Neuroscience informatics最新文献_第4页

Revealing spatiotemporal neural activation patterns in electrocorticography recordings of human speech production by mutual information 通过互信息揭示人类语音产生的脑皮质电图记录中的时空神经激活模式

Neuroscience informatics

Pub Date : 2025-12-01 Epub Date: 2025-08-25 DOI: 10.1016/j.neuri.2025.100232

Julio Kovacs , Dean Krusienski , Minu Maninder , Willy Wriggers

Background

Spatiotemporal mapping of neural activity during continuous speech production has been traditionally approached using correlation coefficient (CC) analysis between cortical signals and speech recordings. A prior study employed this approach using electrocorticography (ECoG) data from participants who underwent invasive intracranial monitoring for epilepsy. However, CC cannot detect nonlinear relationships and is dominated by the correspondence between periods of silence and of non-silence.

New Method

We introduce the mutual information (MI) measure, which can capture both linear and nonlinear dependencies. We validated CC and MI on the sub-second spatiotemporal brain activity recorded during continuous speech tasks. To refine the results, we also implemented a novel “masked analysis”, which excludes periods of silence, and compared it with the standard (unmasked) analysis.

Results

Our findings show that previous results, obtained through more complex statistical methods, can be reproduced using CC with an appropriate threshold cutoff. Moreover, both standard MI and CC are influenced by broad transitions between silence and speech, but masking allows the detection of intrinsic correspondences between the two signals, revealing more localized activity.

Comparison with existing methods

Compared to the standard CC, masked MI highlights early prefrontal and premotor activations emerging ∼440 ms before speech onset. It also identifies sharper, anatomically coherent activations in key speech-related areas, demonstrating improved sensitivity to the fine-grained spatiotemporal dynamics of continuous speech production.

Conclusion

These findings deepen our understanding of the neural pathways underlying speech and underscore the potential of masked MI for advancing neural decoding in future speech-based brain-computer interface applications.

在连续的语音产生过程中，神经活动的时空映射传统上是通过皮质信号和语音记录之间的相关系数（CC）分析来实现的。先前的一项研究采用了这种方法，使用了来自接受侵入性颅内癫痫监测的参与者的皮质电图（ECoG）数据。然而，CC不能检测非线性关系，并且被沉默和非沉默之间的对应关系所主导。新方法引入互信息（MI）度量，可以同时捕获线性和非线性依赖关系。我们在连续语音任务中记录的亚秒时空大脑活动上验证了CC和MI。为了改进结果，我们还实现了一种新的“屏蔽分析”，它排除了沉默期，并将其与标准（未屏蔽）分析进行了比较。结果我们的研究结果表明，以前的结果，通过更复杂的统计方法，可以复制使用CC与适当的阈值截断。此外，标准MI和CC都受到沉默和说话之间广泛过渡的影响，但屏蔽允许检测两个信号之间的内在对应关系，揭示更多的局部活动。与现有方法的比较与标准CC相比，掩蔽性MI突出了在言语开始前约440 ms出现的早期前额叶和运动前激活。它还在关键的语音相关区域识别出更清晰、解剖学上连贯的激活，证明了对连续语音产生的细粒度时空动态的灵敏度提高。结论这些发现加深了我们对语音背后的神经通路的理解，并强调了掩膜MI在未来基于语音的脑机接口应用中推进神经解码的潜力。

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引用次数: 0

A comparative study of hybrid decision tree–deep learning models in the detection of intracranial arachnoid cysts 混合决策树-深度学习模型在颅内蛛网膜囊肿检测中的比较研究

