BioMedical Engineering OnLine最新文献

{"title":"Enhancing the estimation of PaCO₂ from etCO₂ during ventilation through non-invasive parameters in the ovine model.","authors":"Mike Grüne, Lena Olivier, Valerie Pfannschmidt, Matthias Hütten, Thorsten Orlikowsky, Andre Stollenwerk, Mark Schoberer","doi":"10.1186/s12938-024-01292-2","DOIUrl":"10.1186/s12938-024-01292-2","url":null,"abstract":"<p><strong>Background: </strong>In mechanically ventilated neonates, the arterial partial pressure of <math><msub><mtext>CO</mtext> <mn>2</mn></msub> </math> ( <math><msub><mtext>PaCO</mtext> <mn>2</mn></msub> </math> ) is an important indicator for the adequacy of ventilation settings. Determining the <math><msub><mtext>PaCO</mtext> <mn>2</mn></msub> </math> is commonly done using invasive blood gas analyses, which constitute risks for neonates and are typically only available infrequently. An accurate, reliable, and continuous estimation of <math><msub><mtext>PaCO</mtext> <mn>2</mn></msub> </math> is of high interest for medical staff, giving the possibility of a closer monitoring and faster reactions to changes. We aim to present a non-invasive estimation method for <math><msub><mtext>PaCO</mtext> <mn>2</mn></msub> </math> in neonates on the basis of end-tidal <math><msub><mtext>CO</mtext> <mn>2</mn></msub> </math> ( <math><msub><mtext>etCO</mtext> <mn>2</mn></msub> </math> ) with inclusion of different physiological and ventilation parameters. The estimation method should be more accurate than an estimation by unaltered <math><msub><mtext>etCO</mtext> <mn>2</mn></msub> </math> measurements with regard to the mean absolute error and the standard deviation.</p><p><strong>Methods: </strong>Secondary data from 51 preterm lambs are used, due to its high comparability to preterm human data. We utilize robust linear regression on 863 <math><msub><mtext>PaCO</mtext> <mn>2</mn></msub> </math> measurements below or equal to 75 mmHg from the first day of life. <math><msub><mtext>etCO</mtext> <mn>2</mn></msub> </math> along with a set of ventilation settings and measurements as well as vital parameters are included in the regression. Included independent variables are chosen iteratively by highest Pearson correlation to the remaining estimation deviation.</p><p><strong>Results: </strong>The evaluation is carried out on 12 additional neonatal lambs with 246 <math><msub><mtext>PaCO</mtext> <mn>2</mn></msub> </math> measurements below or equal to 75 mmHg from the first two days of life. The estimation method shows a mean absolute error of 3.80 mmHg with a 4.92 mmHg standard deviation of differences and a standard error of 0.31 mmHg in comparison to measured <math><msub><mtext>PaCO</mtext> <mn>2</mn></msub> </math> by blood gas analysis.</p><p><strong>Conclusions: </strong>The estimation of <math><msub><mtext>PaCO</mtext> <mn>2</mn></msub> </math> by the proposed equation is less biased than unaltered <math><msub><mtext>etCO</mtext> <mn>2</mn></msub> </math> . The usage of this method in clinical practice or in applications like the automation of ventilation needs further investigation.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"104"},"PeriodicalIF":2.9,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11515479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142494159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Four-phase CT lesion recognition based on multi-phase information fusion framework and spatiotemporal prediction module.","authors":"Shaohua Qiao, Mengfan Xue, Yan Zuo, Jiannan Zheng, Haodong Jiang, Xiangai Zeng, Dongliang Peng","doi":"10.1186/s12938-024-01297-x","DOIUrl":"10.1186/s12938-024-01297-x","url":null,"abstract":"<p><p>Multiphase information fusion and spatiotemporal feature modeling play a crucial role in the task of four-phase CT lesion recognition. In this paper, we propose a four-phase CT lesion recognition algorithm based on multiphase information fusion framework and spatiotemporal prediction module. Specifically, the multiphase information fusion framework uses the interactive perception mechanism to realize the channel-spatial information interactive weighting between multiphase features. In the spatiotemporal prediction module, we design a 1D deep residual network to integrate multiphase feature vectors, and use the GRU architecture to model the temporal enhancement information between CT slices. In addition, we employ CT image pseudo-color processing for data augmentation and train the whole network based on a multi-task learning framework. We verify the proposed network on a four-phase CT dataset. The experimental results show that the proposed network can effectively fuse the multi-phase information and model the temporal enhancement information between CT slices, showing excellent performance in lesion recognition.