IEEE J Biomed Health Inform. 2025 Mar 5;PP. doi: 10.1109/JBHI.2025.3548263. Online ahead of print.

ABSTRACT

Bronchoalveolar lavage fluid (BALF) is a liquid obtained from the alveoli and bronchi, often used to study pulmonary diseases. So far, proteomic analyses have identified over three thousand proteins in BALF. However, the comprehensive characterization of these proteins remains challenging due to their complexity and technological limitations. This paper presented a novel deep learning framework called SecProGNN, designed to predict secretory proteins in BALF. Firstly, SecProGNN represented proteins as graph-structured data, with amino acids connected based on their interactions. Then, these graphs were processed through graph neural networks (GNNs) model to extract graph features. Finally, the extracted feature vectors were fed into a multi-layer perceptron (MLP) module to predict BALF secreted proteins. Additionally, by utilizing SecProGNN, we investigated potential biomarkers for lung adenocarcinoma and identified 16 promising candidates that may be secreted into BALF.

PMID:40042949 | DOI:10.1109/JBHI.2025.3548263

Elife. 2025 Mar 5;13:RP98485. doi: 10.7554/eLife.98485.

ABSTRACT

Quantitative information about synaptic transmission is key to our understanding of neural function. Spontaneously occurring synaptic events carry fundamental information about synaptic function and plasticity. However, their stochastic nature and low signal-to-noise ratio present major challenges for the reliable and consistent analysis. Here, we introduce miniML, a supervised deep learning-based method for accurate classification and automated detection of spontaneous synaptic events. Comparative analysis using simulated ground-truth data shows that miniML outperforms existing event analysis methods in terms of both precision and recall. miniML enables precise detection and quantification of synaptic events in electrophysiological recordings. We demonstrate that the deep learning approach generalizes easily to diverse synaptic preparations, different electrophysiological and optical recording techniques, and across animal species. miniML provides not only a comprehensive and robust framework for automated, reliable, and standardized analysis of synaptic events, but also opens new avenues for high-throughput investigations of neural function and dysfunction.

PMID:40042890 | DOI:10.7554/eLife.98485

MAGMA. 2025 Mar 5. doi: 10.1007/s10334-025-01233-7. Online ahead of print.

ABSTRACT

OBJECTIVE: This study presents a novel deep learning-based framework for precise brain MR region segmentation, aiming to identify the location and the shape details of different anatomical structures within the brain.

MATERIALS AND METHODS: The approach uses a two-stage 3D segmentation technique on a dataset of adult subjects, including cognitively normal participants and individuals with cognitive decline. Stage 1 employs a 3D U-Net to segment 13 brain regions, achieving a mean DSC of 0.904 ± 0.060 and a mean HD95 of 1.52 ± 1.53 mm (a mean DSC of 0.885 ± 0.065 and a mean HD95 of 1.57 ± 1.35 mm for smaller parts). For challenging regions like hippocampus, thalamus, cerebrospinal fluid, amygdala, basal ganglia, and corpus callosum, Stage 2 with SegResNet refines segmentation, improving mean DSC to 0.921 ± 0.048 and HD95 to 1.17 ± 0.69 mm.

RESULTS: Statistical analysis reveals significant improvements (p-value < 0.001) for these regions, with DSC increases ranging from 1.3 to 3.2% and HD95 reductions of 0.06-0.33 mm. Comparisons with recent studies highlight the superior performance of the performed method.

DISCUSSION: The inclusion of a second stage for refining the segmentation of smaller regions demonstrates substantial improvements, establishing the framework's potential for precise and reliable brain region segmentation across diverse cognitive groups.

PMID:40042762 | DOI:10.1007/s10334-025-01233-7

Eur Radiol. 2025 Mar 5. doi: 10.1007/s00330-025-11468-6. Online ahead of print.