Neuroscience informatics

Pub Date : 2025-12-01 Epub Date: 2025-09-09 DOI: 10.1016/j.neuri.2025.100234

Aziz Ilyas Ozturk , Osman Yıldırım , Ebru İdman , Emrah İdman

Intracranial arachnoid cysts are fluid-filled lesions within the arachnoid membrane, which pose significant diagnostic challenges due to their varying sizes, subtle radiographic characteristics, and often unclear clinical correlations. Traditional diagnostic methods, such as MRI or CT imaging, rely on expert interpretation but suffer from issues like inter-observer variability and diagnostic delays, especially for small or atypically located cysts. To address these challenges, this study integrates machine learning (ML) and deep learning (DL) techniques into neuroimaging diagnostics, introducing three novel hybrid models: DecisionTree-ViT, DecisionTree-Random Forest, and DecisionTree-ResNet50. The DecisionTree-Random Forest hybrid model showed remarkable performance, achieving 96.3% accuracy and 0.98 AUC in differentiating arachnoid cysts from normal cerebrospinal fluid spaces and other intracranial cystic lesions. This model combines deep learning's pattern recognition strengths with decision tree transparency, meeting the clinical need for both accuracy and explainability. The DecisionTree-ResNet50 variant excelled in detecting small (<1 cm) cysts, with a sensitivity of 89.7%, outperforming standalone ResNet50 (82.4%). Specialized contrast-enhancement protocols and anatomically constrained augmentation techniques were applied to address class imbalance and improve model calibration. The DecisionTree-ViT model also demonstrated strong performance, with 94% accuracy and well-calibrated confidence estimates, making it reliable for clinical decision-making. The study compares these hybrid models against pure deep learning and traditional machine learning approaches, highlighting their superior performance in challenging diagnostic scenarios. The integrated interpretability features allow radiologists to validate algorithmic findings, fostering trust in AI-assisted diagnostics. This research showcases the potential of hybrid AI models to transform neuroimaging diagnostics and improve patient outcomes.

颅内蛛网膜囊肿是蛛网膜内充满液体的病变，由于其大小不一，放射学特征微妙，临床相关性不明确，给诊断带来了重大挑战。传统的诊断方法，如MRI或CT成像，依赖于专家的解释，但存在观察者之间的差异和诊断延迟等问题，特别是对于小的或非典型位置的囊肿。为了解决这些挑战，本研究将机器学习（ML）和深度学习（DL）技术集成到神经成像诊断中，引入了三种新的混合模型：DecisionTree-ViT、DecisionTree-Random Forest和DecisionTree-ResNet50。DecisionTree-Random Forest混合模型对蛛网膜囊肿与正常脑脊液间隙及其他颅内囊性病变的鉴别准确率为96.3%，AUC为0.98。该模型将深度学习的模式识别优势与决策树透明度相结合，满足了临床对准确性和可解释性的需求。DecisionTree-ResNet50变体在检测小（1 cm）囊肿方面表现出色，灵敏度为89.7%，优于单独的ResNet50（82.4%）。专门的对比度增强方案和解剖学约束增强技术应用于解决类别不平衡和改进模型校准。DecisionTree-ViT模型也表现出了很强的性能，准确率为94%，置信度估计良好，可用于临床决策。该研究将这些混合模型与纯深度学习和传统机器学习方法进行了比较，突出了它们在具有挑战性的诊断场景中的优越性能。集成的可解释性功能允许放射科医生验证算法结果，促进对人工智能辅助诊断的信任。这项研究展示了混合人工智能模型在改变神经影像学诊断和改善患者预后方面的潜力。

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引用次数: 0

Feature analysis of depression patients' house-tree-person drawings using convolutional neural networks 基于卷积神经网络的抑郁症患者屋树人图特征分析

Neuroscience informatics

Pub Date : 2025-12-01 Epub Date: 2025-10-28 DOI: 10.1016/j.neuri.2025.100239

Liu Zhenyi , Ye Cun Chun

Objective

This study explores the use of Convolutional Neural Networks (CNNs) to analyze House-Tree-Person (HTP) drawings for the classification of depression severity, addressing the subjectivity and limitations of traditional psychological assessment methods.

Methods

A dataset of 1,020 HTP drawings from adults aged 25–30 was collected, consisting of 432 healthy controls, 336 patients with moderate depression, and 252 patients with severe depression. The drawings were labeled based on the Hamilton Depression Scale (HAMD). A CNN model was trained and optimized using cross-validation to extract and classify depression-related visual features. The model's performance was evaluated using accuracy, recall, F1-score, and area under the ROC curve (AUC).

