Computers in biology and medicine最新文献

{"title":"A novel classical machine learning framework for early sepsis prediction using electronic health record data from ICU patients","authors":"Johayra Prithula ,&nbsp;Khandaker Reajul Islam ,&nbsp;Jaya Kumar ,&nbsp;Toh Leong Tan ,&nbsp;Mamun Bin Ibne Reaz ,&nbsp;Tawsifur Rahman ,&nbsp;Susu M. Zughaier ,&nbsp;Muhammad Salman Khan ,&nbsp;M. Murugappan ,&nbsp;Muhammad E.H. Chowdhury","doi":"10.1016/j.compbiomed.2024.109284","DOIUrl":"10.1016/j.compbiomed.2024.109284","url":null,"abstract":"<div><div>Sepsis, a life-threatening condition triggered by the body's response to infection, remains a significant global health challenge, annually affecting millions in the United States alone with substantial mortality and healthcare costs. Early prediction of sepsis is critical for timely intervention and improved patient outcomes. This study introduces an innovative predictive model leveraging machine learning techniques and a specific data-splitting approach on highly imbalanced electronic health records (EHRs). Using PhysioNet/CinC Challenge 2019 data from 40,336 patients, including vital signs, lab values, and demographics. Preliminary assessments using classical and stacked ML models with Synthetic Minority Oversampling Technique (SMOTE) augmentation were conducted, showing improved performance. It is found that stacking ML models enhances overall accuracy but faces limitations in precision, recall, and F1 score for positive class prediction. A novel data-splitting approach with 5-fold cross-validation and SMOTE and COPULA augmentation techniques demonstrated promise, with F1 scores ranging from 93 % to 94 % using the COPULA technique. COPULA excelled at predictions for different hours' onsets compared to the SMOTE technique. The proposed model outperformed existing studies, suggesting clinical viability for early sepsis prediction.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109284"},"PeriodicalIF":7.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142695409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced statistical inference of myocardial stiffness: A time series Gaussian process approach of emulating cardiac mechanics for real-time clinical decision support","authors":"Yuzhang Ge ,&nbsp;Dirk Husmeier ,&nbsp;Arash Rabbani ,&nbsp;Hao Gao","doi":"10.1016/j.compbiomed.2024.109381","DOIUrl":"10.1016/j.compbiomed.2024.109381","url":null,"abstract":"<div><div>Cardiac mechanics modelling promises to revolutionize personalized health care; however, inferring patient-specific biophysical parameters, which are critical for understanding myocardial functions and performance, poses substantial methodological challenges. Our work is primarily motivated to determine the passive stiffness of the myocardium from the measurement of the left ventricle (LV) volume at various time points, which is crucial for diagnosing cardiac physiological conditions. Although there have been significant advancements in cardiac mechanics modelling, the tasks of inference and uncertainty quantification of myocardial stiffness remain challenging, with high computational costs preventing real-time decision support. We adapt Gaussian processes to construct a statistical surrogate model for emulating LV cavity volume during diastolic filling to overcome this challenge. As the LV volumes, obtained at different time points in diastole, constitute a time series, we apply the Kronecker product trick to decompose the complex covariance matrix of the whole system into two separate covariance matrices, one for time and the other for biophysical parameters. To proceed towards personalized health care, we further integrate patient-specific LV geometries into the Gaussian process emulator using principal component analysis (PCA). Utilizing a deep learning neural network for extracting time-series left ventricle volumes from magnetic resonance images, Bayesian inference is applied to determine the posterior probability distribution of critical cardiac mechanics parameters. Tests on real-patient data illustrate the potential for real-time estimation of myocardial properties for clinical decision-making. These advancements constitute a crucial step towards clinical impact, offering valuable insights into posterior uncertainty quantification for complex cardiac mechanics models.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109381"},"PeriodicalIF":7.