Pub Date : 2024-10-18DOI: 10.1016/j.cmpb.2024.108468
Heming Cheng , Dongfang Ding , Jifeng Dai , Gen Li , Ke Zhang , Jianyun Li , Liuchuang Wei , Xue Zhang , Jie Hou
Background and objective
It is an indisputable physiological phenomenon that the arterial axial pre-stretch ratio (AAPSR) decreases with age, but little attention has been paid to the effect of this reduction on chronic diseases during aging.
Methods
Here we reported an experimental method to simulate arteries aging, developed a fluid-structure interaction model with the effect of AAPSR changes, and compared it with the anatomy data and structural parameters of the human thoracic aorta.
Results
We showed that with the process of aging, the decrease of AAPSR leads to a decline of arterial elasticity, a decrease of arterial elastic strain energy, which weakens the ability to promote blood circulation, the corresponding decrease in cardiac output (CO) and cerebral blood flow (CBF) causes distal organ and body tissue ischemia, which is one of the main causes of increased blood pressure and decreased cerebral perfusion in the elderly.
Conclusions
Thus, reduced AAPSR is the one of main manifestation of arteries aging and has an important impact on hypertension and hypoperfusion of the brain in the process of human aging. The research contributes to a better understanding of the physiological and pathological mechanisms of aging-related diseases.
{"title":"Effect of a reduced arterial axial pre-stretch ratio during aging on the cardiac output and cerebral blood flow in the healthy elders","authors":"Heming Cheng , Dongfang Ding , Jifeng Dai , Gen Li , Ke Zhang , Jianyun Li , Liuchuang Wei , Xue Zhang , Jie Hou","doi":"10.1016/j.cmpb.2024.108468","DOIUrl":"10.1016/j.cmpb.2024.108468","url":null,"abstract":"<div><h3>Background and objective</h3><div>It is an indisputable physiological phenomenon that the arterial axial pre-stretch ratio (AAPSR) decreases with age, but little attention has been paid to the effect of this reduction on chronic diseases during aging.</div></div><div><h3>Methods</h3><div>Here we reported an experimental method to simulate arteries aging, developed a fluid-structure interaction model with the effect of AAPSR changes, and compared it with the anatomy data and structural parameters of the human thoracic aorta.</div></div><div><h3>Results</h3><div>We showed that with the process of aging, the decrease of AAPSR leads to a decline of arterial elasticity, a decrease of arterial elastic strain energy, which weakens the ability to promote blood circulation, the corresponding decrease in cardiac output (CO) and cerebral blood flow (CBF) causes distal organ and body tissue ischemia, which is one of the main causes of increased blood pressure and decreased cerebral perfusion in the elderly.</div></div><div><h3>Conclusions</h3><div>Thus, reduced AAPSR is the one of main manifestation of arteries aging and has an important impact on hypertension and hypoperfusion of the brain in the process of human aging. The research contributes to a better understanding of the physiological and pathological mechanisms of aging-related diseases.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108468"},"PeriodicalIF":4.9,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142496559","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}
Attaining global context along with local dependencies is of paramount importance for achieving highly accurate segmentation of objects from image frames and is challenging while developing deep learning-based biomedical image segmentation. Several transformer-based models have been proposed to handle this issue in biomedical image segmentation. Despite this, segmentation accuracy remains an ongoing challenge, as these models often fall short of the target range due to their limited capacity to capture critical local and global contexts. However, the quadratic computational complexity is the main limitation of these models. Moreover, a large dataset is required to train those models.
Methods:
In this paper, we propose a novel multi-scale dual-channel decoder to mitigate this issue. The complete segmentation model uses two parallel encoders and a dual-channel decoder. The encoders are based on convolutional networks, which capture the features of the input images at multiple levels and scales. The decoder comprises a hierarchy of Attention-gated Swin Transformers with a fine-tuning strategy. The hierarchical Attention-gated Swin Transformers implements a multi-scale, multi-level feature embedding strategy that captures short and long-range dependencies and leverages the necessary features without increasing computational load. At the final stage of the decoder, a fine-tuning strategy is implemented that refines the features to keep the rich features and reduce the possibility of over-segmentation.
