ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE人工智能工程应用
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE(英文缩写 ENG APPL ARTIF INTEL),ISSN 0952-1976,eISSN 1873-6769,中文译名:人工智能工程应用 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 7.802 | Q1 |
| 2022 | 8.000 | Q1 |
| 2023 | 7.500 | Q1 |
| 2024 | 8.000 | Q1 |
| 2025 | 9.000 | Q1 |
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 最新收录文献
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1. Automatic classification of circulating blood cell clusters based on multi-channel flow cytometry imaging.
PMID:日期:2025-12-03Circulating blood cell clusters (CCCs) containing red blood cells (RBCs), white blood cells (WBCs), and platelets are significant biomarkers linked to pathological conditions like thrombosis, infection, and inflammation. Flow cytometry, paired with fluorescence staining, is commonly used to analyze these cell clusters, revealing cell morphology and protein profiles. While computational approaches based on machine learning have advanced the automatic analysis of single-cell flow cytometry images, there is a lack of effort to build tools to automatically analyze images containing CCCs. Unlike single cells, cell clusters exhibit irregular shapes and sizes. In addition, these cell clusters often consist of heterogeneous cell types, which require multi-channel staining to identify the specific cell types within the clusters. To address these challenges, we introduce a new computational framework for analyzing CCC images and identifying cell types within clusters. Our framework uses a two-step analysis strategy. First, it categorizes images into cell cluster and non-cluster groups by fine-tuning the You Only Look Once (YOLOv11) model, which outperforms traditional convolutional neural networks (CNNs), such as Vision Transformers (ViT). Then, it identifies cell types by overlaying cluster contours with regions from multi-channel fluorescence stains, thereby minimizing the impact of cell debris and staining artifacts. This approach achieved over 95% accuracy in both cluster classification and cell phenotype identification. In summary, our automated framework effectively analyzes CCC images from flow cytometry, leveraging both bright-field and fluorescence data. Initially tested on blood cells, it holds potential for broader applications, such as analyzing immune and tumor cell clusters, supporting cellular research across various diseases.
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2. Uncertainty- and hardness-weighted loss functions for medical image segmentation.
PMID:日期:2025-11-10Accurate segmentation of medical images is essential for various image processing tasks and is now predominantly achieved using deep learning techniques. However, existing approaches often employ loss functions that fail to account for pixel-level differences in prediction uncertainty or hardness. This limitation frequently results in relatively large segmentation errors, particularly in object boundary regions. To address the limitation, we developed a novel class of uncertainty- / hardness-weighted loss functions by introducing two distinct pixel-wise weighting schemes: probability-guided uncertainty (PGU) and region-enhanced hardness (REH) weights. These weights, derived from the differences between network predictions and their corresponding ground truths, were designed to emphasize challenging pixels while reducing segmentation uncertainties. We validated these loss functions by integrating them with two classical neural networks, i.e., Swin Transformer based U-shape network (Swin-Unet) and V-shape network (V-Net) to segment two- and three-dimensional target objects across four different images datasets, including Retinal Fundus Glaucoma Challenge (REFUGE) dataset, Retinal Vascular Tree Analysis (RETA) dataset, optical coherence tomography (OCT) dataset, and Atria Segmentation Challenge (ASC) dataset. Extensive experiments demonstrated that our developed loss functions outperformed classical losses, such as cross-entropy (CE) and Dice losses, along with their variants, highlighting the effectiveness and generalization of the introduced weighting schemes. The source code is available at https://github.com/wmuLei/uhLoss.
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3. An explainable deep learning approach for sleep staging in sleep apnea patients across all age subgroups from pulse oximetry signals.