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142695412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-scale multimodal deep learning framework for Alzheimer's disease diagnosis","authors":"Mohammed Abdelaziz ,&nbsp;Tianfu Wang ,&nbsp;Waqas Anwaar ,&nbsp;Ahmed Elazab","doi":"10.1016/j.compbiomed.2024.109438","DOIUrl":"10.1016/j.compbiomed.2024.109438","url":null,"abstract":"<div><div>Multimodal neuroimaging data, including magnetic resonance imaging (MRI) and positron emission tomography (PET), provides complementary information about the brain that can aid in Alzheimer's disease (AD) diagnosis. However, most existing deep learning methods still rely on patch-based extraction from neuroimaging data, which typically yields suboptimal performance due to its isolation from the subsequent network and does not effectively capture the varying scales of structural changes in the cerebrum. Moreover, these methods often simply concatenate multimodal data, ignoring the interactions between them that can highlight discriminative regions and thereby improve the diagnosis of AD. To tackle these issues, we develop a multimodal and multi-scale deep learning model that effectively leverages the interaction between the multimodal and multiscale of the neuroimaging data. First, we employ a convolutional neural network to embed each scale of the multimodal images. Second, we propose multimodal scale fusion mechanisms that utilize both multi-head self-attention and multi-head cross-attention, which capture global relations among the embedded features and weigh each modality's contribution to another, and hence enhancing feature extraction and interaction between each scale of MRI and PET images. Third, we introduce a cross-modality fusion module that includes a multi-head cross-attention to fuse MRI and PET data at different scales and promote global features from the previous attention layers. Finally, all the features from every scale are fused to discriminate between the different stages of AD. We evaluated our proposed method on the ADNI dataset, and the results show that our model achieves better performance than the state-of-the-art methods.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109438"},"PeriodicalIF":7.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142695427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep multiple instance learning on heterogeneous graph for drug–disease association prediction","authors":"Yaowen Gu ,&nbsp;Si Zheng ,&nbsp;Bowen Zhang ,&nbsp;Hongyu Kang ,&nbsp;Rui Jiang ,&nbsp;Jiao Li","doi":"10.1016/j.compbiomed.2024.109403","DOIUrl":"10.1016/j.compbiomed.2024.109403","url":null,"abstract":"<div><div>Drug repositioning offers promising prospects for accelerating drug discovery by identifying potential drug–disease associations (DDAs) for existing drugs and diseases. Previous methods have generated meta-path-augmented node or graph embeddings for DDA prediction in drug–disease heterogeneous networks. However, these approaches rarely develop end-to-end frameworks for path instance-level representation learning as well as the further feature selection and aggregation. By leveraging the abundant topological information in path instances, more fine-grained and interpretable predictions can be achieved. To this end, we introduce deep multiple instance learning into drug repositioning by proposing a novel method called MilGNet. MilGNet employs a heterogeneous graph neural network (HGNN)-based encoder to learn drug and disease node embeddings. Treating each drug–disease pair as a bag, we designed a special quadruplet meta-path form and implemented a pseudo meta-path generator in MilGNet to obtain multiple meta-path instances based on network topology. Additionally, a bidirectional instance encoder enhances the representation of meta-path instances. Finally, MilGNet utilizes a multi-scale interpretable predictor to aggregate bag embeddings with an attention mechanism, providing predictions at both the bag and instance levels for accurate and explainable predictions. Comprehensive experiments on five benchmarks demonstrate that MilGNet significantly outperforms ten advanced methods. Notably, three case studies on one drug (Methotrexate) and two diseases (Renal Failure and Mismatch Repair Cancer Syndrome) highlight MilGNet’s potential for discovering new indications, therapies, and generating rational meta-path instances to investigate possible treatment mechanisms. The source code is available at <span><span>https://github.com/gu-yaowen/MilGNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109403"},"PeriodicalIF":7.