Intelligence-based medicine最新文献_第3页

Early prediction of sepsis using an XGBoost model with single time-point non-invasive vital signs and its correlation with C-reactive protein and procalcitonin: A multi-center study 基于单时间点无创生命体征的XGBoost模型早期预测脓毒症及其与c反应蛋白和降钙素原的相关性：一项多中心研究

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100242

Albert C. Yang , Wei-Ming Ma , Dung-Hung Chiang , Yi-Ze Liao , Hsien-Yung Lai , Shu-Chuan Lin , Mei-Chin Liu , Kai-Ting Wen , Tzong-Huei Lin , Wen-Xiang Tsai , Jun-Ding Zhu , Ting-Yu Chen , Hung-Fu Lee , Pei-Hung Liao , Huey-Wen Yien , Chien-Ying Wang

We aimed to develop an early warning system to predict sepsis based solely on single time-point and non-invasive vital signs, and to evaluate its correlation with related biomarkers, namely C-reactive protein (CRP) and Procalcitonin (PCT). We utilized retrospective data from Physionet and four medical centers in Taiwan, encompassing a total of 46,184 Intensive Care Unit (ICU) patients, to develop and validate a machine learning algorithm based on XGBoost for predicting sepsis. The model was specifically designed to use non-invasive vital signs captured at a single time point, The correlation between sepsis AI prediction model and levels of CRP and PCT was evaluated. The developed model demonstrated balanced performance across various datasets, with an average recall of 0.908 and precision of 0.577. The model's performance was further validated by the independent dataset from Cheng-Hsin General Hospital (recall: 0.986, precision: 0.585). Temperature, systolic blood pressure, and respiration rate were the top contributing predictors in the model. A significant correlation was observed between the model's sepsis predictions and elevated CRP levels, while PCT showed a less consistent pattern. Our approach, combining AI algorithms with vital sign data and its clinical relevance to CRP level, offers a more precise and timely sepsis detection, with the potential to improve care in emergency and critical care settings.

我们的目标是开发一种仅基于单一时间点和无创生命体征的脓毒症预警系统，并评估其与相关生物标志物，即c反应蛋白（CRP）和降钙素原（PCT）的相关性。我们利用来自Physionet和台湾四家医疗中心的回顾性数据，包括46,184名重症监护病房（ICU）患者，开发并验证了基于XGBoost的机器学习算法，用于预测败血症。该模型专门设计用于使用在单个时间点捕获的无创生命体征，评估脓毒症AI预测模型与CRP和PCT水平的相关性。开发的模型在各种数据集上表现出平衡的性能，平均召回率为0.908，精度为0.577。通过独立数据集验证模型的有效性（召回率：0.986，精度：0.585）。温度、收缩压和呼吸速率是模型中最重要的预测因子。在模型的脓毒症预测与CRP水平升高之间观察到显著的相关性，而PCT表现出不太一致的模式。我们的方法将人工智能算法与生命体征数据及其与CRP水平的临床相关性相结合，提供了更精确和及时的败血症检测，有可能改善急诊和重症监护环境的护理。

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引用次数: 0

An integrated machine learning based adaptive error minimization framework for Alzheimer's stage identification 基于集成机器学习的阿尔茨海默氏症阶段识别自适应误差最小化框架