Results:
The proposed model is evaluated on publicly available LiTS, 3DIRCADb, and spleen datasets obtained from Medical Segmentation Decathlon. The model is also evaluated on a private dataset from Medical College Kolkata, India. We observe that the proposed model outperforms the state-of-the-art models in liver tumor and spleen segmentation in terms of evaluation metrics at a comparative computational cost.
Conclusion:
The novel dual-channel decoder embeds multi-scale features and creates a representation of both short and long-range contexts efficiently. It also refines the features at the final stage to select only necessary features. As a result, we achieve better segmentation performance than the state-of-the-art models.
{"title":"Multi-scale dual-channel feature embedding decoder for biomedical image segmentation","authors":"Rohit Agarwal , Palash Ghosal , Anup K. Sadhu , Narayan Murmu , Debashis Nandi","doi":"10.1016/j.cmpb.2024.108464","DOIUrl":"10.1016/j.cmpb.2024.108464","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Attaining global context along with local dependencies is of paramount importance for achieving highly accurate segmentation of objects from image frames and is challenging while developing deep learning-based biomedical image segmentation. Several transformer-based models have been proposed to handle this issue in biomedical image segmentation. Despite this, segmentation accuracy remains an ongoing challenge, as these models often fall short of the target range due to their limited capacity to capture critical local and global contexts. However, the quadratic computational complexity is the main limitation of these models. Moreover, a large dataset is required to train those models.</div></div><div><h3>Methods:</h3><div>In this paper, we propose a novel multi-scale dual-channel decoder to mitigate this issue. The complete segmentation model uses two parallel encoders and a dual-channel decoder. The encoders are based on convolutional networks, which capture the features of the input images at multiple levels and scales. The decoder comprises a hierarchy of Attention-gated Swin Transformers with a fine-tuning strategy. The hierarchical Attention-gated Swin Transformers implements a multi-scale, multi-level feature embedding strategy that captures short and long-range dependencies and leverages the necessary features without increasing computational load. At the final stage of the decoder, a fine-tuning strategy is implemented that refines the features to keep the rich features and reduce the possibility of over-segmentation.</div></div><div><h3>Results:</h3><div>The proposed model is evaluated on publicly available LiTS, 3DIRCADb, and spleen datasets obtained from Medical Segmentation Decathlon. The model is also evaluated on a private dataset from Medical College Kolkata, India. We observe that the proposed model outperforms the state-of-the-art models in liver tumor and spleen segmentation in terms of evaluation metrics at a comparative computational cost.</div></div><div><h3>Conclusion:</h3><div>The novel dual-channel decoder embeds multi-scale features and creates a representation of both short and long-range contexts efficiently. It also refines the features at the final stage to select only necessary features. As a result, we achieve better segmentation performance than the state-of-the-art models.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108464"},"PeriodicalIF":4.9,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142496561","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}
Pub Date : 2024-10-18DOI: 10.1016/j.cmpb.2024.108465
Arkadiusz Gertych , Oliver Faust
{"title":"AI explainability and bias propagation in medical decision support","authors":"Arkadiusz Gertych , Oliver Faust","doi":"10.1016/j.cmpb.2024.108465","DOIUrl":"10.1016/j.cmpb.2024.108465","url":null,"abstract":"","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108465"},"PeriodicalIF":4.9,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142496557","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}
Pub Date : 2024-10-16DOI: 10.1016/j.cmpb.2024.108467
Dario Carbonaro , Nicola Ferro , Francesco Mezzadri , Diego Gallo , Alberto L. Audenino , Simona Perotto , Umberto Morbiducci , Claudio Chiastra
Background and objectives
Vascular stents are scaffolding structures implanted in the vessels of patients with obstructive disease. Stents are typically designed as cylindrical lattice structures characterized by the periodic repetition of unit cells. Their design, including geometry and material characteristics, influences their mechanical performance and, consequently, the clinical outcomes. Computational optimization frameworks have proven to be effective in assisting the design phase of vascular stents, facilitating the achievement of enhanced mechanical performances. However, the reliance on time-consuming simulations and the challenge of automating the design process limit the number of design evaluations and reduce optimization efficiency. In this context, a rapid and automated method for the mechanical characterization of vascular stents is presented, taking the stent geometry, conceived as the periodic repetition of a unit cell, and material as input and providing the mechanical response of the stent as output.