ABSTRACT

OBJECTIVES: Incidental airway tumors are rare and can easily be overlooked on chest CT, especially at an early stage. Therefore, we developed and assessed a deep learning-based artificial intelligence (AI) system for detecting and localizing airway nodules.

MATERIALS AND METHODS: At a single academic hospital, we retrospectively analyzed cancer diagnoses and radiology reports from patients who received a chest or chest-abdomen CT scan between 2004 and 2020 to find cases presenting as airway nodules. Primary cancers were verified through bronchoscopy with biopsy or cytologic testing. The malignancy status of other nodules was confirmed with bronchoscopy only or follow-up CT scans if such evidence was unavailable. An AI system was trained and evaluated with a ten-fold cross-validation procedure. The performance of the system was assessed with a free-response receiver operating characteristic curve.

RESULTS: We identified 160 patients with airway nodules (median age of 64 years [IQR: 54-70], 58 women) and added a random sample of 160 patients without airway nodules (median age of 60 years [IQR: 48-69], 80 women). The sensitivity of the AI system was 75.1% (95% CI: 67.6-81.6%) for detecting all nodules with an average number of false positives per scan of 0.25 in negative patients and 0.56 in positive patients. At the same operating point, the sensitivity was 79.0% (95% CI: 70.4-86.6%) for the subset of tumors. A subgroup analysis showed that the system detected the majority of subtle tumors.

CONCLUSION: The AI system detects most airway nodules on chest CT with an acceptable false positive rate.

KEY POINTS: Question Incidental airway tumors are rare and are susceptible to being overlooked on chest CT. Findings An AI system can detect most benign and malignant airway nodules with an acceptable false positive rate, including nodules that have very subtle features. Clinical relevance An AI system shows potential for supporting radiologists in detecting airway tumors.

PMID:40042650 | DOI:10.1007/s00330-025-11468-6

Eur Radiol. 2025 Mar 5. doi: 10.1007/s00330-025-11473-9. Online ahead of print.

ABSTRACT

OBJECTIVES: This study evaluated and compared the effectiveness of various predictive models for forecasting occult lymph node metastasis (LNM) in tongue squamous cell carcinoma (TSCC) patients.

METHODS: In this retrospective diagnostic experiment, 268 patients were recruited from three medical centers. Based on the different hospitals from which the patients were recruited, they were divided into a training set, an internal testing set, and two external testing sets, comprising 107, 53, 63, and 45 patients, respectively. Several predictive models were developed using patients' contrast-enhanced magnetic resonance imaging (CEMRI), including two-dimensional deep learning (2D DL), conventional radiomics (C-radiomics), and intratumoral heterogeneity radiomics (ITH-radiomics). Univariate and multivariate logistic regression analyses were conducted on the clinical data. Finally, two fusion strategies were used to construct the model.

RESULTS: The ITH-radiomics model exhibited superior discriminative power compared to C-radiomics model. The late fusion model had the highest area under the curve (AUC) across all test sets (0.81-0.85). Compared to the late fusion model, the AUC values for the early fusion, 2D DL, C-radiomics, and ITH-radiomics models in the test sets ranged from 0.77 to 0.82, 0.64 to 0.81, 0.66 to 0.77, and 0.77 to 0.80, respectively. Additionally, the late fusion model demonstrated the highest accuracy (76-89%) and specificity (87-100%) across the test sets.

CONCLUSIONS: The evaluation of the models' effectiveness revealed that the decision-based late fusion model, which integrated 2D DL, C-radiomics, ITH-radiomics, and clinical data, achieved the best results. This predictive approach can more accurately assess patients' conditions and aid in selecting surgical plans.

KEY POINTS: Question How well does fusing multiple models work for predicting occult lymph node metastasis in patients with tongue squamous cell carcinoma? Findings The late fusion model, incorporating two-dimensional deep learning, conventional-radiomics, intratumoral heterogeneity-radiomics, and clinical features, achieved the best results compared to each individual model. Clinical relevance Patients with a high intratumoral heterogeneity-radiomics index exhibit an increased risk of occult lymph node metastasis in tongue squamous cell carcinoma patients, which showed that the late fusion model achieves superior predictive performance compared to the early fusion model.