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100243

Fahima Hossain, Rajib Kumar Halder, Mohammed Nasir Uddin

Alzheimer's disease (AD) is a degenerative neurological condition that impairs cognitive functioning. Early detection is critical for slowing disease progression and limiting brain damage. Although machine learning and deep learning models help identify Alzheimer's disease, their accuracy and efficiency are widely questioned. This study provides an integrated system for classifying four AD phases from 6400 MRI scans using pre-trained neural networks and machine learning classifiers. Preprocessing steps include noise removal, image enhancement (AGCWD, Bilateral Filter), and segmentation. Intensity normalization and data augmentation methods are applied to improve model generalization. Two models are developed: the first employs pre-trained neural net-works (VGG16, VGG19, DenseNet201, ResNet50, EfficientNetV7, InceptionV3, InceptionResNetV2, and MobileNet) for both feature extraction and classification. In contrast, the second integrates features from these networks with machine learning classifiers (XGBoost, Random Forest, SVM, KNN, Gradient Boosting, AdaBoost, Decision Tree, Linear Discriminant Analysis, Logistic Regression, and Multilayer Perceptron). The second model incorporates an adaptive error minimization sys-tem for enhanced accuracy. VGG16 achieved the highest accuracy (99.61 % training and 97.94 % testing), whereas VGG19+MLP with adaptive error minimization achieved 97.08 %, exhibiting superior AD classification ability.

阿尔茨海默病（AD）是一种退化的神经系统疾病，损害认知功能。早期发现对于减缓疾病进展和限制脑损伤至关重要。虽然机器学习和深度学习模型有助于识别阿尔茨海默病，但它们的准确性和效率受到广泛质疑。本研究提供了一个集成系统，使用预训练的神经网络和机器学习分类器从6400个MRI扫描中对四个AD阶段进行分类。预处理步骤包括去噪、图像增强（AGCWD，双边滤波）和分割。采用强度归一化和数据增强方法提高模型泛化能力。开发了两个模型：第一个模型使用预训练的神经网络（VGG16, VGG19, DenseNet201, ResNet50, EfficientNetV7, InceptionV3， InceptionResNetV2和MobileNet）进行特征提取和分类。相比之下，第二种将这些网络的特征与机器学习分类器（XGBoost、随机森林、SVM、KNN、梯度增强、AdaBoost、决策树、线性判别分析、逻辑回归和多层感知器）集成在一起。第二种模型采用自适应误差最小化系统来提高精度。VGG16的准确率最高（训练准确率为99.61%，测试准确率为97.94%），而VGG19+自适应误差最小化的MLP准确率为97.08%，表现出更强的AD分类能力。

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引用次数: 0

Using convolutional network in graphical model detection of autism disorders with fuzzy inference systems 基于模糊推理系统的卷积网络在自闭症障碍图形模型检测中的应用

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100213

S. Rajaprakash , C. Bagath Basha , C. Sunitha Ram , I. Ameethbasha , V. Subapriya , R. Sofia

Autism spectrum disorder (ASD) study faces several challenges, including variations in brain connectivity patterns, small sample sizes, and data heterogeneity detection by magnetic resonance imaging (MRI). These issues make it challenging to identify consistent imaging modalities. Researchers have explored improved analysis techniques to solve the above problem via multimodal imaging and graph-based methods. Therefore, it is better to understand ASD neurology. The current techniques focus mainly on pairwise comparisons between individuals and often overlook features and individual characteristics. To overcome these limitations, in the proposed novel method, a multiscale enhanced graph with a convolutional network is used for ASD detection.

This work integrates non-imaging phenotypic data (from brain imaging data) with functional connectivity data (from Functional magnetic resonance images). In this approach, the population graph represents all individuals as vertices. The phenotypic data were used to calculate the weight between vertices in the graph using the fuzzy inference system. Fuzzy if-then rules, is used to determine the similarity between the phenotypic data. Each vertice connects feature vectors derived from the image data. The vertices and weights of each edge are used to incorporate phenotypic information. A random walk with a fuzzy MSE-GCN framework employs multiple parallel GCN layer embeddings. The outputs from these layers are joined in a completely linked layer to detect ASD efficiently. We assessed the performance of this background by the ABIDE data set and utilized recursive feature elimination and a multilayer perceptron for feature selection. This method achieved an accuracy rate of 87 % better than the current study.

