addition to the diagnosis of prostate cancer, a study on the methods of preventing this disease was investigated in literature reviews, and finally, after simulation, prostate cancer presentation factors were determined. Conclusions: The simulation results indicated that the proposed method has a performance advantage over the other state-of-the-art methods, and the accuracy of this method is up to 97.41%.

PMID:40376132 | PMC:PMC12081159 | DOI:10.1155/proc/2019841

Front Physiol. 2025 May 1;16:1581699. doi: 10.3389/fphys.2025.1581699. eCollection 2025.

ABSTRACT

Freezing of Gait (FoG) is a disabling motor symptom that characterizes Parkinson's Disease (PD) patients and significantly affects their mobility and quality of life. The paper presents a novel hybrid deep learning framework for the detection of FoG episodes using wearable sensors. The methodology combines CNNs for spatial feature extraction, BiLSTM networks for temporal modeling, and an attention mechanism to enhance interpretability and focus on critical gait features. The approach leverages multimodal datasets, including tDCS FOG, DeFOG, Daily Living, and Hantao's Multimodal, to ensure robustness and generalizability. The proposed model deals with sensor noise, inter-subject variability, and data imbalance through comprehensive preprocessing techniques such as sensor fusion, normalization, and data augmentation. The proposed model achieved an average accuracy of 92.5%, F1-score of 89.3%, and AUC of 0.91, outperforming state-of-the-art methods. Post-training quantization and pruning enabled deployment on edge devices such as Raspberry Pi and Coral TPU, achieving inference latency under 350 ms. Ablation studies show the critical contribution of key architectural components to the model's effectiveness. Optimized to be deployed real-time, it is a potentially promising solution that can help correctly detect FoG, thereby achieving better clinical monitoring and improving patients' outcomes in a controlled as well as real world.

PMID:40376117 | PMC:PMC12079673 | DOI:10.3389/fphys.2025.1581699

bioRxiv [Preprint]. 2025 Apr 18:2025.04.13.648574. doi: 10.1101/2025.04.13.648574.

ABSTRACT

Deep neural networks have led to significant advancements in microscopy image generation and analysis. In single-molecule localization based super-resolution microscopy, neural networks are capable of predicting fluorophore positions from high-density emitter data, thus reducing acquisition time, and increasing imaging throughput. However, neural network-based solutions in localization microscopy require intensive human intervention and computation expertise to address the compromise between model performance and its generalization. For example, researchers manually tune parameters to generate training images that are similar to their experimental data; thus, for every change in the experimental conditions, a new training set should be manually tuned, and a new model should be trained. Here, we introduce AutoDS and AutoDS3D, two software programs for reconstruction of single-molecule super-resolution microscopy data that are based on Deep-STORM and DeepSTORM3D, that significantly reduce human intervention from the analysis process by automatically extracting the experimental parameters from the imaging raw data. In the 2D case, AutoDS selects the optimal model for the analysis out of a set of pre-trained models, hence, completely removing user supervision from the process. In the 3D case, we improve the computation efficiency of DeepSTORM3D and integrate the lengthy workflow into a graphic user interface that enables image reconstruction with a single click. Ultimately, we demonstrate superior performance of both pipelines compared to Deep-STORM and DeepSTORM3D for single-molecule imaging data of complex biological samples, while significantly reducing the manual labor and computation time.

PMID:40376092 | PMC:PMC12080944 | DOI:10.1101/2025.04.13.648574

Front Immunol. 2025 May 1;16:1513589. doi: 10.3389/fimmu.2025.1513589. eCollection 2025.

ABSTRACT

INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) exhibits higher hypoxia level than most solid tumors, and the presence of intratumoral hypoxia is associated with a poor prognosis. However, the identification of hypoxia levels based on pathological images, and the mechanisms regulating ferroptosis resistance, remain to be elucidated. The objective of this study was to construct a deep learning model to evaluate the hypoxia characteristics of PDAC and to explore the role of Sulfide quinone oxidoreductase (SQOR) in hypoxia-mediated ferroptosis resistance.

