Intelligence-based medicine最新文献_第2页

TransFusion-BCNet: A transformer-driven multi-modal fusion and explainable deep learning framework for breast cancer diagnosis 输血- bcnet：用于乳腺癌诊断的转换器驱动的多模式融合和可解释的深度学习框架

Intelligence-based medicine

Pub Date : 2026-01-12 DOI: 10.1016/j.ibmed.2026.100346

Ahmed Kateb Jumaah Al-Nussairi , Saleem Malik , Yasser Taha Alzubaidi , S Gopal Krishna Patro , Kasim Sakran Abass , Iman Basheti , Mohammad Khishe

Most AI breast cancer detection systems use single-modality imaging algorithms, limiting clinical reliability. Early and accurate detection improves therapy and mortality. These challenges are addressed by Transformer-driven multi-modal fusion and explainable deep learning system, TransFusion-BCNet for breast cancer diagnosis. The framework consists of three parts. The TriFusion-Transformer (TriFT) performs three-tier fusion: intra-modality fusion across multiple mammogram views and imaging sources, inter-modality fusion combining mammogram, ultrasound, MRI, and clinical features, and decision-level fusion for robust outcome prediction. TriFT detects complicated connections across heterogeneous modalities, unlike classical fusion. Second, we present the FusionAttribution Map (FAMap), a dual-level interpretability mechanism that generates imaging data region-level saliency maps and modality-level contribution scores to evaluate input source influence. This openness helps clinicians understand where and which modality drives predictions. Third, the MetaFusion Optimizer (MFO) adjusts fusion weights, network depth, and learning parameters via evolutionary search and gradient-based fine-tuning. Traditional optimizers lack model generalization and training stability. This staged technique improved both. TransFusion-BCNet outperforms CNN–Transformer hybrids with 99.4 % accuracy, 99.0 % precision, 99.2 % recall, and 99.1 % F1-score in extensive CBIS-DDSM,BUSI, TCGA-BRCA and RIDER Breast MRI datasets. With TriFT, FAMap, and MFO, TransFusion-BCNet provides a robust, transparent, and clinically interpretable diagnostic framework, improving AI in breast cancer screening and decision assistance.

大多数人工智能乳腺癌检测系统使用单模态成像算法，限制了临床可靠性。早期和准确的检测可以改善治疗和死亡率。这些挑战由transformer驱动的多模态融合和可解释的深度学习系统——用于乳腺癌诊断的输血- bcnet来解决。该框架由三部分组成。TriFT进行三层融合：跨多个乳房x光片视图和成像源的模态内融合，结合乳房x光片、超声、MRI和临床特征的模态间融合，以及用于稳健结果预测的决策级融合。与传统的融合不同，TriFT可以检测跨异构模式的复杂连接。其次，我们提出了FusionAttribution Map (FAMap)，这是一种双层可解释性机制，可生成成像数据区域级显著性图和模式级贡献分数，以评估输入源的影响。这种开放性有助于临床医生了解在哪里以及哪种模式驱动预测。第三，MetaFusion Optimizer （MFO）通过进化搜索和基于梯度的微调来调整融合权重、网络深度和学习参数。传统的优化算法缺乏模型泛化和训练稳定性。这种分阶段的技术改善了这两方面。在广泛的CBIS-DDSM、BUSI、TCGA-BRCA和RIDER乳腺MRI数据集中，输血- bcnet的准确率为99.4%，精密度为99.0%，召回率为99.2%，f1评分为99.1%，优于CNN-Transformer混合方案。通过TriFT、FAMap和MFO，输血- bcnet提供了一个强大、透明和临床可解释的诊断框架，改善了人工智能在乳腺癌筛查和决策辅助方面的应用。

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引用次数: 0

Enhancing risk prediction for diabetes, hypertension, and heart disease using SMOTE-ENN balancing with PCA and gradient boosting in healthcare AI 在医疗保健AI中使用SMOTE-ENN平衡与PCA和梯度增强来增强糖尿病、高血压和心脏病的风险预测