自闭症谱系障碍（ASD）的研究面临着一些挑战，包括脑连接模式的差异、小样本量和磁共振成像（MRI）数据异质性检测。这些问题使得确定一致的成像模式具有挑战性。研究人员已经探索了改进的分析技术，通过多模态成像和基于图的方法来解决上述问题。因此，更好地了解ASD神经学。目前的技术主要集中在个体之间的两两比较，往往忽略了特征和个体特征。为了克服这些局限性，本文提出了一种基于卷积网络的多尺度增强图检测ASD的新方法。这项工作整合了非成像表型数据（来自脑成像数据）和功能连接数据（来自功能磁共振图像）。在这种方法中，总体图将所有个体表示为顶点。利用表型数据，利用模糊推理系统计算图中顶点之间的权重。模糊if-then规则用于确定表型数据之间的相似性。每个顶点连接来自图像数据的特征向量。每个边的顶点和权值被用来合并表型信息。模糊MSE-GCN框架的随机漫步采用多个并行GCN层嵌入。这些层的输出连接在一个完全链接的层中，以有效地检测ASD。我们通过ABIDE数据集评估了该背景的性能，并利用递归特征消除和多层感知器进行特征选择。该方法的准确率比目前的研究提高了87%。

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引用次数: 0

Predicting the effect of Bevacizumab therapy in ovarian cancer from H&E whole slide images using transformer model 利用变压器模型从H&E全幻灯片图像预测贝伐单抗治疗卵巢癌的效果

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100231

Md Shakhawat Hossain , Munim Ahmed , Md Sahilur Rahman , MM Mahbubul Syeed , Mohammad Faisal Uddin

Ovarian cancer (OC) ranks fifth in all cancer-related fatalities in women. Epithelial ovarian cancer (EOC) is a subclass of OC, accounting for 95 % of all patients. Conventional treatment for EOC is debulking surgery with adjuvant Chemotherapy; however, in 70 % of cases, this leads to progressive resistance and tumor recurrence. The United States Food and Drug Administration (FDA) recently approved Bevacizumab therapy for EOC patients. Bevacizumab improved survival and decreased recurrence in 30 % of cases, while the rest reported side effects, which include severe hypertension (27 %), thrombocytopenia (26 %), bleeding issues (39 %), heart problems (11 %), kidney problems (7 %), intestinal perforation and delayed wound healing. Moreover, it is costly; single-cycle Bevacizumab therapy costs approximately $3266. Therefore, selecting patients for this therapy is critical due to the high cost, probable adverse effects and small beneficiaries. Several methods were proposed previously; however, they failed to attain adequate accuracy. We present an AI-driven method to predict the effect from H&E whole slide image (WSI) produced from a patient's biopsy. We trained multiple CNN and transformer models using 10 × and 20 × images to predict the effect. Finally, the Data Efficient Image Transformer (DeiT) model was selected considering its high accuracy, interoperability and time efficiency. The proposed method achieved 96.60 % test accuracy and 93 % accuracy in 5-fold cross-validation and can predict the effect in less than 30 s. This method outperformed the state-of-the-art test accuracy (85.10 %) by 11 % and cross-validation accuracy (88.2 %) by 5 %. High accuracy and low prediction time ensured the efficacy of the proposed method.

卵巢癌（OC）在所有与癌症相关的女性死亡中排名第五。上皮性卵巢癌（EOC）是卵巢癌的一个亚类，占所有患者的95%。EOC的常规治疗是减体积手术加辅助化疗；然而，在70%的病例中，这导致了逐渐的耐药性和肿瘤复发。美国食品和药物管理局（FDA）最近批准了贝伐单抗治疗EOC患者。在30%的病例中，贝伐单抗提高了生存率并降低了复发率，而其余的病例报告了副作用，包括严重高血压（27%）、血小板减少（26%）、出血问题（39%）、心脏问题（11%）、肾脏问题（7%）、肠穿孔和伤口愈合延迟。此外，它是昂贵的；单周期贝伐单抗治疗费用约为3266美元。因此，选择患者进行这种治疗是至关重要的，因为成本高，可能的不良反应和小的受益者。之前提出了几种方法；然而，他们未能达到足够的准确性。我们提出了一种人工智能驱动的方法来预测从患者活检产生的H&；E全幻灯片图像（WSI）的效果。我们使用10 ×和20 ×图像训练多个CNN和transformer模型来预测效果。最后，考虑到数据高效图像转换器（Data Efficient Image Transformer, DeiT）模型具有较高的精度、互操作性和时间效率，选择了DeiT模型。该方法的试验准确度为96.60%，5次交叉验证准确度为93%，可在30 s内预测效果。该方法比目前最先进的测试准确度（85.10%）提高了11%，交叉验证准确度（88.2%）提高了5%。较高的预测精度和较短的预测时间保证了该方法的有效性。

