Pub Date : 2024-05-01Epub Date: 2023-10-09DOI: 10.1115/1.4063464
Martin L Tanaka, Orlando Lopez
The nature of collaborations between industry, academic, and government entities are discussed by the authors who together have significant experience in all three of these sectors. This article examines the intricacies and coordination needed between different stakeholder environments toward successful medical device innovation. The value of different types of collaboration models is illustrated through examples and the author's perspectives on current opportunities, challenges, and future outlook.
{"title":"Outlook on Industry-Academia-Government Collaborations Impacting Medical Device Innovation.","authors":"Martin L Tanaka, Orlando Lopez","doi":"10.1115/1.4063464","DOIUrl":"https://doi.org/10.1115/1.4063464","url":null,"abstract":"<p><p>The nature of collaborations between industry, academic, and government entities are discussed by the authors who together have significant experience in all three of these sectors. This article examines the intricacies and coordination needed between different stakeholder environments toward successful medical device innovation. The value of different types of collaboration models is illustrated through examples and the author's perspectives on current opportunities, challenges, and future outlook.</p>","PeriodicalId":73734,"journal":{"name":"Journal of engineering and science in medical diagnostics and therapy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583288/pdf/jesmdt-23-1034_025001.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49685829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spine degeneration is a normal aging process. It may lead to stenotic spines that may have implications for pain and quality of life. The diagnosis is based on clinical symptomatology and imaging. Magnetic resonance images often reveal the nature and degree of stenosis of the spine. Stenosis is concerning to clinicians and patients because of the decreased space in the spinal canal and potential for elevated risk of cord and/or osteoligamentous spinal column injuries. Numerous finite element models of the cervical spine have been developed to study the biomechanics of the osteoligamentous column such as range of motion and vertebral stress; however, spinal cord modeling is often ignored. The objective of this study was to determine the external column and internal cord and disc responses of stenotic spines using finite element modeling. A validated model of the subaxial spinal column was used. The osteoligamentous column was modified to include the spinal cord. Mild, moderate, and severe degrees of stenosis commonly identified in civilian populations were simulated at C5-C6. The column-cord model was subjected to postero-anterior acceleration at T1. The range of motion, disc pressure, and cord stress-strain were obtained at the index and superior and inferior adjacent levels of the stenosis. The external metric representing the segmental motion was insensitive while the intrinsic disc and cord variables were more sensitive, and the index level was more affected by stenosis. These findings may influence surgical planning and patient education in personalized medicine.
{"title":"Cervical Column and Cord and Column Responses in Whiplash With Stenosis: A Finite Element Modeling Study.","authors":"Narayan Yoganandan, Balaji Harinathan, Aditya Vedantam","doi":"10.1115/1.4063250","DOIUrl":"10.1115/1.4063250","url":null,"abstract":"<p><p>Spine degeneration is a normal aging process. It may lead to stenotic spines that may have implications for pain and quality of life. The diagnosis is based on clinical symptomatology and imaging. Magnetic resonance images often reveal the nature and degree of stenosis of the spine. Stenosis is concerning to clinicians and patients because of the decreased space in the spinal canal and potential for elevated risk of cord and/or osteoligamentous spinal column injuries. Numerous finite element models of the cervical spine have been developed to study the biomechanics of the osteoligamentous column such as range of motion and vertebral stress; however, spinal cord modeling is often ignored. The objective of this study was to determine the external column and internal cord and disc responses of stenotic spines using finite element modeling. A validated model of the subaxial spinal column was used. The osteoligamentous column was modified to include the spinal cord. Mild, moderate, and severe degrees of stenosis commonly identified in civilian populations were simulated at C5-C6. The column-cord model was subjected to postero-anterior acceleration at T1. The range of motion, disc pressure, and cord stress-strain were obtained at the index and superior and inferior adjacent levels of the stenosis. The external metric representing the segmental motion was insensitive while the intrinsic disc and cord variables were more sensitive, and the index level was more affected by stenosis. These findings may influence surgical planning and patient education in personalized medicine.</p>","PeriodicalId":73734,"journal":{"name":"Journal of engineering and science in medical diagnostics and therapy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583276/pdf/jesmdt-23-1045_021003.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49685827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper addresses the scarcity of comprehensive studies on the collective impact of various parametric lattice designs on mesostructure functionality. Focusing on optimizing the energy absorption of a serpentine mesostructure made using SLA, this research leverages a feedforward neural network to explore the interplay between line width, number of turns, and material properties on the energy absorbed by the structure. Compression simulations using a finite element model, covering a range of configurations, provided the dataset for neural network training. The resulting network was used to probe correlations between geometric variables, material, and energy absorption. Additionally, a neural network sensitivity analysis explored the impact of hidden layers and number of neurons on the network's performance, demonstrating the network's robustness. The optimized mesostructure configuration, identified by the neural network, maximized energy absorption. Using foundational mechanics of materials concepts, the discussion explains the how the geometry and material of the cellular mesostructure affects structural stiffness.