METHODS: Multi-omics data were integrated to analyze the correlation between hypoxia score of PDAC, SQOR expression and prognosis, and ferroptosis resistance level. A deep learning model of Whole Slide Images (WSIs) were constructed to predict the hypoxia level of patients. In vitro hypoxia cell models, SQOR knockdown experiments and nude mouse xenograft models were used to verify the regulatory function of SQOR on ferroptosis.

RESULTS: PDAC exhibited significantly higher hypoxia levels than normal tissues, correlating with reduced overall survival in patients. In slide level, our deep learning model can effectively identify PDAC hypoxia levels with good performance. SQOR was upregulated in tumor tissues and positively associated with both hypoxia score and ferroptosis resistance. SQOR promotes the malignant progression of PDAC in hypoxic environment by enhancing the resistance of tumor cells to ferroptosis. SQOR knockdown resulted in decreased cell viability, decreased migration ability and increased MDA level under hypoxic Ersatin induced conditions. Furthermore, SQOR inhibitor in combination with ferroptosis inducer has the potential to inhibit tumor growth in vivo in a synergistic manner.

DISCUSSION: This study has established a hypoxia detection model of PDAC based on WSIs, providing a new tool for clinical evaluation. The study revealed a new mechanism of SQOR mediating ferroptosis resistance under hypoxia and provided a basis for targeted therapy.

PMID:40375994 | PMC:PMC12078260 | DOI:10.3389/fimmu.2025.1513589

Proc SPIE Int Soc Opt Eng. 2024 Feb;12933:1293319. Epub 2024 Apr 3.

ABSTRACT

Segmentation of microvascular structures, such as arterioles, venules, and capillaries, from human kidney whole slide images (WSI) has become a focal point in renal pathology. Current manual segmentation techniques are time-consuming and not feasible for large-scale digital pathology images. While deep learning-based methods offer a solution for automatic segmentation, most suffer from a limitation: they are designed for and restricted to training on single-site, single-scale data. In this paper, we present Omni-Seg, a novel single dynamic network method that capitalizes on multi-site, multi-scale training data. Unique to our approach, we utilize partially labeled images, where only one tissue type is labeled per training image, to segment microvascular structures. We train a singular deep network using images from two datasets, HuBMAP and NEPTUNE, across different magnifications (40×, 20×, 10×, and 5×). Experimental results indicate that Omni-Seg outperforms in terms of both the Dice Similarity Coefficient (DSC) and Intersection over Union (IoU). Our proposed method provides renal pathologists with a powerful computational tool for the quantitative analysis of renal microvascular structures.

PMID:40375952 | PMC:PMC12080525

J Med Internet Res. 2025 May 15;27:e69785. doi: 10.2196/69785.

ABSTRACT

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a common and progressive respiratory condition characterized by persistent airflow limitation and symptoms such as dyspnea, cough, and sputum production. Acute exacerbations (AE) of COPD (AE-COPD) are key determinants of disease progression; yet, existing predictive models relying mainly on spirometric measurements, such as forced expiratory volume in 1 second, reflect only a fraction of the physiological information embedded in respiratory function tests. Recent advances in artificial intelligence (AI) have enabled more sophisticated analyses of full spirometric curves, including flow-volume loops and volume-time curves, facilitating the identification of complex patterns associated with increased exacerbation risk.

OBJECTIVE: This study aimed to determine whether a predictive model that integrates clinical data and spirometry images with the use of AI improves accuracy in predicting moderate-to-severe and severe AE-COPD events compared to a clinical-only model.

METHODS: A retrospective cohort study was conducted using COPD registry data from 2 teaching hospitals from January 2004 to December 2020. The study included a total of 10,492 COPD cases, divided into a development cohort (6870 cases) and an external validation cohort (3622 cases). The AI-enhanced model (AI-PFT-Clin) used a combination of clinical variables (eg, history of AE-COPD, dyspnea, and inhaled treatments) and spirometry image data (flow-volume loop and volume-time curves). In contrast, the Clin model used only clinical variables. The primary outcomes were moderate-to-severe and severe AE-COPD events within a year of spirometry.