Results

The CNN model demonstrated a classification accuracy of 89% for distinguishing normal and depressed individuals, with an AUC of 0.96. In differentiating moderate from severe depression, the model achieved an AUC of 1.00, indicating near-perfect classification. The extracted features, such as line clarity and detail richness, correlated with depression severity, confirming their diagnostic relevance.

Conclusion

The study validates CNN-based image analysis as an effective and objective method for depression assessment using HTP drawings. The model not only improves accuracy but also offers potential applications in automated mental health screening. Future research should integrate multimodal data, such as speech and physiological signals, to enhance diagnostic precision.

目的探讨利用卷积神经网络（cnn）对屋树人（House-Tree-Person， HTP）图进行抑郁症严重程度分类的方法，解决传统心理评估方法的主观性和局限性。方法收集25 ~ 30岁成人HTP图1020张，其中健康对照432例，中度抑郁患者336例，重度抑郁患者252例。这些图画是根据汉密尔顿抑郁量表（HAMD）进行标记的。使用交叉验证对CNN模型进行训练和优化，提取和分类抑郁症相关的视觉特征。使用准确率、召回率、f1评分和ROC曲线下面积（AUC）来评估模型的性能。结果CNN模型对正常和抑郁个体的分类准确率为89%，AUC为0.96。在区分中度抑郁症和重度抑郁症时，该模型的AUC为1.00，表明分类接近完美。提取的特征，如线条清晰度和细节丰富度，与抑郁症严重程度相关，证实了它们的诊断相关性。结论基于cnn的图像分析是一种有效、客观的HTP图抑郁评价方法。该模型不仅提高了准确性，而且在自动心理健康筛查中提供了潜在的应用。未来的研究应整合多模态数据，如语音和生理信号，以提高诊断精度。

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引用次数: 0

Decoding memory with explainable AI: A large-scale EEG-based machine learning study of encoding vs. retrieval 用可解释的人工智能解码记忆：基于脑电图的大规模机器学习研究编码与检索

Neuroscience informatics

Pub Date : 2025-12-01 Epub Date: 2025-08-14 DOI: 10.1016/j.neuri.2025.100227

Mohammed Tawshif Hossain , Adnan Sami Sarker , Arnab Chowdhury , Rajesh Mitra , Raiyan Rahman , M.R.C. Mahdy

Understanding the distinct neural signatures that differentiate memory encoding from retrieval remains a key challenge in cognitive neuroscience. This study applies machine learning to EEG data from the Penn Electrophysiology of Encoding and Retrieval Study (PEERS), involving 100 participants across over 400 sessions, to classify these cognitive states. We used Discrete Wavelet Transform (DWT) on EEG signals from six critical brain regions and evaluated seven machine learning models. Gradient Boosting emerged as the most effective classifier, achieving 81.97% accuracy and a 91.62% AUC. To interpret this performance, we applied Explainable AI (XAI) methods, specifically SHapley Additive exPlanations (SHAP). This analysis revealed that theta-band relative energy, especially in the Left and Right Anterior Superior (LAS/RAS) regions, was the most influential predictor. Low theta-band energy and RMS values were particularly indicative of encoding states. Topographic maps provided further validation, showing significant neural differences in anterior regions, notably within the theta range. However, the study is limited by the use of a fixed 2.5 s analysis window and demographic skew in the dataset, which may affect generalizability. Future work should address these issues through varied windowing strategies and more diverse populations. This study advances understanding of cognitive memory processes and supports the development of adaptive, memory-aware AI systems, contributing to both neuroscience and neurotechnology.

理解区分记忆编码和检索的不同神经特征仍然是认知神经科学的一个关键挑战。这项研究将机器学习应用于宾夕法尼亚大学编码和检索电生理学研究（PEERS）的脑电图数据，涉及100名参与者，跨越400多个会议，对这些认知状态进行分类。我们使用离散小波变换（DWT）对来自6个关键脑区的脑电图信号进行处理，并评估了7种机器学习模型。Gradient Boosting是最有效的分类器，准确率达到81.97%，AUC为91.62%。为了解释这种表现，我们应用了可解释人工智能（Explainable AI， XAI）方法，特别是SHapley加性解释（SHAP）。该分析显示，theta波段相对能量，特别是在左右前上（LAS/RAS）区域，是最具影响力的预测因子。低波段能量和RMS值特别表明编码状态。地形图提供了进一步的验证，显示了显著的神经差异在前部区域，特别是在θ波范围内。然而，该研究受到使用固定的2.5 s分析窗口和数据集中的人口统计偏差的限制，这可能会影响通用性。未来的工作应该通过不同的窗口策略和更多样化的人群来解决这些问题。这项研究促进了对认知记忆过程的理解，并支持了自适应、记忆感知的人工智能系统的发展，为神经科学和神经技术做出了贡献。