Methods
Vascular stents were assumed to be thin-walled hollow cylinders sharing the same macroscopic geometrical characteristics as the cylindrical lattice structure but composed of an anisotropic homogenized material. Homogenization theory was applied to average the microscopic inhomogeneities at the stent unit cell level into a homogenized material at the macro-scale, enabling the calculation of the associated homogenized material tensor. Analytical formulations were derived to relate the stent mechanical behavior to the homogenized stiffness tensor, considering linear elastic theory for thin-walled hollow cylinders and three loading scenarios of relevance for vascular stents: radial crimping; axial traction; torsion. Validation was conducted by comparing the derived analytical formulations with results obtained from finite element analyses on typical stent designs.
Results
Homogenized stiffness tensors were computed for the unit cells of three stent designs, revealing insights into their mechanical performance, including whether they exhibit auxetic behavior. The derived analytical formulations were successfully validated with finite element analyses, yielding low relative differences in the computed values of foreshortening, radial, axial and torsional stiffnesses for all three stents.
Conclusions
The proposed method offers a rapid, fully automated procedure that facilitates the assessment of the mechanical behavior of vascular stents and is suitable for effective integration into computational optimization frameworks.
{"title":"Easy-to-use formulations based on the homogenization theory for vascular stent design and mechanical characterization","authors":"Dario Carbonaro , Nicola Ferro , Francesco Mezzadri , Diego Gallo , Alberto L. Audenino , Simona Perotto , Umberto Morbiducci , Claudio Chiastra","doi":"10.1016/j.cmpb.2024.108467","DOIUrl":"10.1016/j.cmpb.2024.108467","url":null,"abstract":"<div><h3>Background and objectives</h3><div>Vascular stents are scaffolding structures implanted in the vessels of patients with obstructive disease. Stents are typically designed as cylindrical lattice structures characterized by the periodic repetition of unit cells. Their design, including geometry and material characteristics, influences their mechanical performance and, consequently, the clinical outcomes. Computational optimization frameworks have proven to be effective in assisting the design phase of vascular stents, facilitating the achievement of enhanced mechanical performances. However, the reliance on time-consuming simulations and the challenge of automating the design process limit the number of design evaluations and reduce optimization efficiency. In this context, a rapid and automated method for the mechanical characterization of vascular stents is presented, taking the stent geometry, conceived as the periodic repetition of a unit cell, and material as input and providing the mechanical response of the stent as output.</div></div><div><h3>Methods</h3><div>Vascular stents were assumed to be thin-walled hollow cylinders sharing the same macroscopic geometrical characteristics as the cylindrical lattice structure but composed of an anisotropic homogenized material. Homogenization theory was applied to average the microscopic inhomogeneities at the stent unit cell level into a homogenized material at the macro-scale, enabling the calculation of the associated homogenized material tensor. Analytical formulations were derived to relate the stent mechanical behavior to the homogenized stiffness tensor, considering linear elastic theory for thin-walled hollow cylinders and three loading scenarios of relevance for vascular stents: radial crimping; axial traction; torsion. Validation was conducted by comparing the derived analytical formulations with results obtained from finite element analyses on typical stent designs.</div></div><div><h3>Results</h3><div>Homogenized stiffness tensors were computed for the unit cells of three stent designs, revealing insights into their mechanical performance, including whether they exhibit auxetic behavior. The derived analytical formulations were successfully validated with finite element analyses, yielding low relative differences in the computed values of foreshortening, radial, axial and torsional stiffnesses for all three stents.</div></div><div><h3>Conclusions</h3><div>The proposed method offers a rapid, fully automated procedure that facilitates the assessment of the mechanical behavior of vascular stents and is suitable for effective integration into computational optimization frameworks.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108467"},"PeriodicalIF":4.9,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142564117","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}
Transcranial focused ultrasound (tFUS) is an emerging non-invasive therapeutic technology that offers new brain stimulation modality. Precise localization of the acoustic focus to the desired brain target throughout the procedure is needed to ensure the safety and effectiveness of the treatment, but acoustic distortion caused by the skull poses a challenge. Although computational methods can provide the estimated location and shape of the focus, the computation has not reached sufficient speed for real-time inference, which is demanded in real-world clinical situations. Leveraging the advantages of deep learning, we propose multi-modal networks capable of generating intracranial pressure map in real-time.