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic discrimination between neuroendocrine carcinomas and grade 3 neuroendocrine tumors by deep learning of H&E images","authors":"Alex Arrieta Legorburu ,&nbsp;Julen Bohoyo Bengoetxea ,&nbsp;Carlos Gracia ,&nbsp;Joan C. Ferreres ,&nbsp;Maria Rosa Bella-Cueto ,&nbsp;Marcos J. Araúzo-Bravo","doi":"10.1016/j.compbiomed.2024.109443","DOIUrl":"10.1016/j.compbiomed.2024.109443","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Neuroendocrine neoplasms (NENs) arise from diffuse neuroendocrine cells and are categorized as either well-differentiated and less proliferative Neuroendocrine Tumors (NETs), divided into low (G1), middle (G2), and high grades (G3), or poorly differentiated, and more proliferative Neuroendocrine Carcinomas (NECs). Low-grade NENs typically necessitate surgical intervention, whereas high-grade ones often require chemotherapy. However, low-grade NENs may exhibit aggressive behavior. Therefore, it is crucial to precisely refine the diagnosis of NENs. This refinement is achievable when differentiation/non-differentiation is evident or when the Ki-67 or mitosis index is low. The challenge arises in cases of morphologically undifferentiated instances with a high Ki-67 percentage and/or high mitotic index. To address this challenge, we developed a Deep Learning (DL) system named NEToC, designed to differentiate between NETs and NECs using exclusively morphological information from immunohistochemistry images, without relying on Ki-67 or mitosis assessments. NEToC was developed using 95 NEN cases from the period 2015 to 2018 at Parc Tauli Hospital in Spain, comprising 588 images. Implemented as a Graphical User Interface (GUI) system, NEToC is intended for deployment in pathological departments of hospitals to perform federated supervision. We tested the performance of NEToC with 119 images that were not used during the Artificial Neural Network (ANN) training phase, and evaluated its robustness across various resolutions: 64 × 64, 128 × 128, 256 × 256, and 512 × 512 pixels. The achieved accuracies for these resolutions were 74 %, 98 %, 98 %, and 100 %, respectively, for an underrepresented NET G3 experiment, and 66 %, 89 %, 95 % and 94 % for a represented NET G3 experiment. Based on several measured performance metrics, the optimal resolution appears to be between 128 × 128 and 256 × 256 pixels, considering computational resources and accuracy requirements. However, we found that the 256 × 256-pixel resolution is more robust to classify underrepresented classes in the learning phase. These results imply that the information to discriminate between NECs and Grade 3 NETs needs to be resolved in regions with a pixel resolution of no more than 4 μm/pixel. Most of the misclassifications were false negatives, where NET G1-type images were erroneously classified as NEC-type. Our results demonstrate that a DL-based diagnostic algorithm provides a more accurate diagnosis in NEN cases where physicians face challenges. NEToC has been initially trained with and used to classify gastrointestinal NENs. Since the NEN morphology does not change among the different organs, the use of NEToC can be extrapolated to NENs from different organs. NEToC facilitates federated supervision, allowing pathologists to collect interchangeable files based on NEToC classification predictions. NEToC is an easy-to-use, adaptable software that integrates multiple ANNs to improve s","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109443"},"PeriodicalIF":7.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LATUP-Net: A lightweight 3D attention U-Net with parallel convolutions for brain tumor segmentation","authors":"Ebtihal J. Alwadee ,&nbsp;Xianfang Sun ,&nbsp;Yipeng Qin ,&nbsp;Frank C. Langbein","doi":"10.1016/j.compbiomed.2024.109353","DOIUrl":"10.1016/j.compbiomed.2024.109353","url":null,"abstract":"<div><div>Early-stage 3D brain tumor segmentation from magnetic resonance imaging (MRI) scans is crucial for prompt and effective treatment. However, this process faces the challenge of precise delineation due to the tumors’ complex heterogeneity. Moreover, energy sustainability targets and resource limitations, especially in developing countries, require efficient and accessible medical imaging solutions. The proposed architecture, a Lightweight 3D ATtention U-Net with Parallel convolutions, LATUP-Net, addresses these issues. It is specifically designed to reduce computational requirements significantly while maintaining high segmentation performance. By incorporating parallel convolutions, it enhances feature representation by capturing multi-scale information. It further integrates an attention mechanism to refine segmentation through selective feature recalibration. LATUP-Net achieves promising segmentation performance: the average Dice scores for the whole tumor, tumor core, and enhancing tumor on the BraTS 2020 dataset