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引用次数: 0

Exploring community pharmacist's psychological intentions to adopt generative artificial intelligence (GenAI) chatbots for patient information, education, and counseling 探索社区药剂师采用生成式人工智能（GenAI）聊天机器人进行患者信息、教育和咨询的心理意向

Neuroscience informatics

Pub Date : 2025-09-01 Epub Date: 2025-06-05 DOI: 10.1016/j.neuri.2025.100213

Hafidz Ihsan Hidayatullah , Muhammad Taufiq Saifullah , Muhammad Thesa Ghozali , Ayesha Aziz

Generative AI (GenAI) chatbots, driven by advanced machine learning algorithms, are emerging as transformative tools for enhancing patient education, information dissemination, and counseling (EIC) in healthcare. This study investigated the psychological determinants of community pharmacists' intentions to adopt GenAI chatbots using the Extended Technology Acceptance Model (ETAM). A cross-sectional survey of 240 licensed community pharmacists across several Indonesian provinces assessed key constructs, including self-efficacy (SE), perceived usefulness (PU), perceived ease of use (PEU), attitude toward technology (ATT), trust (TT), and behavioral intention (BI). Structural equation modeling revealed that SE significantly influenced PU (

β = 0.37

) and PEU (

β = 0.57

), indicating that confidence in using technology positively affects perceived utility and usability. PU further predicted ATT (

β = 0.39

) and BI (

β = 0.236

), emphasizing the motivational role of perceived benefits. Trust emerged as a crucial mediator, channeling favorable attitudes into actionable behavioral intentions (indirect

β = 0.148

). The model demonstrated strong fit indices (

χ^{2} = 263.09

, RMSEA = 0.019, GFI = 0.915, CFI = 0.991), supporting the psychological framework. These findings highlight the importance of fostering trust, improving perceived usability, and enhancing self-efficacy through targeted training to promote GenAI chatbot adoption. Future research should explore longitudinal behavioral changes and contextual influences to support sustainable AI integration in pharmacy practice.

由先进机器学习算法驱动的生成式人工智能（GenAI）聊天机器人正在成为增强医疗保健领域患者教育、信息传播和咨询（EIC）的变革性工具。本研究使用扩展技术接受模型（ETAM）调查了社区药剂师采用GenAI聊天机器人意图的心理决定因素。对印度尼西亚几个省的240名有执照的社区药剂师进行了横断面调查，评估了关键结构，包括自我效能感（SE）、感知有用性（PU）、感知易用性（PEU）、对技术的态度（ATT）、信任（TT）和行为意向（BI）。结构方程模型显示，SE显著影响PU （β=0.37）和PEU (β=0.57)，表明使用技术的信心正向影响感知效用和可用性。PU进一步预测了ATT （β=0.39）和BI (β=0.236)，强调了感知利益的激励作用。信任是一个重要的中介，将有利的态度转化为可操作的行为意图（间接β=0.148）。模型拟合指数较强（χ2=263.09, RMSEA = 0.019, GFI = 0.915, CFI = 0.991），支持心理框架。这些发现强调了通过有针对性的培训来促进GenAI聊天机器人的采用，培养信任、提高感知可用性和增强自我效能的重要性。未来的研究应该探索纵向行为变化和环境影响，以支持人工智能在药学实践中的可持续整合。