3. 一种可解释的深度学习方法，用于从脉搏血氧饱和度信号中对所有年龄亚组的睡眠呼吸暂停患者进行睡眠分期PMID:日期:2025-10-04Deep-learning (DL) approaches have been developed using pulse rate (PR) and blood oxygen saturation (SpO) recordings from pulse oximetry to streamline sleep staging, particularly for obstructive sleep apnea (OSA) patients. However, lack of interpretability and validation across patients from a wide range of ages (children, adolescents, adults, and elderly OSA individuals) are two major concerns. In this study, a DL model based on the U-Net framework (POxi-SleepNet) was tailored to accurately perform 4-class sleep staging (wake, light sleep, deep sleep, and rapid-eye movement sleep) in OSA patients across all age subgroups using PR and SpO signals. An explainable artificial intelligence (XAI) methodology based on semantic segmentation via gradient-weighted class activation mapping (Seg-Grad-CAM) was also applied to quantitatively interpret the time and frequency characteristics of pulse oximetry recordings that influence sleep stage classification. Overnight PR and SpO signals from 17303 sleep studies from six datasets encompassing children, adolescents, adults, and elderly OSA individuals were used. POxi-SleepNet showed high performance for sleep staging in the six databases, with accuracies between 81.5 % and 84.5 % and Cohen's kappa values from 0.726 to 0.779. It also demonstrated greater generalizability than previous studies. XAI analysis showed the key contributions of mean and variability in PR and SpO amplitude, as well as changes in their spectral content across specific frequency bands (0.004-0.020 Hz, 0.020-0.100 Hz, and 0.180-0.400 Hz), for sleep stage classification. These findings indicate that POxi-SleepNet could effectively automate sleep staging and assist in diagnosing OSA across all age groups in clinical settings.
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4. A computation-efficient network with feature aggregation for cancer subtype classification on histopathological images.
4. 一种用于组织病理学图像上癌症亚型分类的高效计算网络，具有特征聚合功能PMID:日期:2025-08-12Histopathology whole-slide images (WSI) capture detailed structural and morphological features of tumor tissue, offering rich histological and molecular information. Deep learning (DL) methods have emerged to assist in automatically examining histopathology WSIs and supporting tumor classification. Traditional DL approaches for WSI images face challenges due to the intrinsic complexity of tumor tissue characteristics and the extremely large image size. Multiple instance learning (MIL) methods have been proposed to address these issues by splitting the WSI images into small non-overlapping tiles and aggregating predictions from selected informative tiles for the final classification outcome. However, MIL methods still face challenges such as the need for accurate pseudo-labels, the risk of losing local information, or the failure to learn explicit class-relevant information. To address these limitations, we propose a novel framework that uses a lightweight convolutional neural network (CNN)-based tile encoder (CTE) to extract local tile features and a Transformer-based feature aggregator (TFA) to fuse local features into a representative global feature for WSI classification. Three key contributions of our framework are as follows. Firstly, we design a two-stage training strategy that decouples a lightweight CTE pre-training (using sparsely sampled tiles for efficiency) and TFA fine-tuning (using all tiles for accuracy). It significantly reduces computational costs compared to existing MIL methods while alleviating local information loss. Secondly, dynamic self-attention-based aggregation is designed in TFA, leveraging the Transformer's self-attention to weigh all local tile features without accurate pseudo-labels. It ensures comprehensive integration of local information from both tumor and ambiguous non-tumor regions to enrich global representations from input WSIs, which benefits the final classification performance. Finally, interpretable saliency maps are generated from TFA attention scores, highlighting histopathologically relevant regions to align model decisions with clinical reasoning. Comprehensive experiments on three cancer subtype datasets demonstrate the effectiveness of our proposed method over existing MIL approaches. We also conduct further investigations into the impacts of various factors on model performance, gaining in-depth insights into our method. Our framework achieves higher classification accuracy while maintaining computational efficiency, making it a promising tool for histopathology image analysis.
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5. A hierarchical network model for the estimate of the energy expenditure in individuals with type 1 diabetes.