Methods:
The dataset consisted of free-field pressure maps, intracranial pressure maps, medical images, and transducer placements was obtained from 11 human subjects. The free-field and intracranial pressure maps were computed using the k-space method. We developed network models based on convolutional neural networks and the Swin Transformer, featuring a multi-modal encoder and a decoder.
Results:
Evaluations on foreseen data achieved high focal volume conformity of approximately 93% for both computed tomography (CT) and magnetic resonance (MR) data. For unforeseen data, the networks achieved the focal volume conformity of 88% for CT and 82% for MR. The inference time of the proposed networks was under 0.02 s, indicating the feasibility for real-time simulation.
Conclusions:
The results indicate that our networks can effectively and precisely perform real-time simulation of the intracranial pressure map during tFUS applications. Our work will enhance the safety and accuracy of treatments, representing significant progress for low-intensity focused ultrasound (LIFU) therapies.
{"title":"Multi-modal networks for real-time monitoring of intracranial acoustic field during transcranial focused ultrasound therapy","authors":"Minjee Seo , Minwoo Shin , Gunwoo Noh , Seung-Schik Yoo , Kyungho Yoon","doi":"10.1016/j.cmpb.2024.108458","DOIUrl":"10.1016/j.cmpb.2024.108458","url":null,"abstract":"<div><h3>Background and objective:</h3><div>Transcranial focused ultrasound (tFUS) is an emerging non-invasive therapeutic technology that offers new brain stimulation modality. Precise localization of the acoustic focus to the desired brain target throughout the procedure is needed to ensure the safety and effectiveness of the treatment, but acoustic distortion caused by the skull poses a challenge. Although computational methods can provide the estimated location and shape of the focus, the computation has not reached sufficient speed for real-time inference, which is demanded in real-world clinical situations. Leveraging the advantages of deep learning, we propose multi-modal networks capable of generating intracranial pressure map in real-time.</div></div><div><h3>Methods:</h3><div>The dataset consisted of free-field pressure maps, intracranial pressure maps, medical images, and transducer placements was obtained from 11 human subjects. The free-field and intracranial pressure maps were computed using the <em>k</em>-space method. We developed network models based on convolutional neural networks and the Swin Transformer, featuring a multi-modal encoder and a decoder.</div></div><div><h3>Results:</h3><div>Evaluations on foreseen data achieved high focal volume conformity of approximately 93% for both computed tomography (CT) and magnetic resonance (MR) data. For unforeseen data, the networks achieved the focal volume conformity of 88% for CT and 82% for MR. The inference time of the proposed networks was under 0.02 s, indicating the feasibility for real-time simulation.</div></div><div><h3>Conclusions:</h3><div>The results indicate that our networks can effectively and precisely perform real-time simulation of the intracranial pressure map during tFUS applications. Our work will enhance the safety and accuracy of treatments, representing significant progress for low-intensity focused ultrasound (LIFU) therapies.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108458"},"PeriodicalIF":4.9,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142496560","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}
Pub Date : 2024-10-11DOI: 10.1016/j.cmpb.2024.108453
C. Rousseau , Q.L. Vuong , Y. Gossuin , B. Maes , G. Rosolen
Background and Objective
: Theranostics is the combination of the diagnostic and therapeutic phases. Here we focus on simultaneous use of photothermal therapy and magnetic resonance imaging, employing a contrast-photothermal agent that converts incident light into heat and affects the transverse relaxation time, a key magnetic resonance imaging parameter. Our work considers a gold–magnetite nanoshell platform to gauge the feasibility of magnetic resonance imaging monitoring of the heating associated with phototherapy, by studying the modification of the transverse relaxation rate induced by laser illumination of a solution containing these hybrid nanoparticles.