{"title":"Exploring community pharmacist's psychological intentions to adopt generative artificial intelligence (GenAI) chatbots for patient information, education, and counseling","authors":"Hafidz Ihsan Hidayatullah , Muhammad Taufiq Saifullah , Muhammad Thesa Ghozali , Ayesha Aziz","doi":"10.1016/j.neuri.2025.100213","DOIUrl":"10.1016/j.neuri.2025.100213","url":null,"abstract":"<div><div>Generative AI (GenAI) chatbots, driven by advanced machine learning algorithms, are emerging as transformative tools for enhancing patient education, information dissemination, and counseling (EIC) in healthcare. This study investigated the psychological determinants of community pharmacists' intentions to adopt GenAI chatbots using the Extended Technology Acceptance Model (ETAM). A cross-sectional survey of 240 licensed community pharmacists across several Indonesian provinces assessed key constructs, including self-efficacy (SE), perceived usefulness (PU), perceived ease of use (PEU), attitude toward technology (ATT), trust (TT), and behavioral intention (BI). Structural equation modeling revealed that SE significantly influenced PU (<span><math><mi>β</mi><mo>=</mo><mn>0.37</mn></math></span>) and PEU (<span><math><mi>β</mi><mo>=</mo><mn>0.57</mn></math></span>), indicating that confidence in using technology positively affects perceived utility and usability. PU further predicted ATT (<span><math><mi>β</mi><mo>=</mo><mn>0.39</mn></math></span>) and BI (<span><math><mi>β</mi><mo>=</mo><mn>0.236</mn></math></span>), emphasizing the motivational role of perceived benefits. Trust emerged as a crucial mediator, channeling favorable attitudes into actionable behavioral intentions (indirect <span><math><mi>β</mi><mo>=</mo><mn>0.148</mn></math></span>). The model demonstrated strong fit indices (<span><math><msup><mrow><mi>χ</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>263.09</mn></math></span>, RMSEA = 0.019, GFI = 0.915, CFI = 0.991), supporting the psychological framework. These findings highlight the importance of fostering trust, improving perceived usability, and enhancing self-efficacy through targeted training to promote GenAI chatbot adoption. Future research should explore longitudinal behavioral changes and contextual influences to support sustainable AI integration in pharmacy practice.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 3","pages":"Article 100213"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272556","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

Deep learning model for patient emotion recognition using EEG-tNIRS data 基于EEG-tNIRS数据的患者情绪识别深度学习模型

Neuroscience informatics

Pub Date : 2025-09-01 Epub Date: 2025-07-22 DOI: 10.1016/j.neuri.2025.100219

Mohan Raparthi , Nischay Reddy Mitta , Vinay Kumar Dunka , Sowmya Gudekota , Sandeep Pushyamitra Pattyam , Venkata Siva Prakash Nimmagadda

This study presents a novel approach that integrates electroencephalogram (EEG) and functional near-infrared spectroscopy (tNIRS) data to enhance emotion classification accuracy. A Modality-Attentive Multi-Channel Graph Convolution Model (MAMP-GF) is introduced, leveraging GraphSAGE-based representation learning to capture inter-channel relationships. Multi-level feature extraction techniques, including Channel Features (CF), Statistical Features (SF), and Graph Features (GF), are employed to maximize the discriminative power of EEG-tNIRS signals. To enhance modality fusion, we propose and evaluate three fusion strategies: MA-GF, MP-GF, and MA-MP-GF, which integrate graph convolutional networks with a modality attention mechanism. The model is trained and validated using EEG and tNIRS data collected from 30 subjects exposed to emotionally stimulating video clips. Experimental results demonstrate that the proposed MA-MP-GF fusion model achieves 98.77% accuracy in subject-dependent experiments, significantly outperforming traditional single-modal and other multimodal fusion methods. In cross-subject validation, the model attains a 55.53% accuracy, highlighting its robustness despite inter-subject variability. The findings illustrate that the proposed graph convolution fusion approach, combined with modality attention, effectively enhances emotion recognition accuracy and stability. This research underscores the potential of EEG-tNIRS fusion in real-time, non-invasive emotion monitoring, paving the way for advanced applications in personalized healthcare and affective computing.