5. 用于估计1型糖尿病患者能量消耗的层次网络模型PMID:日期:2025-07-23Total daily energy expenditure (TDEE) is impacted by many medical conditions, such as diabetes. In the case of type 1 diabetes (T1D), individuals need to have an accurate assessment of the energy expenditure in real-time to avoid dietary imbalance, and improve glycemic control. This work proposes a hierarchical Long Short-Term Memory (LSTM)-based modeling approach to predict real-time continuous energy expenditure, expressed as metabolic equivalents (METs), for individuals with T1D on a 24-hour basis by leveraging the step count and heart rate data from a wrist-band smartwatch. To deal with the inter- and intra-individual variability, the proposed model uses three different LSTMs to capture population, activity-type and subject scale information. To evaluate the impact of the components of the hierarchy, the performance of the proposed hierarchical model was assessed at each level. The results show that the combination of population data, such as heart rate and step counts, with individual data in a hierarchical architecture helps to achieve superior prediction performance, than using only individual heart rate and step counts data.Additionally, compared to non-hierarchical modeling, the hierarchical modeling can provide precise and individualized prediction of the METs categories, as it allows the integration of the variation at different levels of the hierarchy. This model can be used to augment current automated insulin delivery (AID) systems to adapt insulin infusion according to the predicted activity intensity and compensate for glycemic perturbations due to exercise.
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6. Synthetic Data-augmented Explainable Vision Transformer for Colorectal Cancer Diagnosis via Surface Tactile Imaging.
6. 合成数据增强的可解释视觉Transformer用于通过表面触觉成像进行结直肠癌诊断PMID:日期:2025-03-29In this work, we present a synthetic data augmented explainable Vision Transformer (ViT) framework designed for the informed and intuitive early diagnosis of colorectal cancer (CRC) polyps. The framework uses textural images - generated by our recently developed vision-based tactile sensor (called HySenSe) and augmented by synthetically generated images from a diffusion model pipeline, to output class-based probabilities of potential CRC polyp types. Additionally, it provides local relevancy-based heatmaps to assist clinicians by highlighting key areas of interest in the tactile images representing CRC polyp textures. We benchmark each aspect of this framework through: (i) Inception Scores for the synthetic images generated by the diffusion pipeline, (ii) Performance evaluation and sensitivity analyses on the effects of synthetic data addition on model generalizability compared with other state-of-the-art architectures, (iii) Dimensionality reduction techniques to confirm the suitability of synthetically generated images, and (iv) Comparison of two independent approaches visualizing explainability.
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7. Interpretable manifold learning for T-wave alternans assessment with electrocardiographic imaging.
7. 用于心电图成像中T波交替评估的可解释流形学习PMID:日期:2025-01-17T-wave alternans (TWA) is a biomarker for sudden cardiac death prediction, characterized by subtle variations in the amplitude or morphology of consecutive T-waves in electrocardiographic studies. Electrocardiographic imaging (ECGI) offers increased spatial resolution, enabling TWA distribution analysis across the epicardium. However, existing TWA estimation methods disregard ECGI spatial information by analyzing each signal independently. To address this gap, we present a novel, subject-specific, interpretable manifold learning-based TWA estimation method tailored to ECGI. First, Uniform Manifold Approximation and Projection (UMAP) reduces input data dimensions. Second, the Louvain algorithm detects communities and identifies the TWA-dominant community. Finally, the location of this community and the rest of the communities is compared, and a Bootstrap-based TWA classifier is applied. A customized Shapley additive explanations method was developed to identify the signal segments most affecting the algorithm decisions to enhance explainability. Reducing the input data to 18 dimensions improved the separation of the TWA-dominant community, with an average normalized distance of 0.28. The Bootstrap analysis showed that the TWA-dominant community had a distance metric up to 0.2 above the confidence interval upper limit. The TWA-dominant community input signals showed different TWA patterns, namely, hump-shaped and amplitude-shifted TWA, and the interpretability algorithm revealed that UMAP focuses on them when projecting points into the latent space. Our method achieved maximum accuracy in subjects with known outcomes and made consistent patient decisions based on input signals. This study introduces the first ECGI-specific TWA detection method. Its subject-specific nature enables the extraction of individual-specific characteristics, offering personalized diagnostic insights.
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8. Effective Prediction of Drug Transport in a Partially Liquefied Vitreous Humor: Physics-informed Neural Network Modeling for Irregular Liquefaction Geometry.
