Intelligence-based medicine最新文献_第3页

A machine learning–based method for supporting the diagnosis of eosinophilic granulomatosis with polyangiitis: Development and evaluation 一种基于机器学习的支持诊断嗜酸性肉芽肿病合并多血管炎的方法：发展和评估

Intelligence-based medicine

Pub Date : 2025-11-28 DOI: 10.1016/j.ibmed.2025.100317

Yosra Bouattour , Mohamed Hédi Maâloul , Zouhir Bahloul , Sameh Marzouk

Background/objectives

Eosinophilic granulomatosis with polyangiitis (EGPA), formerly Churg-Strauss syndrome, is a rare systemic vasculitis often diagnosed late due to its heterogeneous presentation, leading to severe complications—particularly cardiac involvement, a major cause of morbidity and mortality. We developed EGPA-ML, an artificial intelligence (AI)-based tool using supervised machine learning (ML), to support early and accurate EGPA diagnosis, especially in non-specialized settings.

Methods

A retrospective cohort of patients evaluated for suspected vasculitis at Hedi Chaker Hospital, Sfax, Tunisia, from 1997 to 2023 (nearly three decades), provided 1904 clinical, biological, and histological features. After data cleaning, standardization, and feature selection, 56 key features were retained. Patients were classified as {EGPA} or {NOT_EGPA} per the 2022 ACR/EULAR criteria, with expert consensus (κ = 0.85). Multiple supervised ML algorithms were evaluated via 10-fold cross-validation. The best model was integrated into EGPA-ML, a Java-based clinical decision support system. Performance was assessed on an independent dataset of n = 280 key features, with reference classification {EGPA}/{NOT_EGPA} validated by experts (κ = 0.89).

Results

On the test and evaluation dataset, EGPA-ML achieved a recall of 0.992, precision of 0.869, and F1-score of 0.926. Feature importance analysis identified asthma and eosinophil count as top predictors (36.5 % each), followed by ANCA status, vascular purpura, and histological vasculitis.

Conclusions

EGPA-ML is a high-performance, interpretable, and adaptive tool based on supervised ML, supporting timely EGPA diagnosis. It represents a practical advancement for clinical decision-making in rare diseases, particularly in internal medicine, pulmonology, and cardiology.

背景/目的嗜酸性肉芽肿病合并多血管炎（EGPA），以前称为Churg-Strauss综合征，是一种罕见的全身性血管炎，由于其异质表现，通常诊断较晚，导致严重的并发症，特别是心脏受累，是发病率和死亡率的主要原因。我们开发了EGPA-ML，这是一种基于人工智能（AI）的工具，使用监督机器学习（ML）来支持早期和准确的EGPA诊断，特别是在非专业环境中。方法对1997年至2023年（近30年）在突尼斯斯法克斯Hedi Chaker医院接受疑似血管炎评估的患者进行回顾性队列分析，提供了1904项临床、生物学和组织学特征。经过数据清理、标准化和特征选择，保留了56个关键特征。根据2022年ACR/EULAR标准将患者分为{EGPA}或{NOT_EGPA}，专家共识（κ = 0.85）。通过10倍交叉验证评估多个监督ML算法。将最佳模型集成到基于java的临床决策支持系统EGPA-ML中。在n = 280个关键特征的独立数据集上进行性能评估，参考分类{EGPA}/{NOT_EGPA}经过专家验证（κ = 0.89）。结果在测试和评价数据集中，EGPA-ML的召回率为0.992，精密度为0.869，f1得分为0.926。特征重要性分析发现哮喘和嗜酸性粒细胞计数是最重要的预测因子（各占36.5%），其次是ANCA状态、血管性紫癜和组织学血管炎。结论segpa -ML是一种基于监督式ML的高性能、可解释性和自适应的工具，支持EGPA的及时诊断。它代表了罕见病临床决策的实际进步，特别是在内科、肺脏学和心脏病学方面。

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引用次数: 0

Radiological trends in convolutional neural networks for breast cancer diagnosis 卷积神经网络在乳腺癌诊断中的放射学趋势