PMID:40042648 | DOI:10.1007/s00330-025-11473-9

Environ Geochem Health. 2025 Mar 5;47(4):101. doi: 10.1007/s10653-025-02413-z.

ABSTRACT

With increasing scholarly focus on the ramifications of land use changes on water quality, although substantial research has been undertaken, the findings demonstrate pronounced spatial variability and the heterogeneity of research methodologies. To address this critical gap, this review offers a rigorous evaluation of the strengths and limitations of current research methodologies, providing targeted recommendations for refinement. It systematically assesses the existing body of literature concerning the influence of land use changes on water quality, with particular emphasis on the spatial heterogeneity of research results and the uniformity of employed methodologies. Despite variations in geographical contexts and research subjects, the methodological paradigms remain largely consistent, typically encompassing the acquisition and analysis of water quality and land use data, the delineation of buffer zones, and the application of correlation and regression analyses. However, these approaches encounter limitations in addressing regional disparities, nonlinear interactions, and real-time monitoring complexities. The review advocates for methodological advancements, such as the integration of automated monitoring systems and IoT technologies, alongside the fusion of deep learning algorithms with remote sensing techniques, to enhance both the precision and efficiency of data collection. Furthermore, it recommends the standardization of buffer zone delineation, the reinforcement of foundational water quality assessments, and the utilization of catchment-scale analyses to more accurately capture the influence of land use changes on water quality. Future inquiries should prioritize the development of interdisciplinary ecological models to elucidate the interaction and feedback mechanisms between land use, water quality, and climate change.

PMID:40042544 | DOI:10.1007/s10653-025-02413-z

Alzheimers Dement. 2025 Mar;21(3):e14601. doi: 10.1002/alz.14601.

ABSTRACT

INTRODUCTION: The utility of retinal photography-derived aging biomarkers for predicting cognitive decline remains under-explored.

METHODS: A memory-clinic cohort in Singapore was followed-up for 5 years. RetiPhenoAge, a retinal aging biomarker, was derived from retinal photographs using deep-learning. Using competing risk analysis, we determined the associations of RetiPhenoAge with cognitive decline and dementia, with the UK Biobank utilized as the replication cohort. The associations of RetiPhenoAge with MRI markers(cerebral small vessel disease [CSVD] and neurodegeneration) and its underlying plasma proteomic profile were evaluated.

RESULTS: Of 510 memory-clinic subjects(N = 155 cognitive decline), RetiPhenoAge associated with incident cognitive decline (subdistribution hazard ratio [SHR] 1.34, 95% confidence interval [CI] 1.10-1.64, p = 0.004), and incident dementia (SHR 1.43, 95% CI 1.02-2.01, p = 0.036). In the UK Biobank (N = 33 495), RetiPhenoAge similarly predicted incident dementia (SHR 1.25, 95% CI 1.09-1.41, p = 0.008). RetiPhenoAge significantly associated with CSVD, brain atrophy, and plasma proteomic signatures related to aging.

DISCUSSION: RetiPhenoAge may provide a non-invasive prognostic screening tool for cognitive decline and dementia.

HIGHLIGHTS: RetiPhenoAge, a retinal aging marker, was studied in an Asian memory clinic cohort. Older RetiPhenoAge predicted future cognitive decline and incident dementia. It also linked to neuropathological markers, and plasma proteomic profiles of aging. UK Biobank replication found that RetiPhenoAge predicted 12-year incident dementia. Future studies should validate RetiPhenoAge as a prognostic biomarker for dementia.

PMID:40042460 | DOI:10.1002/alz.14601

Microb Biotechnol. 2025 Mar;18(3):e70121. doi: 10.1111/1751-7915.70121.