{"title":"Neural Network Modeling of an SLA Printed Mesostructure","authors":"Anne Schmitz","doi":"10.1115/1.4065291","DOIUrl":"https://doi.org/10.1115/1.4065291","url":null,"abstract":"\u0000 This paper addresses the scarcity of comprehensive studies on the collective impact of various parametric lattice designs on mesostructure functionality. Focusing on optimizing the energy absorption of a serpentine mesostructure made using SLA, this research leverages a feedforward neural network to explore the interplay between line width, number of turns, and material properties on the energy absorbed by the structure. Compression simulations using a finite element model, covering a range of configurations, provided the dataset for neural network training. The resulting network was used to probe correlations between geometric variables, material, and energy absorption. Additionally, a neural network sensitivity analysis explored the impact of hidden layers and number of neurons on the network's performance, demonstrating the network's robustness. The optimized mesostructure configuration, identified by the neural network, maximized energy absorption. Using foundational mechanics of materials concepts, the discussion explains the how the geometry and material of the cellular mesostructure affects structural stiffness.","PeriodicalId":73734,"journal":{"name":"Journal of engineering and science in medical diagnostics and therapy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140716495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
U-Net network is widely used in the field of medical image segmentation. The automatic segmentation and detection of lung nodules can help in the early detection of lung cancer. Therefore, in this paper, to solve the problems of small proportion of nodules in CT images, complex features and insufficient segmentation accuracy, an improved U-Net network based on residual network and attention mechanism was proposed. The feature extraction part of RkcU-Net network is based on Res2net, a variant of Resnet, and on which a feature extraction module with automatic selection of convolution kernel size is designed to perform multi-scale convolution inside the feature layer to form perceptual fields of different sizes. This module selects the appropriate convolution kernel size to extract lung nodule features in the face of different fine-grained lung nodules. Secondly, the Contextual Supplementary (CS) Block is designed to use the information of adjacent upper and lower layers to correct for the upper layer features, eliminating the discrepancy in the fusion of features at different levels. In this paper, the LUNA16 dataset was selected as the basis for lung nodule segmentation experiments. The method used in this dataset can achieve a iou of 80.59% and a DSC score of 89.25%. The network effectively improves the accuracy of lung nodule segmentation compared with other models. The results show that the method enhances the feature extraction ability of the network and improves the segmentation effect. In addition, the contribution of jump connections to information recovery should be noted.
{"title":"Pulmonary Nodule Segmentation Network Based On RkcU-Net","authors":"Yi Luo, Miao Cao, Xu Chang","doi":"10.1115/1.4065245","DOIUrl":"https://doi.org/10.1115/1.4065245","url":null,"abstract":"\u0000 U-Net network is widely used in the field of medical image segmentation. The automatic segmentation and detection of lung nodules can help in the early detection of lung cancer. Therefore, in this paper, to solve the problems of small proportion of nodules in CT images, complex features and insufficient segmentation accuracy, an improved U-Net network based on residual network and attention mechanism was proposed. The feature extraction part of RkcU-Net network is based on Res2net, a variant of Resnet, and on which a feature extraction module with automatic selection of convolution kernel size is designed to perform multi-scale convolution inside the feature layer to form perceptual fields of different sizes. This module selects the appropriate convolution kernel size to extract lung nodule features in the face of different fine-grained lung nodules. Secondly, the Contextual Supplementary (CS) Block is designed to use the information of adjacent upper and lower layers to correct for the upper layer features, eliminating the discrepancy in the fusion of features at different levels. In this paper, the LUNA16 dataset was selected as the basis for lung nodule segmentation experiments. The method used in this dataset can achieve a iou of 80.59% and a DSC score of 89.25%. The network effectively improves the accuracy of lung nodule segmentation compared with other models. The results show that the method enhances the feature extraction ability of the network and improves the segmentation effect. In addition, the contribution of jump connections to information recovery should be noted.","PeriodicalId":73734,"journal":{"name":"Journal of engineering and science in medical diagnostics and therapy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140748414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sheridan Perry, Matthew Folkman, Takara O’Brien, Lauren Wilson, Eric Coyle, Raymond W. Liu, Charles T. Price, Victor Huayamave