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"103"},"PeriodicalIF":2.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11492744/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A transcriptomic analysis of dental pulp stem cell senescence in vitro.","authors":"Jidong Xu, Mingchang Hu, Longfei Liu, Xuecheng Xu, Linlin Xu, Yu Song","doi":"10.1186/s12938-024-01298-w","DOIUrl":"https://doi.org/10.1186/s12938-024-01298-w","url":null,"abstract":"<p><strong>Background/purpose: </strong>The use of human dental pulp stem cells (hDPSCs) as autologous stem cells for tissue repair and regenerative techniques is a significant area of global research. The objective of this study was to investigate the effects of long-term in vitro culture on the multidifferentiation potential of hDPSCs and the potential molecular mechanisms involved.</p><p><strong>Materials and methods: </strong>The tissue block method was used to extract hDPSCs from orthodontic-minus-extraction patients, which were then expanded and cultured in vitro for 12 generations. Stem cells from passages three, six, nine, and twelve were selected. Flow cytometry was used to detect the expression of stem cell surface markers, and CCK-8 was used to assess cell proliferation. β-Galactosidase staining was employed to detect cellular senescence, Alizarin Red S staining to assess osteogenic potential, and Oil Red O staining to evaluate lipogenic capacity. RNA sequencing (RNA-seq) was conducted to identify differentially expressed genes in DPSCs and investigate their potential mechanisms.</p><p><strong>Results: </strong>With increasing passage numbers, pulp stem cells showed an increase in senescence and a decrease in proliferative capacity and osteogenic-lipogenic multidifferentiation potential. The expression of stem cell surface markers CD34 and CD45 was stable, whereas the expression of CD73, CD90, and CD105 decreased with increasing passages. According to the RNA-seq analysis, the differentially expressed genes CFH, WNT16, HSD17B2, IDI1, and COL5A3 may be associated with stem cell senescence.</p><p><strong>Conclusion: </strong>Increased in vitro expansion induced cellular senescence in pulp stem cells, which resulted in a reduction in their proliferative capacity and osteogenic-lipogenic differentiation potential. The differential expression of genes such as CFH, WNT16, HSD17B2, IDI1, and COL5A3 may represent a potential mechanism for the induction of cellular senescence in pulp stem cells.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"102"},"PeriodicalIF":2.9,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11488381/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential role of metal nanoparticles in treatment of peri-implant mucositis and peri-implantitis.","authors":"Mohammad Hosseini Hooshiar, Asieh Mozaffari, Mareb Hamed Ahmed, Radhwan Abdul Kareem, Athmar Jaber Zrzo, Aseel Salah Mansoor, Zainab H Athab, Zahra Parhizgar, Parisa Amini","doi":"10.1186/s12938-024-01294-0","DOIUrl":"https://doi.org/10.1186/s12938-024-01294-0","url":null,"abstract":"<p><p>Peri-implantitis (PI), a pathological condition associated with plaque, affects the tissues around dental implants. In addition, peri-implant mucositis (PIM) is a precursor to the destructive inflammatory PI and is an inflammation of the soft tissues surrounding the dental implant. It is challenging to eradicate and regulate the PI treatment due to its limited effectiveness. Currently, there is a significant interest in the development and research of additional biocompatible materials to prevent the failure of dental implants. Nanotechnology has the potential to address or develop solutions to the significant challenge of implant failure caused by cytotoxicity and biocompatibility in dentistry. Nanoparticles (NPs) may be used as carriers for the release of medicines, as well as to make implant coatings and supply appropriate materials for implant construction. Furthermore, the bioactivity and therapeutic efficacy of metal NPs in peri-implant diseases (PID) are substantiated by a plethora of in vitro and in vivo studies. Furthermore, the use of silver (Ag), gold (Au), zinc oxide, titanium oxide (TiO₂), copper (Cu), and iron oxide NPs as a cure for dental implant infections brought on by bacteria that have become resistant to several medications is the subject of recent dentistry research. Because of their unique shape-dependent features, which enhance bio-physio-chemical functionalization, antibacterial activity, and biocompatibility, metal NPs are employed in dental implants. This study attempted to provide an overview of the application of metal and metal oxide NPs to control and increase the success rate of implants while focusing on the antimicrobial properties of these NPs in the treatment of PID, including PIM and PI. Additionally, the study reviewed the potential benefits and drawbacks of using metal NPs in clinical settings for managing PID, with the goal of advancing future treatment strategies for these conditions.