Intelligence-based medicine

Pub Date : 2026-01-08 DOI: 10.1016/j.ibmed.2025.100339

Tapon Paul , Md Assaduzzaman , Nafiz Fahad , Md Jakir Hossen

Predicting chronic disease remains a crucial problem, particularly in low-resource environments where accurate and timely predictions are of utmost importance. Machine learning methods do not adequately generalize, encounter data imbalance and computation issues when used. In this study, an improved model that makes predictions using Synthetic minority over-sampling technique and edited nearest neighbor balancing techniques and Principal Component Analysis and Gradient Boosting to predict chronic diseases based on readily available clinical profiles is proposed. The dataset used in this study was retrieved from provide precise source: Kaggle dataset. It contains over 1500 anonymized patient records and includes 15 features such as demographic, lifestyle, and clinical measures. Standardized encoding labels, and adjusting classes using SMOTE-ENN have been completed before the PCA was conducted to improve computation speed and reduce overfitting. Decision Tree, Random Forest, LightGBM, XGBoost have been used for comparison to suggest the best performing model seen to be Gradient Boosting. PCA is performed, and the Gradient Boosting approach produces better results. Precision measures how often the classification system is correct when making a positive test result, while recall is determined using a contingency table, and the F1 score, the possibility of modeling outcomes out of 100 trials. The model proposed in the experiment provides the following outputs: Accuracy (CV: 99.33 %, CI: 98.90 %–99.50 %), Precision (CV: 99 %, CI: 98 %–99.5 %), Recall (CV: 99 %, CI: 98 %–99.5 %), F1-Score (CV: 99 %, CI: 98 %–99.5 %). The model's performance was evaluated using cross-validation, yielding an accuracy of 98.90 %. The classifying system performance is specified by the ROC-AUC ranking. It outperforms the model making indefinite projections; its ROC-AUC value is greater than 0.99. The suggested model is a robust, interpretable, and high-precision approach for the early detection of chronic conditions. Therefore, the suggested machine learning system can deliver a considerable promise with respect to creating patient-oriented outcomes.

预测慢性病仍然是一个关键问题，特别是在资源匮乏的环境中，准确和及时的预测至关重要。机器学习方法泛化不充分，使用时会遇到数据不平衡和计算问题。在本研究中，提出了一种改进的模型，该模型使用合成少数过采样技术和编辑最近邻平衡技术以及主成分分析和梯度增强来预测基于现有临床资料的慢性疾病。本研究使用的数据集检索自提供精确来源：Kaggle数据集。它包含1500多名匿名患者记录，包括15个特征，如人口统计、生活方式和临床措施。为了提高计算速度和减少过拟合，在PCA之前已经完成了标准化的编码标签和使用SMOTE-ENN进行类调整。决策树，随机森林，LightGBM， XGBoost已经被用于比较，以表明表现最好的模型是梯度增强。进行了主成分分析，结果表明梯度增强方法效果较好。精确度衡量的是分类系统在产生阳性测试结果时的正确频率，而召回率是使用列联表和F1分数来确定的，F1分数是100次试验中建模结果的可能性。实验中提出的模型提供了以下输出：准确率（CV: 99.33%, CI: 98.90% - 99.50%），精度（CV: 99%, CI: 98% - 99.5%），召回率（CV: 99%, CI: 98% - 99.5%）， F1-Score （CV: 99%, CI: 98% - 99.5%）。使用交叉验证对模型的性能进行了评估，准确度为98.90%。分类系统的性能由ROC-AUC排序指定。它优于模型进行不确定的预测；ROC-AUC值大于0.99。所建议的模型是一种稳健的、可解释的、高精度的慢性疾病早期检测方法。因此，建议的机器学习系统在创造以患者为导向的结果方面可以提供相当大的承诺。

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引用次数: 0

Enhancing AI-based oral decision support systems: Hybrid image processing for detecting impacted maxillary canines in orthopantomograms 增强基于人工智能的口腔决策支持系统：混合图像处理在矫形断层摄影中检测上颌埋伏齿

Intelligence-based medicine

Pub Date : 2026-01-07 DOI: 10.1016/j.ibmed.2026.100345

Roopitha C.H , Veena Mayya , V. Sivakumar , Vathsala Patil , Divya Pai , Pavithra Varchas