RESULTS: In the external validation cohort, the AI-PFT-Clin model outperformed the Clin model, showing an area under the receiver operating characteristic curve of 0.755 versus 0.730 (P<.05) for moderate-to-severe AE-COPD and 0.713 versus 0.675 (P<.05) for severe AE-COPD. The AI-PFT-Clin model demonstrated reliable predictive capability across subgroups, including younger patients and those without previous exacerbations. Higher AI-PFT-Clin scores correlated with elevated AE-COPD risk (adjusted hazard ratio for Q4 vs Q1: 4.21, P<.001), with sustained predictive stability over a 10-year follow-up period.

CONCLUSIONS: The AI-PFT-Clin model, by integrating clinical data with spirometry images, offers enhanced predictive accuracy for AE-COPD events compared to a clinical-only approach. This AI-based framework facilitates the early identification of high-risk individuals through the detection of physiological abnormalities not captured by conventional metrics. The model's robust performance and long-term predictive stability suggest its potential utility in proactive COPD management and personalized intervention planning. These findings highlight the promise of incorporating advanced AI techniques into routine COPD management, particularly in populations traditionally seen as lower risk, supporting improved management of COPD through tailored patient care.

PMID:40373296 | DOI:10.2196/69785

ACS Sens. 2025 May 15. doi: 10.1021/acssensors.4c03602. Online ahead of print.

ABSTRACT

The prevalence of sleep disorders in the aging population and the importance of sleep quality for health have emphasized the need for accurate and accessible sleep monitoring solutions. Polysomnography (PSG) remains the clinical gold standard for diagnosing sleep disorders; however, its discomfort and inconvenience limit its accessibility. To address these issues, a wearable device (WD) integrated with stretchable transparent electrodes (STEs) is developed in this study for multisignal sleep monitoring and artificial intelligence (AI)-driven sleep staging. Utilizing conductive and flexible STEs, the WD records multiple biological signals (electroencephalogram [EEG], electrooculogram [EOG], electromyogram [EMG], photoplethysmography, and motion data) with high precision and low noise, comparable to PSG (<4 μVRMS). It achieves a 73.2% accuracy and a macro F1 score of 0.72 in sleep staging using an AI model trained on multisignal inputs. Notably, accuracy marginally improves when using only the EEG, EOG, and EMG channels, which may simplify future device designs. This WD offers a compact, multisignal solution for at-home sleep monitoring, with the potential for use as an evaluation tool for personalized sleep therapies.

PMID:40373282 | DOI:10.1021/acssensors.4c03602

PLoS One. 2025 May 15;20(5):e0322586. doi: 10.1371/journal.pone.0322586. eCollection 2025.

ABSTRACT

Apples are one of the most productive fruits in the world, in addition to their nutritional and health advantages for humans. Even with the continuous development of AI in agriculture in general and apples in particular, automated systems continue to encounter challenges identifying rotten fruit and variations within the same apple category, as well as similarity in type, color, and shape of different fruit varieties. These issues, in addition to apple diseases, substantially impact the economy, productivity, and marketing quality. In this paper, we first provide a novel comprehensive collection named Apple Fruit Varieties Collection (AFVC) with 29,750 images through 85 classes. Second, we distinguish fresh and rotten apples with Apple Fruit Quality Categorization (AFQC), which has 2,320 photos. Third, an Apple Diseases Extensive Collection (ADEC), comprised of 2,976 images with seven classes, was offered. Fourth, following the state of the art, we develop an Optimized Apple Orchard Model (OAOM) with a new loss function named measured focal cross-entropy (MFCE), which assists in improving the proposed model's efficiency. The proposed OAOM gives the highest performance for apple varieties identification with AFVC; accuracy was 93.85%. For the apples rotten recognition with AFQC, accuracy was 98.28%. For the identification of the diseases via ADEC, it was 99.66%. OAOM works with high efficiency and outperforms the baselines. The suggested technique boosts apple system automation with numerous duties and outstanding effectiveness. This research benefits the growth of apple's robotic vision, development policies, automatic sorting systems, and decision-making enhancement.