Methods:
We simulate a system composed of an aqueous solution with hybrid nanoshells under continuous laser irradiation, enabling the evaluation of spatial variations of the transverse relaxation rate within the sample. We work with the hybrid nanoshell platform comprising a metal/gold shell for thermoplasmonic effects and a magnetite core for magnetic resonance imaging contrast enhancement. The optical properties of the nanoshells are first obtained through simulations using the finite element method. Next, the heating generated by the laser illumination is calculated by numerical integration. Finally, the transverse relaxation rate is obtained through the application of an analytical model. Additionally, we conduct an optimization of the nanoshell geometry to fulfill requirements of both magnetic resonance imaging and phototherapy techniques.
Results:
Our findings demonstrate a narrow range of nanoshell sizes exhibiting both a plasmonic absorption peak in the human biological window and a high response to laser illumination of the transverse relaxation rate. Furthermore, the illumination can induce up to a 30% modification in transverse relaxation rate compared to the non-illuminated scenario in this range of nanoshell sizes.
Conclusions:
In this work we establish the numerical understanding of the interplay between phototherapy and nuclear magnetic resonance imaging when employed concurrently. This allows magnetic resonance imaging monitoring of the heating associated with phototherapy.
{"title":"Concurrent photothermal therapy and nuclear magnetic resonance imaging with plasmonic–magnetic nanoparticles: A numerical study","authors":"C. Rousseau , Q.L. Vuong , Y. Gossuin , B. Maes , G. Rosolen","doi":"10.1016/j.cmpb.2024.108453","DOIUrl":"10.1016/j.cmpb.2024.108453","url":null,"abstract":"<div><h3>Background and Objective</h3><div>: Theranostics is the combination of the diagnostic and therapeutic phases. Here we focus on simultaneous use of photothermal therapy and magnetic resonance imaging, employing a contrast-photothermal agent that converts incident light into heat and affects the transverse relaxation time, a key magnetic resonance imaging parameter. Our work considers a gold–magnetite nanoshell platform to gauge the feasibility of magnetic resonance imaging monitoring of the heating associated with phototherapy, by studying the modification of the transverse relaxation rate induced by laser illumination of a solution containing these hybrid nanoparticles.</div></div><div><h3>Methods:</h3><div>We simulate a system composed of an aqueous solution with hybrid nanoshells under continuous laser irradiation, enabling the evaluation of spatial variations of the transverse relaxation rate within the sample. We work with the hybrid nanoshell platform comprising a metal/gold shell for thermoplasmonic effects and a magnetite core for magnetic resonance imaging contrast enhancement. The optical properties of the nanoshells are first obtained through simulations using the finite element method. Next, the heating generated by the laser illumination is calculated by numerical integration. Finally, the transverse relaxation rate is obtained through the application of an analytical model. Additionally, we conduct an optimization of the nanoshell geometry to fulfill requirements of both magnetic resonance imaging and phototherapy techniques.</div></div><div><h3>Results:</h3><div>Our findings demonstrate a narrow range of nanoshell sizes exhibiting both a plasmonic absorption peak in the human biological window and a high response to laser illumination of the transverse relaxation rate. Furthermore, the illumination can induce up to a 30% modification in transverse relaxation rate compared to the non-illuminated scenario in this range of nanoshell sizes.</div></div><div><h3>Conclusions:</h3><div>In this work we establish the numerical understanding of the interplay between phototherapy and nuclear magnetic resonance imaging when employed concurrently. This allows magnetic resonance imaging monitoring of the heating associated with phototherapy.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108453"},"PeriodicalIF":4.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142459887","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}
Pub Date : 2024-10-11DOI: 10.1016/j.cmpb.2024.108455
Hardik Telangore , Victor Azad , Manish Sharma , Ankit Bhurane , Ru San Tan , U. Rajendra Acharya
Background and Objective:
Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. However, current methods predict SCD only a few minutes before its onset, limiting intervention time. This study aims to develop a deep learning-based model for the early prediction of SCD using electrocardiography (ECG) signals.