本研究提出了一种结合脑电图（EEG）和功能近红外光谱（tNIRS）数据的新方法，以提高情绪分类的准确性。引入了一种模态关注的多通道图卷积模型（MAMP-GF），利用基于graphsage的表示学习来捕获通道间关系。采用通道特征（CF）、统计特征（SF）和图特征（GF）等多层次特征提取技术，最大限度地提高了EEG-tNIRS信号的判别能力。为了增强模态融合，我们提出并评估了三种融合策略：MA-GF、MP-GF和MA-MP-GF，它们将图卷积网络与模态注意机制相结合。该模型是通过从30名观看情绪刺激视频片段的受试者中收集的EEG和tnir数据进行训练和验证的。实验结果表明，MA-MP-GF融合模型在主体相关实验中准确率达到98.77%，显著优于传统的单模态和其他多模态融合方法。在跨主题验证中，该模型达到55.53%的准确率，突出了其鲁棒性，尽管存在不同主题的差异。研究结果表明，本文提出的图卷积融合方法与模态关注相结合，有效地提高了情感识别的准确性和稳定性。这项研究强调了EEG-tNIRS融合在实时、非侵入性情绪监测中的潜力，为个性化医疗保健和情感计算的高级应用铺平了道路。

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引用次数: 0

A systematic review and meta-analysis on the diagnostic accuracy of artificial intelligence and computer-aided diagnosis of lumbar prolapsed intervertebral disc 人工智能和计算机辅助诊断腰椎间盘突出症诊断准确性的系统回顾和荟萃分析

Neuroscience informatics

Pub Date : 2025-09-01 Epub Date: 2025-07-22 DOI: 10.1016/j.neuri.2025.100221

Sandeep Pattnaik , Manu Goyal , Rajneesh Kumar Gujral , Amit Mittal

Introduction

Lumbar prolapsed intervertebral disc (PIVD) is a debilitating lower back condition, whose accurate and timely diagnosis is crucial for its effective management. Artificial intelligence (AI) and computer-aided diagnosis (CAD) techniques have the potential to revolutionise diagnosis by improving accuracy, efficiency, and objectivity. This systematic review with meta-analysis thus aims to thoroughly assess the available knowledge on the usability of different AI and CAD-based tools in lumbar PIVD diagnosis.

Methods

A systematic search of electronic databases, between June and August 2024 for relevant full-text studies. The primary outcomes for review included the diagnostic accuracy (of each AI and CAD system. Subsequently, a meta-analysis was conducted to synthesise the results of the included studies.

Result

A total of eight studies were identified, evaluating thirteen CAD or AI systems. The meta-analysis involved three of the studies, and it demonstrated a high pooled sensitivity (0.901, 95% CI: 0.871–0.924) and specificity (0.919, 95% CI: 0.898–0.936) for lumbar PIVD diagnosis.

Conclusion

To conclude, these findings strongly support the potential of AI/CAD systems to improve the accuracy and efficiency of lumbar PIVD diagnosis.

Prospero ID

CRD42023444785

腰椎间盘突出症（PIVD）是一种使腰背部衰弱的疾病，准确及时的诊断对其有效治疗至关重要。人工智能（AI）和计算机辅助诊断（CAD）技术有可能通过提高准确性、效率和客观性来彻底改变诊断。因此，本系统综述和荟萃分析旨在全面评估不同人工智能和基于cad的工具在腰椎PIVD诊断中的可用性。方法系统检索电子数据库，于2024年6 - 8月间进行相关全文研究。评估的主要结果包括每个AI和CAD系统的诊断准确性。随后，进行荟萃分析以综合纳入研究的结果。结果共确定了8项研究，评估了13个CAD或AI系统。荟萃分析涉及三项研究，结果显示腰椎PIVD诊断具有较高的综合敏感性（0.901,95% CI: 0.871-0.924）和特异性（0.919,95% CI: 0.898-0.936）。综上所述，这些发现有力地支持了AI/CAD系统在提高腰椎PIVD诊断的准确性和效率方面的潜力。普洛斯彼罗IDCRD42023444785

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引用次数: 0

Beyond the numbers: App-enabled stroke prediction system for high-risk individuals in imbalanced datasets 数字之外：应用程序支持的中风预测系统，用于不平衡数据集中的高风险人群

Neuroscience informatics

Pub Date : 2025-09-01 Epub Date: 2025-06-18 DOI: 10.1016/j.neuri.2025.100215

Abrar Faiaz Eram , Aliva Sadnim Mahmud , Marwan Mostafa Khadem , Md Amimul Ihsan

Background:

Brain stroke, characterized by interrupted blood flow to the brain, poses significant mortality risks and quality-of-life impacts. While machine learning approaches show promise in stroke prediction, current research often relies on synthetic data to address dataset imbalance, potentially compromising real-world model performance in clinical settings.