Timely identification of impacted canines is crucial for preventing complications such as root resorption, misalignment, and damage to neighboring teeth. This research evaluates image processing techniques for AI-based oral decision support systems using orthopantomogram (OPG) images to determine effective preprocessing methods to improve deep learning (DL) model performance. The approach involved preprocessing OPG images followed by object detection using YOLOv5. Preprocessing techniques included median filtering, Gaussian blur, CLAHE, image sharpening, histogram stretching, and CLAHE combined with image sharpening. Performance was evaluated using precision, recall, F1 score, mAP, and IoU metrics. The CLAHE-sharpening combination achieved superior performance with precision of 0.934, recall of 0.932, F1 score of 0.931, mAP of 0.948, and IoU of 0.741, significantly outperforming unprocessed images. Grad-CAM visualizations confirmed that preprocessing enabled the model to identify relevant regions effectively. This study emphasizes the importance of preprocessing methods in improving the diagnostic accuracy in dental radiography for improved treatment planning.

及时识别患牙对于预防并发症如牙根吸收、牙齿错位和对邻近牙齿的损害至关重要。本研究评估了基于人工智能的口头决策支持系统的图像处理技术，使用正体层图（OPG）图像来确定有效的预处理方法，以提高深度学习（DL）模型的性能。该方法包括预处理OPG图像，然后使用YOLOv5进行目标检测。预处理技术包括中值滤波、高斯模糊、CLAHE、图像锐化、直方图拉伸、CLAHE与图像锐化相结合。使用精度、召回率、F1分数、mAP和IoU指标评估性能。clahe -锐化组合的精度为0.934，召回率为0.932，F1评分为0.931，mAP为0.948，IoU为0.741，明显优于未经处理的图像。Grad-CAM可视化证实，预处理使模型能够有效地识别相关区域。本研究强调了预处理方法在提高牙科x线摄影诊断准确性方面的重要性，以改善治疗计划。

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引用次数: 0

The second-generation 3D-Printed localization grid for MRI-guided interventional procedures 用于mri引导介入手术的第二代3d打印定位网格

Intelligence-based medicine

Pub Date : 2026-01-06 DOI: 10.1016/j.ibmed.2026.100343

Queenie T.K. Shea , Wing Ki Wong , Louis Lee

Patient-specific three-dimensional (3D)-printed magnetic resonance imaging localization grids (MR-Grids) have demonstrated feasibility as an assistive tool for image-guided interventional procedures. However, a limitation of conventional designs arises when the target lesion lies in an imaging plane where the discrete grid markers are not visible. To address this challenge, we proposed a patient-specific MR-Grid incorporating a contrast-filled flexible tubing system arranged in a criss-cross pattern, ensuring visibility across all imaging slices.

The proposed MR-Grid comprises two primary components: (1) a 3D-printed patient-specific scaffold designed to conform to individual anatomical contours, and (2) a contrast-filled flexible tubing system inserted into the grooves of the scaffold.

The MR-Grid was tested in an interventional procedure using a biopsy phantom containing MRI-visible lesions to validate its utility. The grid facilitated precise needle insertion by identifying the optimal entry point under MR guidance, demonstrating its potential to improve accuracy and efficiency in image-guided interventions.

患者特异性三维（3D）打印的磁共振成像定位网格（MR-Grids）已被证明可作为图像引导介入手术的辅助工具。然而，当目标病变位于不可见的离散网格标记的成像平面时，传统设计的局限性就出现了。为了应对这一挑战，我们提出了一种针对患者的MR-Grid，其中包括以纵横交错模式排列的造影剂填充柔性管系统，确保所有成像切片的可见性。提出的MR-Grid包括两个主要组成部分：(1)3d打印的患者专用支架，设计符合个人解剖轮廓，以及(2)插入支架凹槽的对比填充柔性管道系统。MR-Grid在介入手术中进行了测试，使用包含mri可见病变的活检假体来验证其实用性。网格通过识别MR引导下的最佳切入点，促进了针的精确插入，展示了其在图像引导干预中提高准确性和效率的潜力。

