Neuroscience informatics最新文献

{"title":"Revealing spatiotemporal neural activation patterns in electrocorticography recordings of human speech production by mutual information","authors":"Julio Kovacs ,&nbsp;Dean Krusienski ,&nbsp;Minu Maninder ,&nbsp;Willy Wriggers","doi":"10.1016/j.neuri.2025.100232","DOIUrl":"10.1016/j.neuri.2025.100232","url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>New Method</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Comparison with existing methods</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100232"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932672","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}

{"title":"A comparative study of hybrid decision tree–deep learning models in the detection of intracranial arachnoid cysts","authors":"Aziz Ilyas Ozturk ,&nbsp;Osman Yıldırım ,&nbsp;Ebru İdman ,&nbsp;Emrah İdman","doi":"10.1016/j.neuri.2025.100234","DOIUrl":"10.1016/j.neuri.2025.100234","url":null,"abstract":"<div><div>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 (&lt;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.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100234"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026696","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}

{"title":"Feature analysis of depression patients' house-tree-person drawings using convolutional neural networks","authors":"Liu Zhenyi ,&nbsp;Ye Cun Chun","doi":"10.1016/j.neuri.2025.100239","DOIUrl":"10.1016/j.neuri.2025.100239","url":null,"abstract":"<div><h3>Objective</h3><div>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.</div></div><div><h3>Methods</h3><div>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).</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100239"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416057","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}

{"title":"Decoding memory with explainable AI: A large-scale EEG-based machine learning study of encoding vs. retrieval","authors":"Mohammed Tawshif Hossain ,&nbsp;Adnan Sami Sarker ,&nbsp;Arnab Chowdhury ,&nbsp;Rajesh Mitra ,&nbsp;Raiyan Rahman ,&nbsp;M.R.C. Mahdy","doi":"10.1016/j.neuri.2025.100227","DOIUrl":"10.1016/j.neuri.2025.100227","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100227"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860746","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}

{"title":"Exploring community pharmacist's psychological intentions to adopt generative artificial intelligence (GenAI) chatbots for patient information, education, and counseling","authors":"Hafidz Ihsan Hidayatullah ,&nbsp;Muhammad Taufiq Saifullah ,&nbsp;Muhammad Thesa Ghozali ,&nbsp;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}

{"title":"A systematic review and meta-analysis on the diagnostic accuracy of artificial intelligence and computer-aided diagnosis of lumbar prolapsed intervertebral disc","authors":"Sandeep Pattnaik ,&nbsp;Manu Goyal ,&nbsp;Rajneesh Kumar Gujral ,&nbsp;Amit Mittal","doi":"10.1016/j.neuri.2025.100221","DOIUrl":"10.1016/j.neuri.2025.100221","url":null,"abstract":"<div><h3>Introduction</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Result</h3><div>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.</div></div><div><h3>Conclusion</h3><div>To conclude, these findings strongly support the potential of AI/CAD systems to improve the accuracy and efficiency of lumbar PIVD diagnosis.</div></div><div><h3>Prospero ID</h3><div>CRD42023444785</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 3","pages":"Article 100221"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722968","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}

{"title":"Deep learning model for patient emotion recognition using EEG-tNIRS data","authors":"Mohan Raparthi ,&nbsp;Nischay Reddy Mitta ,&nbsp;Vinay Kumar Dunka ,&nbsp;Sowmya Gudekota ,&nbsp;Sandeep Pushyamitra Pattyam ,&nbsp;Venkata Siva Prakash Nimmagadda","doi":"10.1016/j.neuri.2025.100219","DOIUrl":"10.1016/j.neuri.2025.100219","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 3","pages":"Article 100219"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696612","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}

{"title":"Beyond the numbers: App-enabled stroke prediction system for high-risk individuals in imbalanced datasets","authors":"Abrar Faiaz Eram ,&nbsp;Aliva Sadnim Mahmud ,&nbsp;Marwan Mostafa Khadem ,&nbsp;Md Amimul Ihsan","doi":"10.1016/j.neuri.2025.100215","DOIUrl":"10.1016/j.neuri.2025.100215","url":null,"abstract":"<div><h3>Background:</h3><div>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.</div></div><div><h3>Method:</h3><div>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.</div></div><div><h3>Results:</h3><div>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.</div></div><div><h3>Conclusions:</h3><div>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.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 3","pages":"Article 100215"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338309","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}

{"title":"AI-enhanced diagnosis of very late-onset schizophrenia-like psychosis: A step toward preventing dementia in older adults","authors":"Ali Allahgholi ,&nbsp;Ava Mazhari","doi":"10.1016/j.neuri.2025.100223","DOIUrl":"10.1016/j.neuri.2025.100223","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 3","pages":"Article 100223"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722969","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}

{"title":"An automated measurement of head circumference using CT scans: An application in children with head abnormalities","authors":"Priscila Satomi Acamine ,&nbsp;Rafael Maffei Loureiro ,&nbsp;Lucas dos Anjos Longas ,&nbsp;Fabio Augusto Ribeiro Dalpra ,&nbsp;Luigi Villanova Machado de Barros Lago ,&nbsp;Larissa Vasconcellos de Moraes ,&nbsp;Paulo Cesar Filho Estevam ,&nbsp;Luiz Otávio Vittorelli ,&nbsp;Lucas Silva Kallás ,&nbsp;Ana Paula Antunes Pascalicchio Bertozzi ,&nbsp;Maria Isabel Barros Guinle ,&nbsp;Gilberto Szarf ,&nbsp;Saulo Duarte Passos ,&nbsp;Birajara Soares Machado ,&nbsp;Joselisa Péres Queiroz De Paiva","doi":"10.1016/j.neuri.2025.100217","DOIUrl":"10.1016/j.neuri.2025.100217","url":null,"abstract":"<div><div>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 <span><math><mo>(</mo><mrow><mi>ICC</mi></mrow><mo>[</mo><mn>2</mn><mo>,</mo><mi>k</mi><mo>]</mo><mo>=</mo><mn>0.99</mn><mo>)</mo></math></span>, consistent (<span><math><mrow><mi>ICC</mi></mrow><mo>[</mo><mn>3</mn><mo>,</mo><mi>k</mi><mo>]</mo></math></span> = 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.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 3","pages":"Article 100217"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535145","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}