Methods:
A multimodal explainable deep learning-based model is developed to analyze ECG signals at discrete intervals ranging from 5 to 30 min before SCD onset. The raw ECG signals, 2D scalograms generated through wavelet transform and 2D Hilbert spectrum generated through Hilbert–Huang transform (HHT) of ECG signals were applied to multiple deep learning algorithms. For raw ECG, a combination of 1D-convolutional neural networks (1D-CNN) and long short-term memory networks were employed for feature extraction and temporal pattern recognition. Besides, to extract and analyze features from scalograms and Hilbert spectra, Vision Transformer (ViT) and 2D-CNN have been used.
Results:
The developed model achieved high performance, with accuracy, precision, recall and F1-score of 98.81%, 98.83%, 98.81%, and 98.81% respectively to predict SCD onset 30 min in advance. Further, the proposed model can accurately classify SCD patients and normal controls with 100% accuracy. Thus, the proposed method outperforms the existing state-of-the-art methods.
Conclusions:
The developed model is capable of capturing diverse patterns on ECG signals recorded at multiple discrete time intervals (at 5-minute increments from 5 min to 30 min) prior to SCD onset that could discriminate for SCD. The proposed model significantly improves early SCD prediction, providing a valuable tool for continuous ECG monitoring in high-risk patients.
{"title":"Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert–Huang and wavelet transforms with explainable vision transformer and CNN models","authors":"Hardik Telangore , Victor Azad , Manish Sharma , Ankit Bhurane , Ru San Tan , U. Rajendra Acharya","doi":"10.1016/j.cmpb.2024.108455","DOIUrl":"10.1016/j.cmpb.2024.108455","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. However, current methods predict SCD only a few minutes before its onset, limiting intervention time. This study aims to develop a deep learning-based model for the early prediction of SCD using electrocardiography (ECG) signals.</div></div><div><h3>Methods:</h3><div>A multimodal explainable deep learning-based model is developed to analyze ECG signals at discrete intervals ranging from 5 to 30 min before SCD onset. The raw ECG signals, 2D scalograms generated through wavelet transform and 2D Hilbert spectrum generated through Hilbert–Huang transform (HHT) of ECG signals were applied to multiple deep learning algorithms. For raw ECG, a combination of 1D-convolutional neural networks (1D-CNN) and long short-term memory networks were employed for feature extraction and temporal pattern recognition. Besides, to extract and analyze features from scalograms and Hilbert spectra, Vision Transformer (ViT) and 2D-CNN have been used.</div></div><div><h3>Results:</h3><div>The developed model achieved high performance, with accuracy, precision, recall and F1-score of 98.81%, 98.83%, 98.81%, and 98.81% respectively to predict SCD onset 30 min in advance. Further, the proposed model can accurately classify SCD patients and normal controls with 100% accuracy. Thus, the proposed method outperforms the existing state-of-the-art methods.</div></div><div><h3>Conclusions:</h3><div>The developed model is capable of capturing diverse patterns on ECG signals recorded at multiple discrete time intervals (at 5-minute increments from 5 min to 30 min) prior to SCD onset that could discriminate for SCD. The proposed model significantly improves early SCD prediction, providing a valuable tool for continuous ECG monitoring in high-risk patients.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108455"},"PeriodicalIF":4.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142496558","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}
Scaffolds designed for tissue engineering must consider multiple parameters, namely the permeability of the design and the wall shear stress experienced by the cells on the scaffold surface. However, these parameters are not independent from each other, with changes that improve wall shear stress, negatively impacting permeability and vice versa. This study introduces a novel multi-objective optimization framework using Direct MultiSearch (DMS) to design triply periodic minimal surface (TPMS) scaffolds for bone tissue engineering.