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引用次数: 0

Automated right ventricular assessment in pediatric echocardiography via deep learning improves measurement reliability and reduces variability 通过深度学习的儿童超声心动图自动右心室评估提高了测量可靠性并减少了变异性

Intelligence-based medicine

Pub Date : 2026-01-06 DOI: 10.1016/j.ibmed.2026.100344

Ping He , Faith Zhu , Mariella Vargas-Gutierrez , Rakhika Kumar , Wei Hui , Yalin Lin , Mark K. Friedberg , Luc Mertens , Lauren Erdman

Background

Right ventricular (RV) function is important for pediatric cardiac evaluation but accurate and reproducible quantification of RV function is challenging. This study aimed to develop a deep learning (DL) model for RV functional assessment from echocardiography (ECHO) which out-performs current, manual methods.

Methods

We trained multiple DL segmentation models, using a dataset of 664 pediatric ECHOs, and proceeded with the best performing model for evaluation. DL model performance was assessed using the dice similarity coefficient (DSC) for segmentation, mean absolute error (MAE) for RVFAC. Blinded expert evaluation was conducted between ground truth and model generated segmentation outputs. A detailed analysis of inter-observer variability identified the main sources of RVFAC variability among four experts and the DL model, as well as opportunities for the model to improve RV assessment in practice.

Findings

The FCBFormer architecture yielded the best segmentation quality with DSC of 0.926 and MAE of 5.913 % for RVFAC prediction. Blinded expert review revealed that model generated segmentation was favored over human in 57.3 % of evaluated cases. All sources of variation were overcome by the RVFAC model: RV contour delineation, RV cardiac cycle selection, and RV end-diastolic/end-systolic frame identification.

Interpretation

This study demonstrates the feasibility of DL-based automated RV functional assessment for pediatric patients, offering a promising approach for more consistent and systematic longitudinal tracking of RV function than manual ECHO assessment.

背景右心室（RV）功能对儿童心脏评估很重要，但准确和可重复的量化右心室功能具有挑战性。本研究旨在开发一种深度学习（DL）模型，用于超声心动图（ECHO）的RV功能评估，该模型优于当前的手动方法。方法采用664例儿童超声数据集，对多个深度学习分割模型进行训练，选取表现最好的模型进行评价。使用骰子相似系数（DSC）进行分割，使用RVFAC的平均绝对误差（MAE）评估DL模型的性能。在地面真实值和模型生成的分割输出之间进行盲法专家评价。通过对观察者间变异性的详细分析，确定了四名专家和DL模型之间RVFAC变异性的主要来源，以及该模型在实践中改进RV评估的机会。结果FCBFormer结构对RVFAC预测的分割质量最好，DSC为0.926，MAE为5.913%。盲法专家评审显示，在57.3%的评估病例中，模型生成的分割优于人类。RVFAC模型克服了所有变异的来源：右心室轮廓描绘、右心室心动周期选择和右心室舒张末期/收缩末期框架识别。本研究证明了基于dl的儿童右心室功能自动评估的可行性，提供了一种比手动ECHO评估更一致和系统的右心室功能纵向跟踪的有希望的方法。

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引用次数: 0

Explainable AI for breast cancer detection: Biglycan biomarker classification with transfer learning 用于乳腺癌检测的可解释人工智能：Biglycan生物标志物分类与迁移学习

Intelligence-based medicine

Pub Date : 2026-01-01 DOI: 10.1016/j.ibmed.2025.100340

Md. Mominul Islam , Naime Akter , Md. Assaduzzaman , Md. Monir Hossain Shimul , Rahmatul Kabir Rasel Sarker

Background

Breast cancer is a leading global cause of cancer-related mortality, where early diagnosis is essential for improved survival outcomes. Although deep learning has shown strong performance in pathology image classification, many models remain difficult to interpret, which limits clinical trust and practical adoption. This study aimed to develop an explainable deep learning framework for classifying breast tissue based on Biglycan (BGN) biomarker expression.

