Intelligence-based medicine最新文献_第5页

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 Epub Date: 2025-01-31 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

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 Epub Date: 2025-08-04 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

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 Epub Date: 2025-04-23 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

Segmentation of low-resolution MII human oocyte images using data-efficient meta-learning 使用数据高效元学习的低分辨率MII人类卵母细胞图像分割

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-12-03 DOI: 10.1016/j.ibmed.2025.100327

Mahshid Alizadeh Kiashi, Ashkan Mousazadeh Soustani, Seyed Abolghasem Mirroshandel

Although many infertility problems in humans are treatable today, some of these methods have problems that need to be addressed. One of the ways to solve infertility problems is to use in vitro fertilization. In this method, if a suitable oocyte with a high chance of fertility is not selected, it may lead to multiple births or even infertility. Identifying the most suitable cell with the highest chance of fertility is a very difficult task even for embryologists. Hence, this research focuses on designing a deep learning framework to take on the challenging task of segmenting low-resolution oocyte microscopic images and accurately delineating and distinguishing the boundaries of the three important regions of the oocyte, i.e., zona pellucida (ZP), perivitelline space (PVS), and ooplasm. Then it is calculated with the key indicators in the health of each cell and compared with the true values to evaluate the level of abnormalities and select the most suitable cell for in vitro fertilization. Finally, after testing on 253 images of the test set, in binary segmentation, the average accuracy is 99.17 in ooplasm, 97.14 in PVS, and 94.78 in ZP, and in multi-class segmentation, the average accuracy is 99.30 in ooplasm, 97.36 in PVS, and 94.95 in ZP. These values have been obtained by training on 300 microscopic images of human oocytes, which have been reduced to less than half compared to previous studies.

虽然今天人类的许多不孕症是可以治疗的，但其中一些方法存在需要解决的问题。解决不孕问题的方法之一是使用体外受精。在这种方法中，如果没有选择生育机会高的合适卵母细胞，可能会导致多胎甚至不孕。即使对胚胎学家来说，确定最合适的细胞和最高的生育机会也是一项非常困难的任务。因此，本研究的重点是设计一个深度学习框架，以承担低分辨率卵母细胞显微图像的分割任务，并准确描绘和区分卵母细胞的三个重要区域，即透明带（ZP）、卵泡周围空间（PVS）和卵浆的边界。然后与各细胞健康状况的关键指标进行计算，并与真实值进行比较，评估异常程度，选择最适合体外受精的细胞。最后，对测试集的253张图像进行测试，在二值分割中，卵浆的平均准确率为99.17，PVS的平均准确率为97.14，ZP的平均准确率为94.78；在多类分割中，卵浆的平均准确率为99.30，PVS的平均准确率为97.36，ZP的平均准确率为94.95。这些值是通过对300个人类卵母细胞的显微图像进行训练获得的，与以前的研究相比，这些图像减少到不到一半。

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引用次数: 0

3D brain tumor segmentation in MRI images using hierarchical adaptive pruning of non-tumor regions 基于非肿瘤区域分层自适应剪枝的MRI图像三维脑肿瘤分割

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-10-10 DOI: 10.1016/j.ibmed.2025.100303

Ali Mehrabi, Nasser Mehrshad

Background

The detection of brain tumors in MRI images has significantly improved with the advent of deep learning methods. However, these approaches often suffer from high complexity, computational cost, and the need for extensive annotated training data, making them less practical for real-time and patient-centered diagnostic systems. To address these challenges, this study introduces a perceptually inspired, algorithmic method that mimics the diagnostic behavior of physicians, offering a lightweight and interpretable alternative for brain tumor segmentation.

Method

We propose a novel adaptive hierarchical pruning algorithm for 3D MRI brain images that iteratively removes low-intensity, non-tumor voxels based on the statistical distribution of intensities. The tumor region is identified through the comparison of the remaining pixel intensity values statistics. The pruning automatically stops when the mean and median of the remaining voxels converge, leaving the candidate tumor region.

Results

The proposed algorithm was evaluated on all patients of the BraTS2019 and BraTS2023 datasets, achieving segmentation accuracies of 99.1 % and 99.13 %, respectively. It demonstrated high sensitivity and specificity compared to several deep learning methods, showing robust performance across diverse patient scans.

Conclusions

This study demonstrates that a simple, perceptually driven segmentation algorithm can match or outperform complex deep learning models, particularly in clinical settings where lightweight, transparent, and efficient tools are essential.