ABSTRACT

Antimicrobial peptides (AMPs) face stability and toxicity challenges in clinical use. Stapled modification enhances their stability and effectiveness, but its application in peptide design is rarely reported. This study built ten prediction models for stapled AMPs using deep and machine learning, tested their accuracy with an independent data set and wet lab experiments, and characterised stapled loop structures using structural, sequence and amino acid descriptors. AlphaFold improved stapled peptide structure prediction. The support vector machine model performed best, while two deep learning models achieved the highest accuracy of 1.0 on an external test set. Designed cysteine- and lysine-stapled peptides inhibited various bacteria with low concentrations and showed good serum stability and low haemolytic activity. This study highlights the potential of the deep learning method in peptide modification and design.

PMID:40042163 | DOI:10.1111/1751-7915.70121

Adv Sci (Weinh). 2025 Mar 5:e2415808. doi: 10.1002/advs.202415808. Online ahead of print.

ABSTRACT

Current imaging technologies are limited in their capability to simultaneously capture intracellular and extracellular dynamics in a spatially and temporally resolved manner. This study presents a multimodal imaging system that integrates nanoplasmonic sensing with multichannel fluorescence imaging to concomitantly analyze intracellular and extracellular processes in space and time at the single-cell level. Utilizing a highly sensitive gold nanohole array biosensor, the system provides label-free and real-time monitoring of extracellular secretion, while implementing nanoplasmonic-compatible multichannel fluorescence microscopy enables to visualize the interconnected intracellular activities. Combined with deep-learning-assisted image processing, this integrated approach allows multiparametric and simultaneous study of various cellular constituents in hundreds of individual cells with subcellular spatial and minute-level temporal resolution over extended periods of up to 20 h. The system's utility is demonstrated by characterizing a range of secreted biomolecules and fluorescence toolkits across three distinct applications: visualization of secretory behaviors along with subcellular organelles and metabolic processes, concurrent monitoring of protein expression and secretion, and assessment of cell cycle phases alongside their corresponding secretory profiles. By offering comprehensive insights, the multifunctional approach is expected to enhance holistic readouts of biological systems, facilitating new discoveries in both fundamental and translational sciences.

PMID:40042114 | DOI:10.1002/advs.202415808

Clin Chem Lab Med. 2025 Mar 6. doi: 10.1515/cclm-2024-1219. Online ahead of print.

NO ABSTRACT

PMID:40042089 | DOI:10.1515/cclm-2024-1219

ACS Sens. 2025 Mar 5. doi: 10.1021/acssensors.4c03472. Online ahead of print.

ABSTRACT

Anterior talofibular ligament (ATFL) sprain is one of the most prevalent sports-related injuries, so proper evaluation of ligament sprains is critical for treatment options. However, existing tests suffer from a lack of standardized quantitative evaluation criteria, interindividual variability, incompatible materials, or risks of infection. Although advanced medical diagnostic methods already have been using noninvasive, portable, and wearable diagnostic electronics, these devices have insufficient adhesion to accurately respond to internal body injuries. Therefore, we propose a high-adhesive hydrogel-based strain sensor made from gelatin, cellulose nanofiber (CNF), and cross-linked poly(acrylic acid) grafted with N-hydrosuccinimide ester. The adhesive strain sensor, with excellent conformability and stretchability, firmly adheres to the skin, making it suitable for accurately evaluating the severity of anterior talofibular ligament sprain. Its strong adhesive (up to 192 kPa) can adapt to the surface characterization of ankles. The high-adhesive hydrogel-based strain sensor has a high tensile strength (680%) and achieves a high gauge factor (GF) of 8.29. Simultaneously, it also presents a 40 μm ultralow detection limit. Additionally, after a deep learning model was integrated to improve sensing accuracy, the system achieved a diagnostic accuracy of 95%, significantly surpassing the magnetic resonance imaging (MRI) gold standard of 81.1%.