Methods

Immunohistochemical photomicrographs from the publicly available Biglycan Breast Cancer Dataset were processed and classified into cancerous and healthy categories. Three transfer learning models, EfficientNet-B0, DenseNet-161, and ResNet-50, were fine-tuned using ImageNet pre-trained weights under a unified training setting with the Adam optimizer (learning rate = 0.001, batch size = 32, epochs = 50). Model performance was evaluated using accuracy, precision, recall, and F1-score. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to visualize tissue regions that contributed most to the model's predictions.

Results

EfficientNet-B0 achieved the best overall performance with 99 % accuracy, precision of 0.97, recall of 1.00 for the cancerous class, and an F1-score of 0.99. Grad-CAM heatmaps indicated that the model focused on diagnostically relevant tissue regions associated with strong BGN-related staining patterns. The best-performing model was integrated into a lightweight web-based application to enable real-time image upload and prediction.

Conclusion

This study presents an explainable transfer learning approach for BGN-driven breast tissue classification with strong performance on the Biglycan dataset. The integration of Grad-CAM provides region-level visual explanations that improve transparency, while the web deployment demonstrates a practical pathway for accessible decision support in digital pathology.

乳腺癌是全球癌症相关死亡的主要原因，早期诊断对于改善生存结果至关重要。尽管深度学习在病理图像分类方面表现出色，但许多模型仍然难以解释，这限制了临床信任和实际应用。本研究旨在开发一个可解释的深度学习框架，用于基于Biglycan （BGN）生物标志物表达对乳腺组织进行分类。方法对公开的Biglycan乳腺癌数据集的免疫组化显微照片进行处理，并将其分为癌变和健康两类。使用ImageNet预训练权值，在Adam优化器的统一训练设置下（学习率= 0.001，批大小= 32,epoch = 50），对effentnet - b0、DenseNet-161和ResNet-50三个迁移学习模型进行了微调。使用准确性、精密度、召回率和f1评分来评估模型的性能。为了提高可解释性，应用梯度加权类激活映射（Grad-CAM）来可视化对模型预测贡献最大的组织区域。结果fficientnet - b0的准确率为99%，精密度为0.97，查全率为1.00，f1评分为0.99。Grad-CAM热图显示，该模型专注于与bgn相关的强染色模式相关的诊断相关组织区域。性能最好的模型被集成到一个轻量级的基于web的应用程序中，以实现实时图像上传和预测。本研究提出了一种可解释的迁移学习方法，用于bgnn驱动的乳腺组织分类，在Biglycan数据集上具有较强的性能。Grad-CAM的集成提供了区域级的可视化解释，提高了透明度，而网络部署展示了数字病理学中可访问的决策支持的实用途径。

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引用次数: 0

Explainable stacked-ensemble prediction of ABL1 tyrosine-kinase inhibitor resistance: A metaheuristic-optimized pipeline ABL1酪氨酸激酶抑制剂耐药的可解释的堆叠系综预测：一个元启发式优化管道

Intelligence-based medicine

Pub Date : 2025-12-30 DOI: 10.1016/j.ibmed.2025.100341

Faris Hassan , Mohanad A. Deif , Alaa Zaghloul , Rania Elgohary , Mohammad Khishe

Accurate identification of ABL1 tyrosine-kinase mutations associated with therapeutic resistance can support timely adjustment of anticancer regimens; however, tyrosine-kinase inhibitor (TKI) mutation datasets are typically small and strongly imbalanced, which can bias model training and inflate performance if data processing is not strictly separated between training and testing. This study proposes an end-to-end, leakage-controlled machine-learning framework for ABL1 TKI-resistance prediction, in which all data-driven operations including feature selection and Synthetic Minority Oversampling Technique (SMOTE) are performed within cross-validation training folds only, preventing information from validation folds or the test set from influencing model development. Multiple base learners were independently tuned using metaheuristic hyperparameter optimization and then integrated using a stacked-ensemble architecture to reduce overfitting and improve generalization. On a held-out test set, the final ensemble achieved 91.9% accuracy, 75.0% precision, 96.9% specificity, 60.0% sensitivity, 66.7% F1-score, 0.626 MCC, 0.938 AUROC, and 0.729 PR-AUC, showing only a modest decline relative to cross-validation estimates. Post-hoc interpretability with Shapley additive explanations (SHAP) highlighted binding-score terms, mutation physicochemical descriptors, ligand flexibility, and local mutation-environment features as the main contributors, consistent with established principles of protein–ligand recognition. Overall, the results support a methodologically disciplined and interpretable approach for mutation-level resistance prediction, while motivating external validation and downstream evaluation of clinical utility.