are 88.41%, 83.82%, and 73.67%, and on the BraTS 2021 dataset, they are 90.29%, 89.54%, and 83.92%, respectively. Hausdorff distance metrics further indicate its improved ability to delineate tumor boundaries. With its significantly reduced computational demand using only 3.07 M parameters, about 59 times fewer than other state-of-the-art models, and running on a single NVIDIA GeForce RTX3060 12 GB GPU, LATUP-Net requires just 15.79 GFLOPs. This makes it a promising solution for real-world clinical applications, particularly in settings with limited resources. Investigations into the model’s interpretability, utilizing gradient-weighted class activation mapping and confusion matrices, reveal that while attention mechanisms enhance the segmentation of small regions, their impact is nuanced. Achieving the most accurate tumor delineation requires carefully balancing local and global features. The code is available at <span><span>https://qyber.black/ca/code-bca</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109353"},"PeriodicalIF":7.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel approach for brain connectivity using recurrent neural networks and integrated gradients","authors":"June Sic Kim","doi":"10.1016/j.compbiomed.2024.109404","DOIUrl":"10.1016/j.compbiomed.2024.109404","url":null,"abstract":"<div><div>Brain connectivity is an important tool for understanding the cognitive and perceptive neural mechanisms in the neuroimaging field. Many methods for estimating effective connectivity have relied on the linear regressive model. However, the linear regression approach might fail to account for the complexity inherent in brain connectivity. Due to the recent success of deep neural networks (DNNs), regressive data are able to be predicted with high accuracy. This study aimed to develop a connectivity method using the prediction performance of a DNN model and the parameters of the model. To this end, a method is proposed that utilizes integrated gradients in a recurrent neural network model. It is an extended application of explainable artificial intelligence in the multivariate autoregressive DNN model. It would be advantageous compared to the methods using the parameters of the linear regressive model or Granger's approach referring to the difference in error between the models. The performance of the connectivity estimation was tested by simulated datasets with various conditions. The overall performance was good on multiple metrics including recall (0.94), precision (0.90), F1-score (0.92), and accuracy (0.97). Compared with other conventional methods, the proposed method is robust and precise. The proposed method also demonstrates that it can be applied to estimate the actual brain connectivity in a magnetoencephalography study. In conclusion, the connectivity method based on integrated gradients provides an accurate estimation of brain connectivity by effectively capturing complex interactions, which is validated through high performance metrics such as recall, precision, F1-score, and accuracy across multiple simulated datasets. It introduces a novel framework to combine DNN and integrated gradients and to estimate effective connectivity by the explainable AI.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109404"},"PeriodicalIF":7.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence-driven intelligent learning models for identification and prediction of cardioneurological disorders: A comprehensive study","authors":"Shahadat Hussain,&nbsp;Shahnawaz Ahmad,&nbsp;Mohammed Wasid","doi":"10.1016/j.compbiomed.2024.109342","DOIUrl":"10.1016/j.compbiomed.2024.109342","url":null,"abstract":"<div><div>The integration of Artificial Intelligence (AI) and Intelligent Learning Models (ILMs) in healthcare has transformed the field, offering precise diagnostics, remote monitoring, personalized treatment, and more. Cardioneurological disorders (CD), affecting the cardiovascular and neurological systems, present significant diagnostic and management challenges. Traditional testing methods often lack sensitivity and specificity, leading to delayed or inaccurate diagnoses. AI-driven ILMs trained on large datasets offer promise for accurate identification and prediction of CD by analyzing complex data patterns. However, there is a lack of comprehensive studies reviewing AI applications for the diagnosis of CD and inter related disorders. This paper comprehensively reviews existing integrated solutions involving AI and ILMs in CD, examining their clinical manifestations, epidemiology, diagnostic challenges, and therapeutic considerations. The study examines recent research on CD, reviews AI-driven models’ landscape, evaluates existing models, addresses practical considerations, and outlines future research directions. Through this work, we aim to provide insights into the transformative potential of AI-driven ILMs in improving clinical practice and patient outcomes for CD.