Intelligence-based medicine

Pub Date : 2025-11-27 DOI: 10.1016/j.ibmed.2025.100322

Ka Lee Li , Martin Ga Zen Tam , Sai Ka Li , Fatema Aftab

Breast cancer remains a leading cause of cancer-related mortality worldwide, making early and accurate diagnosis crucial for patient outcomes. Future diagnosis may employ convolutional neural networks (CNNs), which have established themselves as powerful multi-layered artificial intelligence (AI) tools for computer vision tasks, with growing applications in breast cancer detection, diagnosis and classification. To provide insight into this field's intellectual, social, and conceptual knowledge structures, we conducted a bibliometric review of its 100 most-cited articles. The review looked at articles from January 1, 1995 to August 23, 2024. Our network analyses encourage increased inter-country collaboration. Thematic mapping highlights the increasing role of CNNs as foundational components in present and future AI applications. Multiple correspondence analyses track progress in diagnostic accuracy, system performance, and advanced classification techniques. Study design analyses suggest a need for future CNN research to be benchmarked against human readers and foster closer collaboration between technical and clinical researchers. In this bibliometric analysis, we summarise key contributions, examine emerging research trends, and provide an overview of the evolving landscape of CNN applications in breast cancer diagnostics.

乳腺癌仍然是世界范围内癌症相关死亡的主要原因，因此早期和准确的诊断对患者的预后至关重要。未来的诊断可能会使用卷积神经网络（cnn），卷积神经网络已经成为计算机视觉任务中强大的多层人工智能（AI）工具，在乳腺癌检测、诊断和分类方面的应用越来越多。为了深入了解这个领域的智力、社会和概念知识结构，我们对100篇被引用最多的文章进行了文献计量分析。该评论研究了1995年1月1日至2024年8月23日的文章。我们的网络分析鼓励加强国家间合作。专题映射强调了cnn在当前和未来人工智能应用中作为基础组件的日益重要的作用。多个对应分析跟踪诊断准确性、系统性能和高级分类技术的进展。研究设计分析表明，未来的CNN研究需要以人类读者为基准，并促进技术和临床研究人员之间更密切的合作。在这篇文献计量分析中，我们总结了主要贡献，研究了新兴的研究趋势，并概述了CNN在乳腺癌诊断中应用的发展前景。

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引用次数: 0

Capsule-augmented deep learning architectures for mental health detection from social media text 用于社交媒体文本心理健康检测的胶囊增强深度学习架构

Intelligence-based medicine

Pub Date : 2025-11-24 DOI: 10.1016/j.ibmed.2025.100319

Faheem Ahmad Wagay, Jahiruddin

Mental health detection from social media text has attracted growing research attention due to the global rise in mental health concerns. Traditional deep learning models, such as Bidirectional Long Short-Term Memory (BiLSTM) networks and hybrid Convolutional BiLSTM (Conv-BiLSTM) architectures, have demonstrated strong performance in text classification tasks. However, these models often struggle to capture the hierarchical and spatial relationships that are intrinsic to linguistic data. To address this limitation, this study investigates the integration of capsule networks with BiLSTM and Conv-BiLSTM architectures for mental health detection. Leveraging a real-world Reddit corpus, we conduct extensive experiments comparing baseline BiLSTM and Conv-BiLSTM models with their capsule-enhanced counterparts. Furthermore, we explore the role of advanced loss functions, such as focal loss and contrastive loss, in addressing class imbalance and mitigating boundary blurring among semantically overlapping disorders. Our findings indicate that incorporating capsule layers significantly strengthens feature representation, leading to notable improvements in accuracy and F1-score across multiple mental health categories. The study focuses on six key disorders, including depression, anxiety, borderline personality disorder (BPD), and bipolar disorder. In addition, model interpretability is enhanced using Local Interpretable Model-agnostic Explanations (LIME), which highlights the critical linguistic features driving predictions, thereby improving transparency and reliability in mental health evaluations.

由于全球对心理健康问题的关注日益增加，从社交媒体文本中检测心理健康引起了越来越多的研究关注。传统的深度学习模型，如双向长短期记忆（BiLSTM）网络和混合卷积BiLSTM （convl -BiLSTM）架构，在文本分类任务中表现出了很强的性能。然而，这些模型往往难以捕捉语言数据固有的层次和空间关系。为了解决这一限制，本研究探讨了胶囊网络与BiLSTM和convl -BiLSTM架构的整合，用于心理健康检测。利用真实世界的Reddit语料库，我们进行了广泛的实验，将基线BiLSTM和卷积BiLSTM模型与胶囊增强模型进行比较。此外，我们探讨了高级损失函数的作用，如焦点损失和对比损失，在解决类失衡和减轻语义重叠障碍中的边界模糊。我们的研究结果表明，结合胶囊层显着增强了特征表征，导致多个心理健康类别的准确性和f1得分显着提高。这项研究的重点是六种关键的疾病，包括抑郁症、焦虑症、边缘型人格障碍（BPD）和双相情感障碍。此外，使用局部可解释模型不可知论解释（LIME）增强了模型的可解释性，它突出了驱动预测的关键语言特征，从而提高了心理健康评估的透明度和可靠性。