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引用次数: 0

Using big data to predict young adult ischemic vs. non-ischemic heart disease risk factors: An artificial intelligence based model 利用大数据预测年轻人缺血性与非缺血性心脏病危险因素：基于人工智能的模型

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100207

Salam Bani Hani , Muayyad Ahmad

引用次数: 0

Open-source small language models for personal medical assistant chatbots 个人医疗助理聊天机器人的开源小语言模型

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2024.100197

Matteo Magnini , Gianluca Aguzzi , Sara Montagna

Medical chatbots are becoming essential components of telemedicine applications as tools to assist patients in the self-management of their conditions. This trend is particularly driven by advancements in natural language processing techniques with pre-trained language models (LMs). However, the integration of LMs into clinical environments faces challenges related to reliability and privacy concerns.

This study seeks to address these issues by exploiting a privacy by design architectural solution that utilises the fully local deployment of open-source LMs. Specifically, to mitigate any risk of information leakage, we focus on evaluating the performance of open-source language models (SLMs) that can be deployed on personal devices, such as smartphones or laptops, without stringent hardware requirements.

We assess the effectiveness of this solution adopting hypertension management as a case study. Models are evaluated across various tasks, including intent recognition and empathetic conversation, using Gemini Pro 1.5 as a benchmark. The results indicate that, for certain tasks such as intent recognition, Gemini outperforms other models. However, by employing the “large language model (LLM) as a judge” approach for semantic evaluation of response correctness, we found several models that demonstrate a close alignment with the ground truth. In conclusion, this study highlights the potential of locally deployed SLMs as components of medical chatbots, while addressing critical concerns related to privacy and reliability.

医疗聊天机器人正在成为远程医疗应用的重要组成部分，作为帮助患者自我管理病情的工具。这一趋势尤其受到自然语言处理技术与预训练语言模型（LMs）的进步的推动。然而，将LMs集成到临床环境中面临着与可靠性和隐私问题相关的挑战。本研究试图通过利用开源LMs的完全本地部署来利用隐私设计架构解决方案来解决这些问题。具体来说，为了减少信息泄露的风险，我们着重于评估可以部署在个人设备（如智能手机或笔记本电脑）上的开源语言模型（slm）的性能，而不需要严格的硬件要求。我们以高血压管理为例来评估这种解决方案的有效性。模型在各种任务中进行评估，包括意图识别和移情对话，使用Gemini Pro 1.5作为基准。结果表明，对于某些任务，如意图识别，Gemini优于其他模型。然而，通过采用“大型语言模型（LLM）作为判断”的方法来对响应正确性进行语义评估，我们发现了几个与基本事实密切一致的模型。总之，本研究强调了本地部署的slm作为医疗聊天机器人组件的潜力，同时解决了与隐私和可靠性相关的关键问题。

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引用次数: 0

Development and validation of a moderate aortic stenosis disease progression model 中度主动脉瓣狭窄疾病进展模型的建立和验证

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100201

Miguel R. Sotelo , Paul Nona , Loren Wagner , Chris Rogers , Julian Booker , Efstathia Andrikopoulou

Background

Understanding the multifactorial determinants of rapid progression in patients with aortic stenosis (AS) remains limited. We aimed to develop and validate a machine learning model (ML) for predicting rapid progression from moderate to severe AS within one year.