准确识别与治疗耐药相关的ABL1酪氨酸激酶突变可以支持及时调整抗癌方案；然而，酪氨酸激酶抑制剂（TKI）突变数据集通常很小且极不平衡，如果数据处理没有严格区分训练和测试，这可能会影响模型训练和提高性能。本研究提出了一个端到端、泄漏控制的ABL1 tki抗性预测机器学习框架，其中所有数据驱动的操作，包括特征选择和合成少数派过采样技术（SMOTE），仅在交叉验证训练折叠中执行，防止验证折叠的信息或测试集影响模型开发。采用元启发式超参数优化对多个基学习器进行独立调优，然后采用堆叠集成架构进行集成，以减少过拟合，提高泛化能力。在一个固定的测试集上，最终的集合达到了91.9%的准确度、75.0%的精密度、96.9%的特异性、60.0%的灵敏度、66.7%的f1评分、0.626的MCC、0.938的AUROC和0.729的PR-AUC，相对于交叉验证的估计只有轻微的下降。Shapley加性解释（SHAP）的事后解释性突出了结合评分术语、突变物理化学描述符、配体灵活性和局部突变环境特征作为主要贡献者，与蛋白质配体识别的既定原则一致。总的来说，这些结果支持了一种方法学上有纪律和可解释的突变水平耐药预测方法，同时激发了外部验证和临床效用的下游评估。

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引用次数: 0

Exploring the role of synthetic data in the future of AI in healthcare: A scoping review of frameworks, challenges, and implications 探索人工智能在医疗保健领域的未来中合成数据的作用：框架、挑战和影响的范围审查

Intelligence-based medicine

Pub Date : 2025-12-29 DOI: 10.1016/j.ibmed.2025.100342

Mohammad Ishtiaque Rahman , Md Razuan Hossain , S.M. Sayem , Forhan Bin Emdad

Synthetic data has emerged as a transformative tool in healthcare, particularly in areas such as medical imaging, electronic health records (EHRs), and clinical trial simulation, where data privacy, diversity, and accessibility are critical. This scoping review examines current approaches to synthetic data generation in healthcare, with a focus on AI model training, privacy preservation, and bias mitigation. A comprehensive search of PubMed, IEEE Xplore, and ACM Digital Library yielded 2906 studies, of which 42 met the inclusion criteria. Key data generation techniques included generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, Bayesian networks, federated learning, recurrent neural networks (RNNs), large language models (LLMs), agent-based models, graph-based generators, and SMOTE-based oversampling. Applications ranged from diagnostic model development to privacy-preserving data sharing and educational simulation. However, the field faces persistent challenges, including inconsistent validation practices, the absence of standard benchmarks, high computational demands, and ethical concerns related to consent and bias. This review underscores the need for standardized evaluation protocols, clearer regulatory guidance, and multidisciplinary collaboration to ensure the safe, equitable, and effective use of synthetic data in healthcare AI. In addition to technical advances, the review highlights the socio-technical implications of synthetic data adoption, including its impact on health equity, patient trust, and clinical decision-making.

合成数据已成为医疗保健领域的变革性工具，特别是在医疗成像、电子健康记录（EHRs）和临床试验模拟等领域，这些领域的数据隐私、多样性和可访问性至关重要。本范围审查审查了医疗保健中合成数据生成的当前方法，重点是人工智能模型训练、隐私保护和偏见缓解。综合检索PubMed、IEEE explore和ACM数字图书馆得到2906篇研究，其中42篇符合纳入标准。关键的数据生成技术包括生成对抗网络（GANs）、变分自编码器（VAEs）、扩散模型、贝叶斯网络、联邦学习、循环神经网络（rnn）、大型语言模型（LLMs）、基于代理的模型、基于图的生成器和基于smote的过采样。应用范围从诊断模型开发到保护隐私的数据共享和教育模拟。然而，该领域面临着持续的挑战，包括不一致的验证实践、缺乏标准基准、高计算需求以及与同意和偏见相关的伦理问题。本综述强调需要标准化的评估方案、更清晰的监管指导和多学科合作，以确保在医疗保健人工智能中安全、公平和有效地使用合成数据。除了技术进步外，该审查还强调了采用综合数据的社会技术影响，包括其对卫生公平、患者信任和临床决策的影响。