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引用次数: 0

Enhanced Polycystic Ovary Syndrome diagnosis model leveraging a K-means based genetic algorithm and ensemble approach 基于k均值遗传算法和集成方法的多囊卵巢综合征增强诊断模型

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100253

Najlaa Faris , Aqeel Sahi , Mohammed Diykh , Shahab Abdulla , Siuly Siuly

Polycystic Ovary Syndrome (PCOS) is a prevalent hormonal disorder affecting women in their childbearing years. Detecting PCOS early is crucial for preserving fertility in young women and preventing long-term health complications like hypertension, heart disease, and obesity. While costly clinical tests exist to detect PCOS, there is a growing demand for more accurate and affordable diagnostic methods. The primary objective of this research is to pinpoint the most effective PCOS features that can aid experts in early diagnosis. We introduce a feature extraction model, termed KM-GN, which combines the k-means algorithm with a genetic selection algorithm to identify the most informative features for PCOS detection. These selected features are fed into our designed model, Random Subspace-based Bootstrap Aggregating Ensembles (RSBE). To assess the performance of the proposed RSBE method, we compare it against several individual and ensemble classifiers. The effectiveness of our model is assessed using a freely accessible dataset comprising 43 traits from 541 women, of whom 177 have been diagnosed with PCOS. We employ various statistical metrics to evaluate the performance, including the confusion matrix, accuracy, recall, F1 score, precision, and specificity. The experimental outcomes demonstrate the viability of implementing our proposed model as a hardware tool for efficient detection of PCOS.

多囊卵巢综合征（PCOS）是一种影响育龄妇女的普遍激素失调。早期发现多囊卵巢综合征对于保持年轻女性的生育能力和预防高血压、心脏病和肥胖等长期健康并发症至关重要。虽然存在昂贵的临床测试来检测多囊卵巢综合征，但对更准确和负担得起的诊断方法的需求不断增长。本研究的主要目的是确定最有效的多囊卵巢综合征特征，以帮助专家进行早期诊断。我们引入了一种特征提取模型KM-GN，该模型结合了k-means算法和遗传选择算法来识别PCOS检测中最具信息量的特征。这些选择的特征被馈送到我们设计的模型，随机子空间为基础的Bootstrap聚合集成（RSBE）。为了评估所提出的RSBE方法的性能，我们将其与几个单独和集成分类器进行比较。我们的模型的有效性是使用一个免费访问的数据集来评估的，该数据集包括来自541名女性的43个特征，其中177名被诊断为多囊卵巢综合征。我们采用各种统计指标来评估性能，包括混淆矩阵、准确性、召回率、F1评分、精度和特异性。实验结果表明，将我们提出的模型作为有效检测PCOS的硬件工具是可行的。

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引用次数: 0

Improving CNN interpretability and evaluation via alternating training and regularization in chest CT scans 通过交替训练和正则化在胸部CT扫描中提高CNN的可解释性和评价

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100211

Rodrigo Ramos-Díaz , Jesús García-Ramírez , Jimena Olveres , Boris Escalante-Ramírez

Interpretable machine learning is an emerging trend that holds significant importance, considering the growing impact of machine learning systems on society and human lives. Many interpretability methods are applied in CNN after training to provide deeper insights into the outcomes, but only a few have tried to promote interpretability during training. The aim of this experimental study is to investigate the interpretability of CNN. This research was applied to chest computed tomography scans, as understanding CNN predictions has particular importance in the automatic classification of medical images. We attempted to implement a CNN technique aimed at improving interpretability by relating filters in the last convolutional to specific output classes. Variations of such a technique were explored and assessed using chest CT images for classification based on the presence of lungs and lesions. A search was conducted to optimize the specific hyper-parameters necessary for the evaluated strategies. A novel strategy is proposed employing transfer learning and regularization. Models obtained with this strategy and the optimized hyperparameters were statistically compared to standard models, demonstrating greater interpretability without a significant loss in predictive accuracy. We achieved CNN models with improved interpretability, which is crucial for the development of more explainable and reliable AI systems.