PMID:40373081 | DOI:10.1371/journal.pone.0322586

PLoS One. 2025 May 15;20(5):e0322197. doi: 10.1371/journal.pone.0322197. eCollection 2025.

ABSTRACT

BACKGROUND: The use of Artificial Intelligence (AI) is exponentially rising in the healthcare sector. This change influences various domains of early identification, diagnosis, and treatment of diseases.

PURPOSE: This study examines the integration of AI in healthcare, focusing on its transformative potential in diagnostics and treatment, and the challenges and methodologies. shaping its future development.

METHODS: The review included 68 academic studies retracted from different databases (WOS, Scopus and Pubmed) from January 2020 and April 2024. After careful review and data analysis, AI methodologies, benefits and challenges, were summarized.

RESULTS: The number of studies showed a steady rise from 2020 to 2023. Most of them were the results of a collaborative work with international universities (92.1%). The majority (66.7%) were published in top-tier (Q1) journals and 40% were cited 2-10 times. The results have shown that AI tools such as deep learning methods and machine learning continue to significantly improve accuracy and timely execution of medical processes. Benefits were discussed from both the organizational and the patient perspective in the categories of diagnosis, treatment, consultation and health monitoring of diseases. However, some challenges may exist, despite these benefits, and are related to data integration, errors related to data processing and decision making, and patient safety.

CONCLUSION: The article examines the present status of AI in medical applications and explores its potential future applications. The findings of this review are useful for healthcare professionals to acquire deeper knowledge on the use of medical AI from design to implementation stage. However, a thorough assessment is essential to gather more insights into whether AI benefits outweigh its risks. Additionally, ethical and privacy issues need careful consideration.

PMID:40372995 | DOI:10.1371/journal.pone.0322197

JMIR Cancer. 2025 May 15;11:e64697. doi: 10.2196/64697.

ABSTRACT

BACKGROUND: Progression-free survival (PFS) is a crucial endpoint in cancer drug research. Clinician-confirmed cancer progression, namely real-world PFS (rwPFS) in unstructured text (ie, clinical notes), serves as a reasonable surrogate for real-world indicators in ascertaining progression endpoints. Response evaluation criteria in solid tumors (RECIST) is traditionally used in clinical trials using serial imaging evaluations but is impractical when working with real-world data. Manual abstraction of clinical progression from unstructured notes remains the gold standard. However, this process is a resource-intensive, time-consuming process. Natural language processing (NLP), a subdomain of machine learning, has shown promise in accelerating the extraction of tumor progression from real-world data in recent years.

OBJECTIVES: We aim to configure a pretrained, general-purpose health care NLP framework to transform free-text clinical notes and radiology reports into structured progression events for studying rwPFS on metastatic breast cancer (mBC) cohorts.

METHODS: This study developed and validated a novel semiautomated workflow to estimate rwPFS in patients with mBC using deidentified electronic health record data from the Nference nSights platform. The developed workflow was validated in a cohort of 316 patients with hormone receptor-positive, human epidermal growth factor receptor-2 (HER-2) 2-negative mBC, who were started on palbociclib and letrozole combination therapy between January 2015 and December 2021. Ground-truth datasets were curated to evaluate the workflow's performance at both the sentence and patient levels. NLP-captured progression or a change in therapy line were considered outcome events, while death, loss to follow-up, and end of the study period were considered censoring events for rwPFS computation. Peak reduction and cumulative decline in Patient Health Questionnaire-8 (PHQ-8) scores were analyzed in the progressed and nonprogressed patient subgroups.

RESULTS: The configured clinical NLP engine achieved a sentence-level progression capture accuracy of 98.2%. At the patient level, initial progression was captured within ±30 days with 88% accuracy. The median rwPFS for the study cohort (N=316) was 20 (95% CI 18-25) months. In a validation subset (n=100), rwPFS determined by manual curation was 25 (95% CI 15-35) months, closely aligning with the computational workflow's 22 (95% CI 15-35) months. A subanalysis revealed rwPFS estimates of 30 (95% CI 24-39) months from radiology reports and 23 (95% CI 19-28) months from clinical notes, highlighting the importance of integrating multiple note sources. External validation also demonstrated high accuracy (92.5% sentence level; 90.2% patient level). Sensitivity analysis revealed stable rwPFS estimates across varying levels of missing source data and event definitions. Peak reduction in PHQ-8 scores during the study period highlighted significant associations between patient-reported outcomes and disease progression.