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引用次数: 0

The potential of artificial intelligence in advancing neuroscience: A systematic review of current applications and models 人工智能在推进神经科学方面的潜力：对当前应用和模型的系统回顾

Intelligence-based medicine

Pub Date : 2025-12-27 DOI: 10.1016/j.ibmed.2025.100338

Fatemeh Afroughi , SeyedAhmad SeyedAlinaghi , Pegah Mirzapour , Shabnam Shirdel , Zohal Parmoon , Mohammad Musa Khorshidi , Somaye Mansouri , Mahdi Sheykhi , Yusuf Popoola , Esmaeil Mehraeen

Introduction

Artificial intelligence (AI) is the simulation of human intelligence, in which machines perform problem-solving like the human brain. AI and neuroscience are interrelated. In this study, a systematic review of current AI models and applications was conducted to consider the potential of AI in advancing neuroscience.

Methods

Relevant articles were selected based on a search in three reputable databases, including Web of Science, PubMed, and Scopus. Two independent researchers conducted the selection process in two stages.

Results

A total of 99 studies (2019–2024) met PRISMA criteria. Of these, 83 studies focused on specific brain disorders—most notably Alzheimer's disease (n = 26), stroke (n = 14), epilepsy (n = 7), and Parkinson's disease (n = 7)—while 22 addressed broader neuroscience applications. A range of AI methods were applied, including traditional machine learning techniques (e.g., Support Vector Machines (SVM), Random Forest) and deep learning approaches (e.g., Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs)), with several studies employing hybrid models. A comparative analysis of study designs revealed a heavy reliance on public datasets (e.g., Alzheimers Disease Neuroimaging Initiative (ADNI)) for Alzheimer's research, while studies on other disorders predominantly utilized private cohorts. Regarding validation, the majority of studies employed internal cross-validation strategies, with fewer utilizing independent external datasets to test generalizability.

Conclusion

The transformative potential of AI in advancing neuroscience lies in its ability to increase diagnostic accuracy, predict disease progression, and enhance imaging techniques. Future research should focus on refining AI methods to enhance generalizability and foster collaborations between AI practitioners and neuroscientists.

人工智能（AI）是对人类智能的模拟，机器可以像人脑一样解决问题。人工智能和神经科学是相互关联的。在本研究中，对当前人工智能模型和应用进行了系统回顾，以考虑人工智能在推进神经科学方面的潜力。方法在Web of Science、PubMed、Scopus三个知名数据库中检索相关文章。两位独立的研究人员分两个阶段进行了选择。结果2019-2024年共有99项研究符合PRISMA标准。其中，83项研究集中于特定的脑部疾病——最著名的是阿尔茨海默病（n = 26）、中风（n = 14）、癫痫（n = 7）和帕金森病（n = 7）——而22项研究涉及更广泛的神经科学应用。应用了一系列人工智能方法，包括传统的机器学习技术（例如，支持向量机（SVM），随机森林）和深度学习方法（例如，卷积神经网络（cnn），生成对抗网络（gan）），以及一些采用混合模型的研究。对研究设计的比较分析显示，阿尔茨海默病研究严重依赖公共数据集（例如，阿尔茨海默病神经影像学倡议（ADNI）），而对其他疾病的研究主要使用私人队列。在验证方面，大多数研究采用内部交叉验证策略，较少使用独立的外部数据集来测试概括性。结论人工智能在推进神经科学方面的变革潜力在于其提高诊断准确性、预测疾病进展和增强成像技术的能力。未来的研究应侧重于改进人工智能方法，以提高通用性，并促进人工智能从业者和神经科学家之间的合作。

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引用次数: 0

A scoping review of machine learning models for predicting preterm birth and miscarriage: Mapping the landscape and the performance-validation paradox 预测早产和流产的机器学习模型的范围审查：绘制景观和性能验证悖论

Intelligence-based medicine

Pub Date : 2025-12-22 DOI: 10.1016/j.ibmed.2025.100337

Golnoush Shahraki , Elias Mazrooei Rad , Mohammad Heidari

Objective

This scoping review aims to summarize how machine learning (ML) has been applied to predict adverse pregnancy outcomes—particularly preterm birth and miscarriage—by comparing data sources, model designs, and reported performance.