Methods

8746 patients were identified with moderate AS across seven healthcare organizations. Three ML models were trained using demographic, and echocardiographic variables, namely Random Forest, XGBoost and causal discovery-logistic regression. An ensemble model was developed integrating the aforementioned three. A total of 3355 patients formed the training and internal validation cohort. External validation was performed on 171 patients from one institution.

Results

An ensemble model was selected due to its superior F1 score and precision in internal validation (0.382 and 0.301, respectively). Its performance on the external validation cohort was modest (F1 score = 0.626, precision = 0.532).

Conclusion

An ensemble model comprising only demographic and echocardiographic variables was shown to have modest performance in predicting one-year progression from moderate to severe AS. Further validation in larger populations, along with integration of comprehensive clinical data, is crucial for broader applicability.

背景：对主动脉瓣狭窄（AS）患者快速进展的多因素决定因素的了解仍然有限。我们的目标是开发和验证一种机器学习模型（ML），用于预测一年内从中度到重度AS的快速进展。方法7家医疗机构共8746例中度AS患者。使用人口统计学和超声心动图变量，即随机森林、XGBoost和因果发现-逻辑回归，训练了三个ML模型。开发了一个集成模型，将上述三者集成在一起。共有3355名患者组成了培训和内部验证队列。对来自同一机构的171例患者进行了外部验证。结果集成模型在内部验证中F1得分和精密度均较优（分别为0.382和0.301）。其在外部验证队列中的表现一般（F1评分= 0.626，精密度= 0.532）。结论仅包含人口统计学和超声心动图变量的集成模型在预测中度到重度AS的一年进展方面表现不佳。在更大的人群中进一步验证，以及综合临床数据的整合，对于更广泛的适用性至关重要。

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引用次数: 0

ESC-UNET: A hybrid CNN and Swin Transformers for skin lesion segmentation ESC-UNET：一种混合CNN和Swin变压器的皮肤损伤分割方法

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100257

Anwar Jimi , Nabila Zrira , Oumaima Guendoul , Ibtissam Benmiloud , Haris Ahmad Khan , Shah Nawaz

One of the most important tasks in computer-aided diagnostics is the automatic segmentation of skin lesions, which plays an essential role in the early diagnosis and treatment of skin cancer. In recent years, the Convolutional Neural Network (CNN) has largely replaced other traditional methods for segmenting skin lesions. However, due to insufficient information and unclear lesion region segmentation, skin lesion image segmentation still has challenges. In this paper, we propose a novel deep medical image segmentation approach named “ESC-UNET” which combines the advantages of CNN and Transformer to effectively leverage local information and long-range dependencies to enhance medical image segmentation. In terms of the local information, we use a CNN-based encoder and decoder framework. The CNN branch mines local information from medical images using the locality of convolution processes and the pre-trained EfficientNetB5 network. As for the long-range dependencies, we build a Transformer branch that emphasizes the global context. In addition, we employ Atrous Spatial Pyramid Pooling (ASPP) to gather network-wide relevant information. The Convolution Block Attention Module (CBAM) is added to the model to promote effective features and suppress ineffective features in segmentation. We have evaluated our network using the ISIC 2016, ISIC 2017, and ISIC 2018 datasets. The results demonstrate the efficiency of the proposed model in segmenting skin lesions.