随着深度学习方法的出现，MRI图像中脑肿瘤的检测有了显著的提高。然而，这些方法通常存在复杂性高、计算成本高、需要大量带注释的训练数据等问题，这使得它们在实时和以患者为中心的诊断系统中不太实用。为了解决这些挑战，本研究引入了一种感知启发的算法方法，该方法模仿医生的诊断行为，为脑肿瘤分割提供了一种轻量级且可解释的替代方法。方法提出了一种新的自适应分层剪枝算法，该算法基于强度的统计分布，迭代地去除低强度的非肿瘤体素。通过比较剩余的像素强度值统计来识别肿瘤区域。当剩余体素的均值和中值收敛时，剪枝自动停止，留下候选肿瘤区域。结果该算法在BraTS2019和BraTS2023数据集的所有患者上进行了评估，分割准确率分别达到99.1%和99.13%。与几种深度学习方法相比，它表现出高灵敏度和特异性，在不同的患者扫描中表现出强大的性能。本研究表明，一个简单的、感知驱动的分割算法可以匹配或优于复杂的深度学习模型，特别是在临床环境中，轻量级、透明和高效的工具是必不可少的。

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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 Epub Date: 2025-01-08 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

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 Epub Date: 2025-01-31 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

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 Epub Date: 2025-03-04 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

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 Epub Date: 2025-02-19 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

Regulating intelligence: a systematic analysis of safety, ethics, and equity in artificial intelligence driven healthcare 规范智能：对人工智能驱动的医疗保健中的安全、伦理和公平进行系统分析

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-11-25 DOI: 10.1016/j.ibmed.2025.100320

Muayyad Ahmad

Objective

The growing use of artificial intelligence (AI) in healthcare demands robust regulatory frameworks to ensure safety, ethics, and legal compliance, particularly regarding algorithmic transparency, data privacy, and bias. This systematic review analyzes AI regulations and grey literature (2019–2024) from the Food and Drug Administration (FDA), the World Health Organization (WHO), the Organization for Economic Co-operation and Development (OECD), and the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC).

Methods

This systematic review, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analysed 26 studies (Peer-reviewed studies: n = 17 & Grey literature: n = 9) on AI regulatory frameworks in healthcare between 2019 and 2024. A systematic literature search and rigorous inclusion criteria ensured relevance, with data consolidated into safety, effectiveness, and ethical themes. The analysis integrates a low-middle income countries (LMIC) perspective via WHO policy and a handful of studies, but the main academic body is drawn primarily from high-income contexts.

Bias risk was assessed systematically.

Results

The reviewed studies highlight the critical need for AI regulatory frameworks in healthcare, focusing on patient safety, ethics, and trust. Key findings stress the necessity for transparent, equitable integration and clear guidelines addressing bias, legal issues, and validation. Grey literature consistently emphasizes risk-based safety models and principles like transparency and human oversight. However, a significant gap remains in translating equity commitments into enforceable standards for bias mitigation, underscoring a critical need for future regulatory action.

Conclusion

This review identifies critical gaps in AI regulatory frameworks, particularly in equity, real-world validation, and liability, and proposes actionable, interdisciplinary strategies to ensure AI's safe, ethical, and equitable integration into healthcare.

人工智能（AI）在医疗保健领域的日益广泛应用需要强有力的监管框架，以确保安全、道德和法律合规性，特别是在算法透明度、数据隐私和偏见方面。本系统综述分析了美国食品药品监督管理局（FDA）、世界卫生组织（WHO）、经济合作与发展组织（OECD）和国际标准化组织/国际电工委员会（ISO/IEC）的人工智能法规和灰色文献（2019-2024）。方法：本系统评价遵循系统评价和荟萃分析（PRISMA）指南的首选报告项目，分析了26项关于2019年至2024年医疗保健领域人工智能监管框架的研究（同行评审研究：n = 17；灰色文献：n = 9）。系统的文献检索和严格的纳入标准确保了相关性，并将数据整合到安全性、有效性和伦理主题中。该分析通过世卫组织政策和少数研究整合了中低收入国家的视角，但主要学术机构主要来自高收入背景。系统评估偏倚风险。结果：回顾的研究强调了在医疗保健领域建立人工智能监管框架的迫切需要，重点是患者安全、道德和信任。主要结论强调了透明、公平整合的必要性，以及解决偏见、法律问题和有效性的明确指导方针。灰色文献一贯强调基于风险的安全模型和原则，如透明度和人为监督。然而，在将股权承诺转化为可执行的减轻偏见标准方面仍存在重大差距，这突出表明迫切需要今后采取监管行动。本综述确定了人工智能监管框架的关键差距，特别是在公平、现实验证和责任方面，并提出了可操作的跨学科战略，以确保人工智能安全、道德和公平地融入医疗保健。

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