Methods

Evidence from 40 eligible studies was systematically reviewed. For each, we extracted details on the type of data used (e.g., clinical records, imaging, biomarkers), sample size, ML algorithms, and major performance metrics such as AUC and accuracy. Recurring strengths and weaknesses were identified through thematic analysis.

Results

The reviewed studies drew on a wide range of data sources—from large electronic health records (EHRs) to imaging and time-series physiological signals. Tree-based algorithms and support vector machines generally showed strong predictive performance, with several studies reporting AUC values above 0.90. However, most investigations were limited by small, single-center datasets and lacked external validation, raising concerns about generalizability and clinical interpretability.

Conclusion

Machine learning approaches could meaningfully improve how clinicians anticipate adverse pregnancy outcomes, but their clinical use remains premature. Progress will depend on studies that include larger and more diverse populations, apply rigorous external validation, and focus on developing models that clinicians can interpret and act upon.

本综述旨在通过比较数据来源、模型设计和报告的性能，总结机器学习（ML）如何应用于预测不良妊娠结局（特别是早产和流产）。方法系统回顾40项符合条件的研究的证据。对于每种方法，我们提取了所使用数据类型（例如，临床记录、成像、生物标志物）、样本量、ML算法和主要性能指标（如AUC和准确性）的详细信息。通过专题分析确定了反复出现的优点和缺点。结果回顾的研究利用了广泛的数据来源——从大型电子健康记录（EHRs）到成像和时间序列生理信号。基于树的算法和支持向量机普遍表现出较强的预测性能，有几项研究报告AUC值在0.90以上。然而，大多数研究受限于小型单中心数据集，缺乏外部验证，引起了对普遍性和临床可解释性的担忧。结论机器学习方法可以显著提高临床医生对不良妊娠结局的预测，但其临床应用尚不成熟。进展将取决于包括更大和更多样化人群的研究，应用严格的外部验证，并专注于开发临床医生可以解释和采取行动的模型。

{"title":"A scoping review of machine learning models for predicting preterm birth and miscarriage: Mapping the landscape and the performance-validation paradox","authors":"Golnoush Shahraki , Elias Mazrooei Rad , Mohammad Heidari","doi":"10.1016/j.ibmed.2025.100337","DOIUrl":"10.1016/j.ibmed.2025.100337","url":null,"abstract":"<div><h3>Objective</h3><div>This scoping review aims to summarize how machine learning (ML) has been applied to predict adverse pregnancy outcomes—particularly preterm birth and miscarriage—by comparing data sources, model designs, and reported performance.</div></div><div><h3>Methods</h3><div>Evidence from 40 eligible studies was systematically reviewed. For each, we extracted details on the type of data used (e.g., clinical records, imaging, biomarkers), sample size, ML algorithms, and major performance metrics such as AUC and accuracy. Recurring strengths and weaknesses were identified through thematic analysis.</div></div><div><h3>Results</h3><div>The reviewed studies drew on a wide range of data sources—from large electronic health records (EHRs) to imaging and time-series physiological signals. Tree-based algorithms and support vector machines generally showed strong predictive performance, with several studies reporting AUC values above 0.90. However, most investigations were limited by small, single-center datasets and lacked external validation, raising concerns about generalizability and clinical interpretability.</div></div><div><h3>Conclusion</h3><div>Machine learning approaches could meaningfully improve how clinicians anticipate adverse pregnancy outcomes, but their clinical use remains premature. Progress will depend on studies that include larger and more diverse populations, apply rigorous external validation, and focus on developing models that clinicians can interpret and act upon.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100337"},"PeriodicalIF":0.0,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0