计算机辅助诊断中最重要的任务之一是皮肤病变的自动分割，这对皮肤癌的早期诊断和治疗起着至关重要的作用。近年来，卷积神经网络（CNN）在很大程度上取代了其他传统的皮肤损伤分割方法。然而，由于信息不足和病灶区域分割不清，皮肤病灶图像分割仍然存在挑战。本文提出了一种新的医学图像深度分割方法“ESC-UNET”，该方法结合了CNN和Transformer的优点，有效地利用了局部信息和远程依赖关系来增强医学图像分割。在局部信息方面，我们使用了基于cnn的编码器和解码器框架。CNN分支使用卷积过程的局部性和预训练的effentnetb5网络从医学图像中挖掘局部信息。至于远程依赖，我们构建一个强调全局上下文的Transformer分支。此外，我们采用亚特劳斯空间金字塔池（ASPP）来收集全网络的相关信息。在该模型中加入了卷积块注意模块（CBAM），在分割中提升有效特征，抑制无效特征。我们使用ISIC 2016、ISIC 2017和ISIC 2018数据集评估了我们的网络。实验结果证明了该模型在皮肤损伤分割方面的有效性。

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引用次数: 0

In-silo federated learning vs. centralized learning for segmenting acute and chronic ischemic brain lesions 竖井联合学习与集中式学习对急性和慢性缺血性脑损伤的分割

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100283

Joon Kim , Hoyeon Lee , Jonghyeok Park , Sang Hyun Park , Myungjae Lee , Leonard Sunwoo , Chi Kyung Kim , Beom Joon Kim , Dong-Eog Kim , Wi-Sun Ryu

Objectives

To investigate the efficacy of federated learning (FL) compared to industry-level centralized learning (CL) for segmenting acute infarct and white matter hyperintensity.

Materials and methods

This retrospective study included 13,546 diffusion-weighted images (DWI) from 10 hospitals and 8421 fluid-attenuated inversion recovery (FLAIR) images from 9 hospitals for acute (Task I) and chronic (Task II) lesion segmentation. We trained with datasets originated from 9 and 3 institutions for Task I and Task II, respectively, and externally tested them in datasets originated from 1 and 6 institutions each. For FL, the central server aggregated training results every four rounds with FedYogi (Task I) and FedAvg (Task II). A batch clipping strategy was tested for the FL models. Performances were evaluated with the Dice similarity coefficient (DSC).

Results

The mean ages (SD) for the training datasets were 68.1 (12.8) for Task I and 67.4 (13.0) for Task II. The frequency of male participants was 51.5 % and 60.4 %, respectively. In Task I, the FL model employing batch clipping trained for 360 epochs achieved a DSC of 0.754 ± 0.183, surpassing an equivalently trained CL model (DSC 0.691 ± 0.229; p < 0.001) and comparable to the best-performing CL model at 940 epochs (DSC 0.755 ± 0.207; p = 0.701). In Task II, no significant differences were observed amongst FL model with clipping, without clipping, and CL model after 48 epochs (DSCs of 0.761 ± 0.299, 0.751 ± 0.304, 0.744 ± 0.304). Few-shot FL showed significantly lower performance. Task II reduced training times with batch clipping (3.5–1.75 h).

Conclusions

Comparisons between CL and FL in identical settings suggest the feasibility of FL for medical image segmentation.

目的比较联邦学习（FL）与集中式学习（CL）在急性梗死和脑白质高信号分割中的疗效。材料和方法本回顾性研究包括来自10家医院的13546张弥散加权图像（DWI）和来自9家医院的8421张液体衰减反转恢复（FLAIR）图像，用于急性（任务I）和慢性（任务II）病变分割。我们在Task I和Task II中分别使用来自9个和3个机构的数据集进行训练，并在来自1个和6个机构的数据集中对它们进行外部测试。对于FL，中央服务器使用FedYogi （Task I）和FedAvg （Task II）每四轮汇总训练结果。对FL模型进行了批量裁剪策略测试。用Dice相似系数（DSC）对性能进行评价。结果任务1的平均年龄（SD）为68.1(12.8)，任务2的平均年龄（SD）为67.4（13.0）。男性参与者的频率分别为51.5%和60.4%。在Task I中，采用360次批次裁剪训练的FL模型的DSC为0.754±0.183，超过了同等训练的CL模型(DSC为0.691±0.229；p & lt;0.001)，与940个epoch的最佳CL模型相当(DSC 0.755±0.207；p = 0.701)。在Task II中，经过48个epoch后，经剪裁的FL模型、未经过剪裁的FL模型和CL模型的dsc均无显著差异（dsc分别为0.761±0.299、0.751±0.304、0.744±0.304）。少射FL表现出明显较低的性能。任务II通过批量裁剪减少了训练时间（3.5-1.75小时）。结论在相同的条件下，对CL和FL的比较表明FL用于医学图像分割是可行的。