Method
The optimization algorithm focused on maximizing the permeability of the scaffolds and obtaining a desired value of average wall shear stress (which ranges between the values that promote osteogenic differentiation of 0.1 mPa and 10 mPa). Multiple fluid inlet velocities and target wall shear stress were analyzed. The DMS method successfully generated Pareto fronts for each configuration, enabling the selection of optimized scaffolds based on specific structural requirements.
Results
The findings reveal that increasing the target wall shear stress results in a greater number of non-dominated points on the Pareto front, highlighting a more robust optimization process. Additionally, it was also demonstrated that the tested Schwartz diamond scaffolds had a better permeability-wall shear stress relation when compared to Schoen gyroid geometries.
Conclusions
Direct MultiSearch was proven as an effective tool to aid in the design of tissue engineering scaffolds. This adaptable optimization framework has potential applications beyond bone tissue engineering, including cartilage tissue differentiation.
{"title":"Direct MultiSearch optimization of TPMS scaffolds for bone tissue engineering","authors":"T.H.V. Pires , J.F.A. Madeira , A.P.G. Castro , P.R. Fernandes","doi":"10.1016/j.cmpb.2024.108461","DOIUrl":"10.1016/j.cmpb.2024.108461","url":null,"abstract":"<div><h3>Background</h3><div>Scaffolds designed for tissue engineering must consider multiple parameters, namely the permeability of the design and the wall shear stress experienced by the cells on the scaffold surface. However, these parameters are not independent from each other, with changes that improve wall shear stress, negatively impacting permeability and vice versa. This study introduces a novel multi-objective optimization framework using Direct MultiSearch (DMS) to design triply periodic minimal surface (TPMS) scaffolds for bone tissue engineering.</div></div><div><h3>Method</h3><div>The optimization algorithm focused on maximizing the permeability of the scaffolds and obtaining a desired value of average wall shear stress (which ranges between the values that promote osteogenic differentiation of 0.1 mPa and 10 mPa). Multiple fluid inlet velocities and target wall shear stress were analyzed. The DMS method successfully generated Pareto fronts for each configuration, enabling the selection of optimized scaffolds based on specific structural requirements.</div></div><div><h3>Results</h3><div>The findings reveal that increasing the target wall shear stress results in a greater number of non-dominated points on the Pareto front, highlighting a more robust optimization process. Additionally, it was also demonstrated that the tested Schwartz diamond scaffolds had a better permeability-wall shear stress relation when compared to Schoen gyroid geometries.</div></div><div><h3>Conclusions</h3><div>Direct MultiSearch was proven as an effective tool to aid in the design of tissue engineering scaffolds. This adaptable optimization framework has potential applications beyond bone tissue engineering, including cartilage tissue differentiation.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"257 ","pages":"Article 108461"},"PeriodicalIF":4.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441512","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}
Pub Date : 2024-10-10DOI: 10.1016/j.cmpb.2024.108460
Georgia D. Liapi , Christos P. Loizou , Constantinos S. Pattichis , Marios S. Pattichis , Andrew N. Nicolaides , Maura Griffin , Efthyvoulos Kyriacou
<div><h3>Background and objective</h3><div>Carotid B-mode ultrasound (CBUS) imaging is often used to detect and assess atherosclerotic plaques. Doctors often need to segment plaques in the CBUS images to further examine them. Multiple studies have proposed two-dimensional CBUS plaque segmentation deep learning (DL)-based solutions, achieving promising results. In most of these studies, image standardization is not reported, while not all plaque types are represented. However, prior multiple studies have highlighted the importance of data standardization in computerized CBUS plaque classification or segmentation solutions. In this study, we propose and separately evaluate three progressive preprocessing schemes, to discover the most optimal to standardize CBUS images for DL-based carotid plaque segmentation, while we also assess the effect of each preprocessing in the segmentation performance per echodensity-based plaque type (I, II, III, IV and V).