PMID:40042081 | DOI:10.1021/acssensors.4c03472

Patterns (N Y). 2025 Feb 6;6(2):101177. doi: 10.1016/j.patter.2025.101177. eCollection 2025 Feb 14.

ABSTRACT

Balancing prediction accuracy, model interpretability, and domain generalization (also known as [a.k.a.] out-of-distribution testing/evaluation) is a central challenge in machine learning. To assess this challenge, we took 120 interpretable and 166 opaque models from 77,640 tuned configurations, complemented with ChatGPT, 3 probabilistic language models, and Vec2Read. The models first performed text classification to derive principles of textual complexity (task 1) and then generalized these to predict readers' appraisals of processing difficulty (task 2). The results confirmed the known accuracy-interpretability trade-off on task 1. However, task 2's domain generalization showed that interpretable models outperform complex, opaque models. Multiplicative interactions further improved interpretable models' domain generalization incrementally. We advocate for the value of big data for training, complemented by (1) external theories to enhance interpretability and guide machine learning and (2) small, well-crafted out-of-distribution data to validate models-together ensuring domain generalization and robustness against data shifts.

PMID:40041855 | PMC:PMC11873011 | DOI:10.1016/j.patter.2025.101177

Health Sci Rep. 2025 Mar 2;8(3):e70498. doi: 10.1002/hsr2.70498. eCollection 2025 Mar.

ABSTRACT

BACKGROUND AND AIMS: The increasing elderly population presents significant challenges for healthcare systems, necessitating innovative solutions for continuous health monitoring. This study develops and validates an IoT-based elderly monitoring system designed to enhance the quality of life for elderly people. The system features a robust Android-based user interface integrated with the Firebase cloud platform, ensuring real-time data collection and analysis. In addition, a supervised machine learning technology is implemented to conduct prediction task of the observed user whether in "stable" or "not stable" condition based on real-time parameter.

METHODS: The system architecture adopts the IoT layer including physical layer, network layer, and application layer. Device validation is conducted by involving six participants to measure the real-time data of heart-rate, oxygen saturation, and body temperature, then analysed by mean average percentage error (MAPE) to define error rate. A comparative experiment is conducted to define the optimal supervised machine learning model to be deployed into the system by analysing evaluation metrics. Meanwhile, the user satisfaction aspect evaluated by the terms of usability, comfort, security, and effectiveness.

RESULTS: IoT-based elderly health monitoring system has been constructed with a MAPE of 0.90% across the parameters: heart-rate (1.68%), oxygen saturation (0.57%), and body temperature (0.44%). In machine learning experiment indicates XGBoost model has the optimal performance based on the evaluation metrics of accuracy and F1 score which generates 0.973 and 0.970, respectively. In user satisfaction aspect based on usability, comfort, security, and effectiveness achieving a high rating of 86.55%.

CONCLUSION: This system offers practical applications for both elderly users and caregivers, enabling real-time monitoring of health conditions. Future enhancements may include integration with artificial intelligence technologies such as machine learning and deep learning to predict health conditions from data patterns, further improving the system's capabilities and effectiveness in elderly care.

PMID:40041774 | PMC:PMC11873372 | DOI:10.1002/hsr2.70498

PNAS Nexus. 2025 Feb 20;4(2):pgaf054. doi: 10.1093/pnasnexus/pgaf054. eCollection 2025 Feb.

ABSTRACT

Discovering manufacturing processes has been largely experienced-based. We propose a shift to a systematic approach driven by dependencies between energy inputs and performance outputs. Uncovering these dependencies across diverse process classes requires a universal language that characterizes process inputs and performances. Traditional manufacturing languages, with their individualized syntax and terminology, hinder the characterization across varying length scales and energy inputs. To enable the evaluation of process dependencies, we propose a broad manufacturing language that facilitates the characterization of diverse process classes, which include energy inputs, tool-material interactions, material compatibility, and performance outputs. We analyze the relationships between these characteristics by constructing a dataset of over 50 process classes, which we use to train a variational autoencoder (VAE) model. This generative model encodes our dataset into a 2D latent space, where we can explore, select, and generate processes based on desired performances and retrieve the corresponding process characteristics. After verifying the dependencies derived from the VAE model match with existing knowledge on manufacturing processes, we demonstrate the usefulness of using the model to discover new potential manufacturing processes through three illustrative cases.