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"101"},"PeriodicalIF":2.9,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11470642/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of the biaxial tensile behavior of ovine esophageal tissue using artificial neural networks.","authors":"H M Ngwangwa, D Modungwa, T Pandelani, F J Nemavhola","doi":"10.1186/s12938-024-01296-y","DOIUrl":"https://doi.org/10.1186/s12938-024-01296-y","url":null,"abstract":"<p><p>Diseases of the esophagus affect its function and often lead to replacement of long sections of the organ. Current healing methods involve the use of bioscaffolds processed from other animal models. Although the properties of these animal models are not exactly the same as those of the human esophagus, they nevertheless present a reasonable means of assessing the biomechanical properties of the esophageal tissue. Besides, sheep bear many similarities physiologically to humans and they also suffer from same diseases as humans. The morphology of their esophagus is also comparable to that of humans. Thus, in the study, an ovine esophagus was studied. Studies on the planar biaxial tests of the gross esophageal anatomy are limited. The composite nature of the gross anatomy of the esophagus makes the application of structure-based models such as Holzapfel-type models very difficult. In current studies the tissue is therefore often separated into specific layers with substantial collagen content. The effects of adipose tissue and other non-collagenous tissue often make the mechanical behavior of the esophagus widely diverse and unpredictable using deterministic structure-based models. Thus, it may be very difficult to predict its mechanical behavior. In the study, an NARX neural network was used to predict the stress-strain response of the gross anatomy of the ovine esophagus. The results show that the NARX model was able to achieve a correlation above 99.9% within a fitting error margin of 16%. Therefore, the use of artificial neural networks may provide a more accurate way of predicting the biaxial stress-strain response of the esophageal tissue, and lead to further improvements in the design and development of synthetic replacement materials for esophageal tissue.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"100"},"PeriodicalIF":2.9,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11470611/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Nuclear proteins and diabetic retinopathy: a review.","authors":"Bin Li, Wahab Hussain, Zhi-Liang Jiang, Jia-Yi Wang, Sarfraz Hussain, Talat Bilal Yasoob, Yuan-Kun Zhai, Xin-Ying Ji, Ya-Long Dang","doi":"10.1186/s12938-024-01293-1","DOIUrl":"10.1186/s12938-024-01293-1","url":null,"abstract":"","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"99"},"PeriodicalIF":2.9,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11465530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142399240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using 2D U-Net convolutional neural networks for automatic acetabular and proximal femur segmentation of hip MRI images and morphological quantification: a preliminary study in DDH.","authors":"Dian Zhang, Hongyan Zhou, Tianli Zhou, Yan Chang, Lei Wang, Mao Sheng, Huihui Jia, Xiaodong Yang","doi":"10.1186/s12938-024-01291-3","DOIUrl":"10.1186/s12938-024-01291-3","url":null,"abstract":"<p><strong>Background: </strong>Developmental dysplasia of the hip (DDH) is a common pediatric orthopedic condition characterized by varying degrees of acetabular dysplasia and hip dislocation. Current 2D imaging methods often fail to provide sufficient anatomical detail for effective treatment planning, leading to higher rates of misdiagnosis and missed diagnoses. MRI, with its advantages of being radiation-free, multi-planar, and containing more anatomical information, can provide the crucial morphological and volumetric data needed to evaluate DDH. However, manual techniques for measuring parameters like the center-edge angle (CEA) and acetabular index (AI) are time-consuming. Automating these processes is essential for accurate clinical assessments and personalized treatment strategies.