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引用次数: 0

Swarm intelligence in biomedical engineering 生物医学工程中的群体智能

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100308

Seyyed Ali Zendehbad , Elias Mazrooei Rad , Shahryar Salmani Bajestani

Swarm Intelligence (SI), a specialized branch of Artificial Intelligence (AI), is founded on the collective behaviors observed in biological systems, such as those of ants, bees, and bird flocks. This bio-inspired approach enables SI to develop computational algorithms that tackle complex problems, which traditional methods often struggle to address. Over the past few decades, SI has gained substantial traction in biomedical engineering due to its capacity to address multifaceted issues with higher adaptability and efficiency. This review presents key advancements in SI applications across three primary areas: neurorehabilitation, Alzheimer's Disease Diagnosis (ADD), and medical image processing. In neurorehabilitation, SI has played a pivotal role in improving the precision and adaptability of devices such as exoskeletons and neuroprostheses, enhancing motor function recovery for patients. Similarly, in ADD, SI algorithms have shown significant promise in analyzing neuroimaging and neurophysiological data, increasing diagnostic accuracy and enabling earlier intervention. Furthermore, in medical image processing, SI techniques have been effectively applied to tasks such as image segmentation, tumor detection, feature extraction, and artifact reduction, particularly in modalities like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and ultrasound imaging (such as Sonography). Based on the literature analyzed, SI methods have demonstrated consistent strengths in global optimization, adaptability to noisy data, and robustness in feature selection tasks when compared to traditional machine learning techniques. However, SI still faces challenges, especially regarding computational complexity, model interpretability, and limited clinical translation. Overcoming these hurdles is crucial for the full-scale adoption of this technology in clinical settings. This review not only highlights the progress in these areas but also synthesizes current limitations and future directions to guide the effective integration of SI into real-world biomedical applications.

群体智能（SI）是人工智能（AI）的一个专门分支，它建立在生物系统中观察到的集体行为基础上，例如蚂蚁、蜜蜂和鸟群。这种受生物启发的方法使SI能够开发出解决复杂问题的计算算法，而传统方法往往难以解决这些问题。在过去的几十年里，由于其具有更高的适应性和效率，能够解决多方面的问题，因此在生物医学工程中获得了巨大的吸引力。本文综述了SI在三个主要领域的应用进展：神经康复、阿尔茨海默病诊断（ADD）和医学图像处理。在神经康复中，SI在提高外骨骼和神经假体等设备的精度和适应性，促进患者运动功能恢复方面发挥了关键作用。同样，在ADD中，SI算法在分析神经影像学和神经生理学数据、提高诊断准确性和实现早期干预方面显示出巨大的前景。此外，在医学图像处理中，SI技术已有效地应用于图像分割、肿瘤检测、特征提取和伪影减少等任务，特别是在磁共振成像（MRI）、计算机断层扫描（CT）和超声成像（如超声）等模式中。基于文献分析，与传统机器学习技术相比，SI方法在全局优化、对噪声数据的适应性和特征选择任务的鲁棒性方面表现出一致的优势。然而，SI仍然面临挑战，特别是在计算复杂性、模型可解释性和有限的临床翻译方面。克服这些障碍对于在临床环境中全面采用这项技术至关重要。这篇综述不仅强调了这些领域的进展，而且综合了当前的局限性和未来的方向，以指导科学技术有效地融入现实世界的生物医学应用。

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引用次数: 0