</div></div><div><h3>Methods</h3><div>We included three CBUS image datasets (276 CBUS images, from three medical centres), with which we produced 3 data folds (with the best possible equal inclusion of images from all centers per fold), to perform 3-fold cross validation-based training and evaluation of the pre-released Channel-wise Feature Pyramid Network for Medicine (CFPNet-M) model, in carotid plaque type segmentation. We included the three data folds in their original version (O), generating also three preprocessed versions of them, namely, the resolution-normalized (R), the resolution- and intensity-normalized (RN), and the resolution- and intensity-normalized combined with despeckling (RND) versions. The samples were cropped to the plaque level, and the intersection over union (IoU) and the Dice Similarity Coefficient (DSC), along with other metrics, were used to measure the model's performance. In each training round, 12 % of the images in the 2 training folds was used for internal validation (last fold was used in evaluation). Two experienced ultrasonographers manually delineated plaques in the dataset, to provide us with ground truths, while the plaque types (I to V) were extracted according to the Gray-Weale and Geroulakos classification system. We measured the mean±standard deviation of DSC within and across the three evaluated folds, per preprocessing scheme and per plaque type.</div></div><div><h3>Results</h3><div>CFPNet-M segmented the plaques in the CBUS images in all the data preprocessing versions, yielding progressively improved performances (mean DSC at 81.9 ± 9.1 %, 83.6 ± 9.0 %, 84.1 ± 8.3 %, and 84.4 ± 8.1 % for the O, R, RN and RND 3-fold cross validation processes, respectively), irrespective of the plaque type. Interestingly, CFPNet_M yielded improved performances, for all plaque types (I, II, III, IV and V), when trained and tested with the RND data versus the O version, achieving an 80.6 ± 11 % versus 77.6 ± 17 % DSC for type I, an 84.3 ± 8 % versus 81.2 ± 9 % DSC for type II
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Pub Date : 2024-10-10DOI: 10.1016/j.cmpb.2024.108457
Peishuo Wu, Chi Zhu
Background and Objective:
Incorporating tissue support in fluid–structure interaction analysis of cardiovascular flows is crucial for accurately representing physiological constraints, achieving realistic vessel wall motion, and minimizing artificial oscillations. The generalized Robin boundary condition, which models tissue support with a spring-damper-type force, uses elastic and damping parameters to represent the viscoelastic behavior of perivascular tissues. Using spatially distributed parameters for tissue support, rather than uniform ones, is more realistic and aligns with the varying properties of vessel walls. However, considering the spatial distribution of both can increase the complexity of preprocessing and numerical implementation. In this work, we develop an effective property method for efficient modeling of non-uniform tissue support and quantifying the contribution of tissue support to the mechanical behaviors of vessel walls.
Methods:
Based on the theory of linear viscoelasticity, we derive the mathematical formulas for the effective property method, integrating the parameters of generalized Robin boundary condition into vessel wall properties. The pulse wave velocity incorporating the influence of tissue support is also analyzed. Furthermore, we modify the coupled momentum method, originally formulated for elastic problems, to account for the viscoelastic properties of the vessel wall.
Results:
The method is verified with three-dimensional fluid–structure interaction simulations, achieving a maximum relative error of less than 2.2% for flow rate and less than 0.7% for pressure. This method shows that tissue support parameters can be integrated into vessel wall properties, resulting in increased apparent wall stiffness and viscosity, and further changing pressure, flow rate, and wave propagation.
Conclusion:
In this study, we develop an effective property method for quantitatively assessing the impact of tissue support and for efficiently modeling non-uniform tissue support. Moreover, this method offers further insights into clinically measured pulse wave velocity, demonstrating that it reflects the combined influence of both vessel wall properties and tissue support.
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