Method:

This research proposes an alternative approach focusing on recall as the primary evaluation metric for stroke prediction, specifically targeting the reduction of false negatives. In the context of stroke diagnosis, where missed detection can lead to severe consequences or fatality, recall is crucial for directly measuring the model's ability to identify actual stroke cases.

Results:

Three superior models were identified: Logistic Regression, an Ensemble using Soft Voting (combining Gaussian Naive Bayes and Logistic Regression), and customized Support Vector Machine. Exceptional stroke prediction was achieved with recall values of 92%, 92%, and 94%, respectively. Interpretability is enhanced through SHAP applied to the best one. While previous methods showed recall values between 5.6% and 40%, this approach outperformed these benchmarks (94%). Current research emphasizes accuracy metrics, relying on oversampling, being inappropriate for sensitive medical datasets. The pitfall is a slight increase in false positives, which is tolerable because the cost of misdiagnosing a stroke patient far outweighs the reverse scenario.

Conclusions:

The research demonstrates the effectiveness of focusing on recall as an evaluation metric for stroke prediction, minimizing false negative predictions. To facilitate practical implementation, a mobile application incorporating the best-performing model was included. A primary screening which can supplement doctors in stroke diagnosis and prediction was proposed.

背景：脑中风以脑部血流中断为特征，具有显著的死亡风险和生活质量影响。虽然机器学习方法在中风预测方面显示出前景，但目前的研究往往依赖于合成数据来解决数据集失衡问题，这可能会影响临床环境中真实世界模型的性能。方法：本研究提出了一种替代方法，将召回率作为中风预测的主要评估指标，特别是针对减少假阴性。在中风诊断的背景下，遗漏的检测可能导致严重的后果或死亡，召回对于直接测量模型识别实际中风病例的能力至关重要。结果：确定了三种优越的模型：逻辑回归、软投票集成（结合高斯朴素贝叶斯和逻辑回归）和定制支持向量机。异常脑卒中预测的召回率分别为92%、92%和94%。可解释性通过将SHAP应用于最好的代码而得到增强。虽然以前的方法显示召回值在5.6%到40%之间，但这种方法优于这些基准（94%）。目前的研究强调准确性指标，依赖于过采样，不适合敏感的医疗数据集。陷阱是假阳性的轻微增加，这是可以容忍的，因为误诊中风患者的成本远远超过相反的情况。结论：本研究证明了将回忆作为卒中预测的评估指标的有效性，最大限度地减少了错误的负面预测。为了便于实际实施，我们还提供了一个包含最佳性能模型的移动应用程序。提出了一种辅助医生进行脑卒中诊断和预测的初步筛查方法。

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引用次数: 0

AI-enhanced diagnosis of very late-onset schizophrenia-like psychosis: A step toward preventing dementia in older adults 人工智能对极晚发性精神分裂症样精神病的增强诊断：预防老年人痴呆的一步

Neuroscience informatics

Pub Date : 2025-09-01 Epub Date: 2025-07-28 DOI: 10.1016/j.neuri.2025.100223

Ali Allahgholi , Ava Mazhari

The rapid aging of the global population, projected to reach 2.1 billion individuals aged 60 and older by 2050, is associated with an increased prevalence of mental health conditions, particularly dementia and psychosis. Among these, very late-onset schizophrenia-like psychosis (VLOSLP), defined as occurring after age 60, poses significant diagnostic challenges due to overlapping neurobiological changes and medical conditions common in older adults. Studies have indicated a higher risk of dementia in patients with VLOSLP, emphasizing the necessity for ongoing symptom monitoring. In recent years, artificial intelligence (AI), particularly deep learning (DL) and machine learning (ML), has shown promise in enhancing disease diagnosis through advanced medical imaging techniques. This study aims to classify VLOSLP using MRI images from patients aged 60 and older, obtained from the COBRE and MCICshare databases via the SchizoConnect platform. To address the challenge of limited data, synthetic images were generated using Generative Adversarial Networks (GAN) following preprocessing techniques. These images were then classified using a Support Vector Machine (SVM) classifier, with feature extraction performed through Zernike moments. The findings achieved an area under the curve (AUC) of 0.98, contributing to more accurate diagnoses of VLOSLP and facilitating better management and monitoring of this complex condition in the aging population.