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109342"},"PeriodicalIF":7.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142686195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of a multi-epitope vaccine candidate against carrion disease by immunoinformatics approach","authors":"Damaris Rivera-Asencios ,&nbsp;Abraham Espinoza-Culupú ,&nbsp;Sheyla Carmen-Sifuentes ,&nbsp;Pablo Ramirez ,&nbsp;Ruth García-de-la-Guarda","doi":"10.1016/j.compbiomed.2024.109397","DOIUrl":"10.1016/j.compbiomed.2024.109397","url":null,"abstract":"<div><div>Carrion's disease, caused by the bacterium <em>Bartonella bacilliformis</em>, is a serious public health problem in Peru, Ecuador and Colombia. Currently there is no available vaccine against <em>B. bacilliformis</em>. While antibiotics are the standard treatment, resistant strains have been reported, and there is a potential spread of the vector that transmits the bacteria. This study aimed to design a multi-epitope vaccine candidate against the causative agent of Carrion's disease using immunoinformatics tools. Predictions of B-cell epitopes, as well as CD4⁺ and CD8⁺T cell epitopes, were performed from the entire proteome of <em>B. bacilliformis</em> KC583 using the most frequent alleles from Peru, Ecuador, Colombia, and worldwide. B-cell epitopes and T-cell nested epitopes from outer membrane and virulence-associated proteins were selected. Epitopes were filtered out based on promiscuity, non-allergenicity, conservation, non-homology and non-toxicity. Two vaccine constructs were assembled using linkers. The tertiary structure of the constructs was predicted, and their stability was evaluated through molecular dynamics simulations. The most stable construct was selected for molecular docking with the TLR4 receptor. This study proposes a vaccine construct evaluated in silico as a potential vaccine candidate against <em>Bartonella bacilliformis</em>.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109397"},"PeriodicalIF":7.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142681122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Kinematic classification of mandibular movements in patients with temporomandibular disorders based on PCA","authors":"Ryuji Shigemitsu ,&nbsp;Toru Ogawa ,&nbsp;Emika Sato ,&nbsp;Anderson Souza Oliveira ,&nbsp;John Rasmussen","doi":"10.1016/j.compbiomed.2024.109441","DOIUrl":"10.1016/j.compbiomed.2024.109441","url":null,"abstract":"<div><div>This retrospective study aimed to kinematically classify mandibular movements collected during Temporomandibular Disorders (TMD) treatment, employing Fourier transformation (FT), Principal Component Analysis (PCA), and K-means clustering (k-means), and to investigate their correlation with symptoms of pain-related TMD. The study included five TMD participants diagnosed with myalgia (age: 39–86 years, with an SD of 18.96) and three healthy participants (age: 32–42 years, with an SD of 5.13) with no stomatognathic problems. TMD participants underwent tailored treatment for their symptoms, and their maximum unassisted mouth opening (MMO) was recorded randomly with a motion capture system (ARCUS digma II, Kavo, Biberach, Germany) at multiple time points. MMO for healthy participants served as a control. The dataset comprising 28 trials, was transferred to the AnyBody Modeling System (AnyBody Technology, Aalborg, Denmark) to extract joint angle time series, which were then transformed into Fourier series. Subsequently, PCA and k-means clustering were conducted. Two clusters were identified: Cluster 1, predominantly composed of symptomatic trials, and Cluster 2, mainly consisting of asymptomatic trials. Distinct transition pathways between the clusters were observed among participants, corresponding to the alleviation of pain-related symptoms during TMD treatment. These findings suggest that this approach has potential as an effective tool for diagnosing and assessing TMD by identifying symptomatic kinematic patterns and tracking temporal changes in mandibular movement. Despite the small dataset, these results suggest promise for a novel functional assessment method for TMD based on kinematic features.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109441"},"PeriodicalIF":7.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142686197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}