PMID:日期:2024-09-19As the medium for intravitreal drug delivery, the vitreous body can significantly influence drug delivery because of various possible liquefaction geometries. This work innovatively proposes a varying-porosity approach that is capable of solving the pressure and velocity fields in the heterogeneous vitreous with randomly-shaped liquefaction geometry, validated with a finite difference model. Doing so enables patient-specific treatment for intravitreal drug delivery and can significantly improve treatment efficacy. A physics-informed neural network (PINN) model is also established for the simulation, and three cases are used for validation. Despite limited information, the PINN model, together with the varying-porosity approach, captured fluid and drug transport in the partially liquefied vitreous. This opens the possibility for optimizing intravitreal drug delivery based on ultrasonography in clinical practice.
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9. Topological Persistence Guided Knowledge Distillation for Wearable Sensor Data.
PMID:日期:2023-12-20Deep learning methods have achieved a lot of success in various applications involving converting wearable sensor data to actionable health insights. A common application areas is activity recognition, where deep-learning methods still suffer from limitations such as sensitivity to signal quality, sensor characteristic variations, and variability between subjects. To mitigate these issues, robust features obtained by topological data analysis (TDA) have been suggested as a potential solution. However, there are two significant obstacles to using topological features in deep learning: (1) large computational load to extract topological features using TDA, and (2) different signal representations obtained from deep learning and TDA which makes fusion difficult. In this paper, to enable integration of the strengths of topological methods in deep-learning for time-series data, we propose to use two teacher networks - one trained on the raw time-series data, and another trained on persistence images generated by TDA methods. These two teachers are jointly used to distill a single student model, which utilizes only the raw time-series data at test-time. This approach addresses both issues. The use of KD with multiple teachers utilizes complementary information, and results in a compact model with strong supervisory features and an integrated richer representation. To assimilate desirable information from different modalities, we design new constraints, including orthogonality imposed on feature correlation maps for improving feature expressiveness and allowing the student to easily learn from the teacher. Also, we apply an annealing strategy in KD for fast saturation and better accommodation from different features, while the knowledge gap between the teachers and student is reduced. Finally, a robust student model is distilled, which can at test-time uses only the time-series data as an input, while implicitly preserving topological features. The experimental results demonstrate the effectiveness of the proposed method on wearable sensor data. The proposed method shows 71.74% in classification accuracy on GENEActiv with WRN16-1 (1D CNNs) student, which outperforms baselines and takes much less processing time (less than 17 sec) than teachers on 6k testing samples.
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10. COVID-19 symptom identification using Deep Learning and hardware emulated systems.
PMID:日期:2023-06-28The COVID-19 pandemic disrupted regular global activities in every possible way. This pandemic, caused by the transmission of the infectious Coronavirus, is characterized by main symptoms such as fever, fatigue, cough, and loss of smell. A current key focus of the scientific community is to develop automated methods that can effectively identify COVID-19 patients and are also adaptable for foreseen future virus outbreaks. To classify COVID-19 suspects, it is required to use contactless automatic measurements of more than one symptom. This study explores the effectiveness of using Deep Learning combined with a hardware-emulated system to identify COVID-19 patients in Sri Lanka based on two main symptoms: cough and shortness of breath. To achieve this, a Convolutional Neural Network (CNN) based on Transfer Learning was employed to analyze and compare the features of a COVID-19 cough with other types of coughs. Real-time video footage was captured using a FLIR C2 thermal camera and a web camera and subsequently processed using OpenCV image processing algorithms. The objective was to detect the nasal cavities in the video frames and measure the breath cycles per minute, thereby identifying instances of shortness of breath. The proposed method was first tested on crowd-sourced datasets (Coswara, Coughvid, ESC-50, and a dataset from Kaggle) obtained online. It was then applied and verified using a dataset obtained from local hospitals in Sri Lanka. The accuracy of the developed methodologies in diagnosing cough resemblance and recognizing shortness of breath was found to be 94% and 95%, respectively.