PMID:40041620 | PMC:PMC11878556 | DOI:10.1093/pnasnexus/pgaf054

Hortic Res. 2024 Nov 20;12(2):uhae319. doi: 10.1093/hr/uhae319. eCollection 2025 Feb.

ABSTRACT

Genomic prediction for multiple environments can aid the selection of genotypes suited to specific soil and climate conditions. Methodological advances allow effective integration of phenotypic, genomic (additive, nonadditive), and large-scale environmental (enviromic) data into multi-environmental genomic prediction models. These models can also account for genotype-by-environment interaction, utilize alternative relationship matrices (kernels), or substitute statistical approaches with deep learning. However, the application of multi-environmental genomic prediction in apple remained limited, likely due to the challenge of building multi-environmental datasets and structurally complex models. Here, we applied efficient statistical and deep learning models for multi-environmental genomic prediction of eleven apple traits with contrasting genetic architectures by integrating genomic- and enviromic-based model components. Incorporating genotype-by-environment interaction effects into statistical models improved predictive ability by up to 0.08 for nine traits compared to the benchmark model. This outcome, based on Gaussian and Deep kernels, shows these alternatives can effectively substitute the standard genomic best linear unbiased predictor (G-BLUP). Including nonadditive and enviromic-based effects resulted in a predictive ability very similar to the benchmark model. The deep learning approach achieved the highest predictive ability for three traits with oligogenic genetic architectures, outperforming the benchmark by up to 0.10. Our results demonstrate that the tested statistical models capture genotype-by-environment interactions particularly well, and the deep learning models efficiently integrate data from diverse sources. This study will foster the adoption of multi-environmental genomic prediction to select apple cultivars adapted to diverse environmental conditions, providing an opportunity to address climate change impacts.

PMID:40041603 | PMC:PMC11879405 | DOI:10.1093/hr/uhae319

Comput Struct Biotechnol J. 2025 Feb 14;27:631-638. doi: 10.1016/j.csbj.2025.02.007. eCollection 2025.

ABSTRACT

DNA methylations, such as 5-methylcytosine (5mC), are crucial in biological processes, and aberrant methylations are strongly linked to various human diseases. Genomic 5mC is not randomly distributed but exhibits a strong association with genomic sequences. Thus, various computational methods were developed to predict 5mC status based on DNA sequences. These methods generated promising achievements and overcome the limitations of experimental approaches. However, few studies have comprehensively investigated the dependency of 5mC on genomic sequences, and most existing methods focus on specific genomic regions. In this work, we introduce Deep5mC, a deep learning transformer-based method designed to predict 5mC methylations. Deep5mC leverages long-range dependencies within genomic sequences to estimate the probability of cytosine methylations. Through cross-chromosome evaluation, Deep5mC achieves Matthew's correlation coefficient over 0.86 and F1-score over 0.93, substantially outperforming state-of-the-art methods. Deep5mC not only confirms the influence of long-range sequence context on 5mC prediction but also paves the way for further studying 5mC-sequence dependency across species and in human diseases.

PMID:40041569 | PMC:PMC11879672 | DOI:10.1016/j.csbj.2025.02.007

Front Med (Lausanne). 2025 Feb 18;12:1561485. doi: 10.3389/fmed.2025.1561485. eCollection 2025.

NO ABSTRACT

PMID:40041465 | PMC:PMC11876553 | DOI:10.3389/fmed.2025.1561485

Proc Mach Learn Res. 2024 Jun;248:182-197.