</p><p><strong>Methods: </strong>This study employed a U-Net-based CNN model to automate the segmentation of hip MRI images in children. The segmentation process was validated using a leave-one-out method during training. Subsequently, the segmented hip joint images were utilized in clinical settings to perform automated measurements of key angles: AI, femoral neck angle (FNA), and CEA. This automated approach aimed to replace manual measurements and provide an objective reference for clinical assessments.</p><p><strong>Results: </strong>The U-Net-based network demonstrates high effectiveness in hip segmentation compared to manual radiologist segmentations. In test data, it achieves average DSC values of 0.9109 (acetabulum) and 0.9244 (proximal femur), with a 91.76% segmentation success rate. The average ASD values are 0.3160 mm (acetabulum) and 0.6395 mm (proximal femur) in test data, with Ground Truth (GT) edge points and predicted segmentation maps having a mean distance of less than 1 mm. Using automated segmentation models for clinical hip angle measurements (CEA, AI, FNA) shows no statistical difference compared to manual measurements (p > 0.05).</p><p><strong>Conclusion: </strong>Utilizing U-Net-based image segmentation and automated measurement of morphological parameters significantly enhances the accuracy and efficiency of DDH assessment. These methods improve precision in automatic measurements and provide an objective basis for clinical diagnosis and treatment of DDH.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"98"},"PeriodicalIF":2.9,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11453042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early diagnostic model of pyonephrosis with calculi based on radiomic features combined with clinical variables.","authors":"Yongchao Yan, Yunbo Liu, Yize Guo, Bin Li, Yanjiang Li, Xinning Wang","doi":"10.1186/s12938-024-01295-z","DOIUrl":"10.1186/s12938-024-01295-z","url":null,"abstract":"<p><strong>Objective: </strong>This retrospective aims to develop a comprehensive predictive model based on CT radiomic features and clinical parameters, facilitating early preoperative diagnosis of pyonephrosis.</p><p><strong>Methods: </strong>Clinical and radiological data from 311 patients treated for upper urinary tract stones with obstructive pyelohydronephrosis, between January 2018 and May 2023, were retrospectively collected. Univariate and multivariate logistic regression analyses were conducted on clinical data to identify independent risk factors for pyonephrosis. A clinical model was developed using logistic regression. The 3D Slicer software was employed to manually delineate the region of interest (ROI) in the preoperative CT images, corresponding to the area of pyelohydronephrosis, for feature extraction. The optimal radiomic features were selected to construct radiomic models and calculate the radiomic score (Radscore). Subsequently, a combined clinical-radiomic model-the nomogram-was established by integrating the Radscore with independent risk factors.</p><p><strong>Results: </strong>Univariate and multivariate logistic regression analyses identified cystatin C, Hounsfield Unit (HU) of pyonephrosis, history of ipsilateral urological surgery, and positive urine culture as independent risk factors for pyonephrosis (P < 0.05). Fourteen optimal radiomic features were selected from CT images to construct four radiomic models, with the Naive Bayes model demonstrating the best predictive performance in both training and validation sets. In the training set, the AUCs for the clinical model, radiomic model, and nomogram were 0.902, 0.939, and 0.991, respectively; in the validation set, they were 0.843, 0.874, and 0.959. Both calibration and decision curves showed good agreement between the predicted probabilities of the nomogram and the actual occurrences.</p><p><strong>Conclusion: </strong>The nomogram, constructed from CT radiomic features and clinical variables, provides an effective non-invasive predictive tool for pyonephrosis, surpassing both clinical and radiomic models.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"97"},"PeriodicalIF":2.9,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11448426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142370911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive review of composition, properties, clinical applications, and future perspectives of calcium-enriched mixture (CEM) cement: a systematic analysis","authors":"Saeed Asgary, Mahtab Aram, Mahta Fazlyab","doi":"10.1186/s12938-024-01290-4","DOIUrl":"https://doi.org/10.1186/s12938-024-01290-4","url":null,"abstract":"This review aims to comprehensively explore calcium-enriched mixture (CEM) cement as a crucial biomaterial in dentistry/endodontics. With its growing clinical relevance, there is a need to evaluate its