全球人口迅速老龄化，预计到2050年60岁及以上人口将达到21亿，这与精神健康状况，特别是痴呆症和精神病的患病率上升有关。其中，非常晚发性精神分裂症样精神病（VLOSLP），定义为发生在60岁以后，由于重叠的神经生物学变化和老年人常见的医疗条件，给诊断带来了重大挑战。研究表明，VLOSLP患者痴呆的风险更高，强调了持续监测症状的必要性。近年来，人工智能（AI），特别是深度学习（DL）和机器学习（ML），在通过先进的医学成像技术增强疾病诊断方面显示出了希望。本研究旨在通过SchizoConnect平台从COBRE和MCICshare数据库获得60岁及以上患者的MRI图像，对VLOSLP进行分类。为了解决有限数据的挑战，在预处理技术之后使用生成对抗网络（GAN）生成合成图像。然后使用支持向量机（SVM）分类器对这些图像进行分类，并通过泽尼克矩进行特征提取。研究结果达到了0.98的曲线下面积（AUC），有助于更准确地诊断VLOSLP，并有助于更好地管理和监测老年人群中这一复杂疾病。

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引用次数: 0

An automated measurement of head circumference using CT scans: An application in children with head abnormalities 使用CT扫描自动测量头围：在头部异常儿童中的应用

Neuroscience informatics

Pub Date : 2025-09-01 Epub Date: 2025-06-27 DOI: 10.1016/j.neuri.2025.100217

Priscila Satomi Acamine , Rafael Maffei Loureiro , Lucas dos Anjos Longas , Fabio Augusto Ribeiro Dalpra , Luigi Villanova Machado de Barros Lago , Larissa Vasconcellos de Moraes , Paulo Cesar Filho Estevam , Luiz Otávio Vittorelli , Lucas Silva Kallás , Ana Paula Antunes Pascalicchio Bertozzi , Maria Isabel Barros Guinle , Gilberto Szarf , Saulo Duarte Passos , Birajara Soares Machado , Joselisa Péres Queiroz De Paiva

Manual measurement of head circumference has been a widely adopted method of neurodevelopmental evaluation in both clinical and research settings. Here, we propose a method that uses axial slices of computerized tomography (CT) scans to detect the largest outer margin for measurement. Our method can both complement conventional tape measurements or be applied as a standalone tool, especially in the context of retrospective big data analysis. We applied our algorithm in a set of 74 head CT scans obtained from individual children (8,5 ± 14,1 months old). The method proved to be concordant

(ICC [2, k] = 0.99)

, consistent (

ICC [3, k]

= 1), and showed a correlation of 0.988 compared to obtaining manual head circumferences by specialists. Our method is a reliable alternative to conventional manual measurements of head circumference. It can be readily applied in macrocephaly and microcephaly screening studies and in growth reference charts for syndromes related to head alterations.

人工测量头围已成为临床和研究中广泛采用的神经发育评估方法。在这里，我们提出了一种使用计算机断层扫描（CT）轴向切片来检测测量的最大外缘的方法。我们的方法既可以补充传统的卷尺测量，也可以作为一个独立的工具应用，特别是在回顾性大数据分析的背景下。我们将我们的算法应用于74份来自个体儿童（8.5±14.1个月）的头部CT扫描。该方法被证明是一致的（ICC[2,k]=0.99），一致的（ICC[3,k] = 1），与专家手工获得的头部周长相比，显示出0.988的相关性。我们的方法是一个可靠的替代传统的手工测量头围。它可以很容易地应用于大头畸形和小头畸形的筛查研究以及与头部改变相关的综合征的生长参考图表。

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