ABSTRACT

Electronic Health Records (EHRs) contain rich patient information and are crucial for clinical research and practice. In recent years, deep learning models have been applied to EHRs, but they often rely on massive features, which may not be readily available for all patients. We propose HTP-Star, which leverages hypergraph structures with a pretrain-then-finetune framework for modeling EHR data, enabling seamless integration of additional features. Additionally, we design two techniques, namely (1) Smoothness-inducing Regularization and (2) Group-balanced Reweighting, to enhance the model's robustness during finetuning. Through experiments conducted on two real EHR datasets, we demonstrate that HTP-Star consistently outperforms various baselines while striking a balance between patients with basic and extra features.

PMID:40041452 | PMC:PMC11876795

Proc IEEE Int Conf Big Data. 2024 Dec;2024:4941-4945. doi: 10.1109/bigdata62323.2024.10825319.

ABSTRACT

Predicting seizures ahead of time will have a significant positive clinical impact for people with epilepsy. Advances in machine learning/artificial intelligence (ML/AI) has provided us the tools needed to perform such predictive tasks. To date, advanced deep learning (DL) architectures such as the convolutional neural network (CNN) and long short-term memory (LSTM) have been used with mixed results. However, highly connected activity exhibited by epileptic seizures necessitates the design of more complex ML techniques which can better capture the complex interconnected neurological processes. Other challenges include the variability of EEG sensor data quality, different epilepsy and seizure profiles, lack of annotated datasets and absence of ML-ready benchmarks. In addition, successful models will need to perform inference in almost real-time using limited hardware compute-capacity. To address these challenges, we propose a lightweight architecture, called ESPFormer, whose novelty lies in the simple and smaller model-size and a lower computational load footprint needed to infer in real-time compared to other works in the literature. To quantify the performance of this lightweight model, we compared its performance with a custom-designed residual neural network (ResNet), a pre-trained vision transformer (ViT) and a pre-trained large-language model (LLM). We tested ESPFormer on MLSPred-Bench which is the largest patient-independent seizure prediction dataset comprising 12 benchmarks. Our results demonstrate that ESPFormer provides the best performance in terms of prediction accuracy for 4/12 benchmarks with an average improvement of 2.65% compared to the LLM, 3.35% compared to the ViT and 17.65% compared to the ResNet - and comparable results for other benchmarks. Our results indicate that lightweight transformer architecture may outperform resource-intensive LLM based models for real-time EEG-based seizure predictions.

PMID:40041397 | PMC:PMC11877310 | DOI:10.1109/bigdata62323.2024.10825319

Front Plant Sci. 2025 Feb 18;16:1527508. doi: 10.3389/fpls.2025.1527508. eCollection 2025.

ABSTRACT

Currently, potato defect sorting primarily relies on manual labor, which is not only inefficient but also prone to bias. Although automated sorting systems offer a potential solution by integrating potato detection models, real-time performance remains challenging due to the need to balance high accuracy and speed under limited resources. This study presents an enhanced version of the YOLO v5s model, named YOLO v5s-ours, specifically designed for real-time detection of potato defects. By integrating Coordinate Attention (CA), Adaptive Spatial Feature Fusion (ASFF), and Atrous Spatial Pyramid Pooling (ASPP) modules, the model significantly improves detection accuracy while maintaining computational efficiency. The model achieved 82.0% precision, 86.6% recall, 84.3% F1-Score and 85.1% mean average precision across six categories - healthy, greening, sprouting, scab, mechanical damage, and rot - marking improvements of 24.6%, 10.5%, 19.4%, and 13.7%, respectively, over the baseline model. Although memory usage increased from 13.7 MB to 23.3 MB and frame rate slightly decreased to 30.7 fps, the accuracy gains ensure the model's suitability for practical applications. The research provides significant support for the development of automated potato sorting systems, advancing agricultural efficiency, particularly in real-time applications, by overcoming the limitations of traditional methods.

PMID:40041023 | PMC:PMC11876418 | DOI:10.3389/fpls.2025.1527508