composition, chemical/physical/biological properties, clinical applications, and future perspectives to provide clinicians/researchers with a detailed understanding of its potential in endodontic procedures. Through systematic analysis of available evidence, we assess the advantages/limitations of CEM cement, offering valuable insights for informed decision-making in dental/endodontic practice. Our findings highlight the commendable chemical/physical properties of CEM cement, including handling characteristics, alkalinity, color stability, bioactivity, biocompatibility, sealing ability, and antimicrobial properties. Importantly, CEM cement has shown the potential in promoting regenerative processes, such as dentinogenesis and cementogenesis. It has demonstrated successful outcomes in various clinical applications, including vital pulp therapy techniques, endodontic surgery, open apices management, root resorption/perforation repair, and as an orifice/root canal obturation material. The efficacy and reliability of CEM cement in diverse clinical scenarios underscore its effectiveness in endodontic practice. However, we emphasize the need for well-designed clinical trials with long-term follow-up to further substantiate the full potential of CEM cement. This review serves as a robust reference for researchers/practitioners, offering an in-depth exploration of CEM cement and its multifaceted roles in contemporary dentistry/endodontics.","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"30 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142260140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The value of CCTA combined with machine learning for predicting angina pectoris in the anomalous origin of the right coronary artery","authors":"Ying Wang, MengXing Wang, Mingyuan Yuan, Wenxian Peng","doi":"10.1186/s12938-024-01286-0","DOIUrl":"https://doi.org/10.1186/s12938-024-01286-0","url":null,"abstract":"Anomalous origin of coronary artery is a common coronary artery anatomy anomaly. The anomalous origin of the coronary artery may lead to problems such as narrowing of the coronary arteries at the beginning of the coronary arteries and abnormal alignment, which may lead to myocardial ischemia due to the compression of the coronary arteries. Clinical symptoms include chest tightness and dyspnea, with angina pectoris as a common symptom that can be life-threatening. Timely and accurate diagnosis of anomalous coronary artery origin is of great importance. Coronary computed tomography angiography (CCTA) can provide detailed information on the characteristics of coronary arteries. Therefore, we combined CCTA and artificial intelligence (AI) technology to analyze the CCTA image features and clinical features of patients with anomalous origin of the right coronary artery to predict angina pectoris and the relevance of different features to angina pectoris. In this retrospective analysis, we compiled data on 15 characteristics from 126 patients diagnosed with anomalous right coronary artery origins. The dataset encompassed both CCTA imaging attributes, such as the positioning of the right coronary artery orifices and the alignment of coronary arteries, and clinical parameters including gender and age. To identify the most salient features, we employed the Chi-square feature selection method, which filters features based on their statistical significance. We then focused on features yielding a Chi-square score exceeding a threshold of 1, thereby narrowing down the selection to seven key variables, including cardiac function and gender. Subsequently, we evaluated seven classifiers known for their efficacy in classification tasks. Through rigorous training and testing, we conducted a comparative analysis to identify the top three classifiers with the highest accuracy rates. The top three classifiers in this study are Support Vector Machine (SVM), Ensemble Learning (EL), and Kernel Approximation Classifier. Among the SVM, EL and Kernel Approximation Classifier-based classifiers, the best performance is achieved for linear SVM, optimizable Ensembles Learning and SVM kernel, respectively. And the corresponding accuracy is 75.7%, 75.7%, and 73.0%, respectively. The AUC values are 0.77, 0.80, and 0.75, respectively. Machine learning (ML) models can predict angina pectoris caused by the origin anomalous of the right coronary artery, providing valuable auxiliary diagnostic information for clinicians and serving as a warning to clinicians. It is hoped that timely intervention and treatment can be realized to avoid serious consequences such as myocardial infarction.","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"15 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}