CONCLUSIONS: This workflow enables rapid and reliable determination of rwPFS in patients with mBC receiving combination therapy. Further validation across more diverse external datasets and other cancer types is needed to ensure broader applicability and generalizability.

PMID:40372953 | DOI:10.2196/64697

IEEE Trans Ultrason Ferroelectr Freq Control. 2025 May 15;PP. doi: 10.1109/TUFFC.2025.3570496. Online ahead of print.

ABSTRACT

Ultrasound localization microscopy (ULM) has revolutionized microvascular imaging by breaking the acoustic diffraction limit. However, different ULM workflows depend heavily on distinct prior knowledge, such as the impulse response and empirical selection of parameters (e.g., the number of microbubbles (MBs) per frame M), or the consistency of training-test dataset in deep learning (DL)-based studies. We hereby propose a general ULM pipeline that reduces priors. Our approach leverages a DL model that simultaneously distills microbubble signals and reduces speckle from every frame without estimating the impulse response and M. Our method features an efficient channel attention vision transformer (ViT) and a progressive learning strategy, enabling it to learn global information through training on progressively increasing patch sizes. Ample synthetic data were generated using the k-Wave toolbox to simulate various MB patterns, thus overcoming the deficiency of labeled data. The ViT output was further processed by a standard radial symmetry method for sub-pixel localization. Our method performed well on model-unseen public datasets: one in silico dataset with ground truth and four in vivo datasets of mouse tumor, rat brain, rat brain bolus, and rat kidney. Our pipeline outperformed conventional ULM, achieving higher positive predictive values (precision in DL, 0.88-0.41 vs. 0.83-0.16) and improved accuracy (root-mean-square errors: 0.25-0.14 λ vs. 0.31-0.13 λ) across a range of signal-to-noise ratios from 60 dB to 10 dB. Our model could detect more vessels in diverse in vivo datasets while achieving comparable resolutions to the standard method. The proposed ViT-based model, seamlessly integrated with state-of-the-art downstream ULM steps, improved the overall ULM performance with no priors.

PMID:40372868 | DOI:10.1109/TUFFC.2025.3570496

IEEE Trans Neural Netw Learn Syst. 2025 May 15;PP. doi: 10.1109/TNNLS.2025.3566021. Online ahead of print.

ABSTRACT

Speech enhancement (SE) is critical for improving speech intelligibility and quality in various audio devices. In recent years, deep learning-based methods have significantly improved SE performance, but they often come with a high computational cost, which is prohibitive for a large number of edge devices, such as headsets and hearing aids. This work proposes an ultralow-power SE system based on the brain-inspired spiking neural network (SNN) called Spiking-FullSubNet. Spiking-FullSubNet follows a full-band and subband fusioned approach to effectively capture both global and local spectral information. To enhance the efficiency of computationally expensive subband modeling, we introduce a frequency partitioning method inspired by the sensitivity profile of the human peripheral auditory system. Furthermore, we introduce a novel spiking neuron model that can dynamically control the input information integration and forgetting, enhancing the multiscale temporal processing capability of SNN, which is critical for speech denoising. Experiments conducted on the recent Intel Neuromorphic Deep Noise Suppression (N-DNS) Challenge dataset show that the Spiking-FullSubNet surpasses state-of-the-art (SOTA) methods by large margins in terms of both speech quality and energy efficiency metrics. Notably, our system won the championship of the Intel N-DNS Challenge (algorithmic track), opening up a myriad of opportunities for ultralow-power SE at the edge. Our source code and model checkpoints are publicly available at github.com/haoxiangsnr/spiking-fullsubnet.

PMID:40372867 | DOI:10.1109/TNNLS.2025.3566021