考虑到机器学习系统对社会和人类生活的影响越来越大，可解释的机器学习是一种具有重要意义的新兴趋势。许多可解释性方法在训练后应用于CNN，以提供对结果的更深入的了解，但只有少数方法试图在训练过程中提高可解释性。本实验研究的目的是探讨CNN的可解释性。这项研究应用于胸部计算机断层扫描，因为理解CNN预测在医学图像的自动分类中特别重要。我们试图实现一种CNN技术，旨在通过将最后一个卷积中的过滤器与特定的输出类关联来提高可解释性。这种技术的变化被探索和评估使用胸部CT图像进行分类基于肺和病变的存在。进行搜索以优化评估策略所需的特定超参数。提出了一种利用迁移学习和正则化的新策略。用该策略获得的模型和优化的超参数与标准模型进行统计比较，显示出更大的可解释性，而不会显著降低预测精度。我们实现了具有改进可解释性的CNN模型，这对于开发更具可解释性和可靠性的人工智能系统至关重要。

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引用次数: 0

A multimodal machine learning model for bipolar disorder mania classification: Insights from acoustic, linguistic, and visual cues 双相情感障碍躁狂分类的多模态机器学习模型：来自声学、语言和视觉线索的见解

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100223

Kiruthiga Devi Murugavel , Parthasarathy R , Sandeep Kumar Mathivanan , Saravanan Srinivasan , Basu Dev Shivahare , Mohd Asif Shah

Mood fluctuations that can vary from manic to depressive states are a symptom of a disease known as bipolar disorder, which affects mental health. Interviews with patients and gathering information from their families are essential steps in the diagnostic process for bipolar disorder. Automated approaches for treating bipolar disorder are also being explored. In mental health prevention and care, machine learning techniques (ML) are increasingly used to detect and treat diseases. With frequently analyzed human behaviour patterns, identified symptoms, and risk factors as various parameters of the dataset, predictions can be made for improving traditional diagnosis methods. In this study, A Multimodal Fusion System was developed based on an auditory, linguistic, and visual patient recording as an input dataset for a three-stage mania classification decision system. Deep Denoising Autoencoders (DDAEs) are introduced to learn common representations across five modalities: acoustic characteristics, eye gaze, facial landmarks, head posture, and Facial Action Units (FAUs). This is done in particular for the audio-visual modality. The distributed representations and the transient information during each recording session are eventually encoded into Fisher Vectors (FVs), which capture the representations. Once the Fisher Vectors (FVs) and document embeddings are integrated, a Multi-Task Neural Network is used to perform the classification task, while mitigating overfitting issues caused by the limited size of the bipolar disorder dataset. The study introduces Deep Denoising Autoencoders (DDAEs) for cross-modal representation learning and utilizes Fisher Vectors with Multi-Task Neural Networks, enhancing diagnostic accuracy while highlighting the benefits of multimodal fusion for mental health diagnostics. Achieving an unweighted average recall score of 64.8 %, with the highest AUC-ROC of 0.85 & less interface time of 6.5 ms/sample scores the effectiveness of integrating multiple modalities in improving system performance and advancing feature representation and model interpretability.

从躁狂到抑郁状态的情绪波动是一种被称为双相情感障碍的疾病的症状，这种疾病会影响心理健康。在双相情感障碍的诊断过程中，与患者面谈和从其家庭收集信息是必不可少的步骤。治疗双相情感障碍的自动化方法也在探索中。在心理健康预防和护理中，机器学习技术（ML）越来越多地用于检测和治疗疾病。通过频繁分析人类行为模式、识别症状和风险因素作为数据集的各种参数，可以对改进传统诊断方法进行预测。在这项研究中，一个多模态融合系统是基于听觉、语言和视觉患者记录作为三阶段躁狂分类决策系统的输入数据集而开发的。引入深度去噪自动编码器（DDAEs）来学习五种模式的常见表示：声学特征、眼睛注视、面部标志、头部姿势和面部动作单位（FAUs）。这尤其适用于视听方式。每个记录过程中的分布式表示和瞬态信息最终被编码成捕获表示的Fisher向量（FVs）。一旦将Fisher向量（FVs）和文档嵌入集成在一起，就可以使用多任务神经网络来执行分类任务，同时减轻由双相情感障碍数据集有限大小引起的过拟合问题。该研究引入了用于跨模态表示学习的深度去噪自动编码器（DDAEs），并利用Fisher向量与多任务神经网络，提高了诊断准确性，同时突出了多模态融合对心理健康诊断的好处。实现了64.8%的未加权平均召回分数，最高AUC-ROC为0.85 &；6.5 ms/样本的接口时间较短，对集成多种模式在提高系统性能、提高特征表示和模型可解释性方面的有效性进行了评分。

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引用次数: 0

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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