Developmental dysplasia of the hip (DDH) is a condition in which the acetabular socket inadequately contains the femoral head. If left untreated, DDH can result in degenerative changes in the hip joint. Several imaging techniques are used for DDH assessment. In radiographs, the acetabular index, center-edge angle, Sharp's angle, and migration percentage metrics are used to assess DDH. Determining these metrics is time-consuming and repetitive. This study uses a convolutional neural network (CNN) to identify radiographic measurements and improve traditional methods of identifying DDH. The dataset consisted of 60 subject radiographs rotated along the craniocaudal and mediolateral axes 25 times, generating 1500 images. A CNN detection algorithm was used to identify key radiographic metrics for the diagnosis of DDH. The algorithm was able to detect the metrics with reasonable accuracy in comparison to the manually computed metrics. The CNN performed well on images with high contrast margins between bone and soft tissues. In comparison, the CNN was not able to identify some critical points for metric calculation on a few images that had poor definition due to low contrast between bone and soft tissues. This study shows that CNNs can efficiently measure clinical parameters to assess DDH on radiographs with high contrast margins between bone and soft tissues with purposeful rotation away from an ideal image. Results from this study could help inform and broaden the existing bank of information on using CNNs for radiographic measurement and medical condition prediction.
{"title":"Unaligned Hip Radiograph Assessment Utilizing Convolutional Neural Networks for the Assessment of Developmental Dysplasia of the Hip","authors":"Sheridan Perry, Matthew Folkman, Takara O’Brien, Lauren Wilson, Eric Coyle, Raymond W. Liu, Charles T. Price, Victor Huayamave","doi":"10.1115/1.4064988","DOIUrl":"https://doi.org/10.1115/1.4064988","url":null,"abstract":"\u0000 Developmental dysplasia of the hip (DDH) is a condition in which the acetabular socket inadequately contains the femoral head. If left untreated, DDH can result in degenerative changes in the hip joint. Several imaging techniques are used for DDH assessment. In radiographs, the acetabular index, center-edge angle, Sharp's angle, and migration percentage metrics are used to assess DDH. Determining these metrics is time-consuming and repetitive. This study uses a convolutional neural network (CNN) to identify radiographic measurements and improve traditional methods of identifying DDH. The dataset consisted of 60 subject radiographs rotated along the craniocaudal and mediolateral axes 25 times, generating 1500 images. A CNN detection algorithm was used to identify key radiographic metrics for the diagnosis of DDH. The algorithm was able to detect the metrics with reasonable accuracy in comparison to the manually computed metrics. The CNN performed well on images with high contrast margins between bone and soft tissues. In comparison, the CNN was not able to identify some critical points for metric calculation on a few images that had poor definition due to low contrast between bone and soft tissues. This study shows that CNNs can efficiently measure clinical parameters to assess DDH on radiographs with high contrast margins between bone and soft tissues with purposeful rotation away from an ideal image. Results from this study could help inform and broaden the existing bank of information on using CNNs for radiographic measurement and medical condition prediction.","PeriodicalId":73734,"journal":{"name":"Journal of engineering and science in medical diagnostics and therapy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140085073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cavitation histotripsy has been applied to the disintegration on the surface of soft tissue in a well-controlled manner. Its performance was assumed to be determined by the acoustic pressure alone. Long pulse duration with low pulse repetition frequency (PRF) can also successfully generate erosion. This study was designed to investigate the excitation strategies for cavitation histotripsy-induced erosion. The erosion area and volumes produced by cavitation histotripsy on the alginate gel phantom using single-frequency, dual-frequency, and two pulsed excitations at the same power output at the PRF of 1 Hz and 200 Hz were compared. Dual-frequency excitation can improve the erosion at all PRFs, while pulsed excitations decrease it at the PRF of 200 Hz. Using both pulsed and dual-frequency excitations has more erosion areas than using single-frequency at a PRF of 1 Hz. In comparison, although the induced erosion areas using the pulsed excitations are larger than those of single-frequency at the PRF of 200 Hz, the erosion volumes are much lower than that of dual-frequency excitation. It suggests that a sufficient long pulse duration is another important factor for the performance of cavitation histotripsy. Dual-frequency excitation or amplitude modulation by the low-frequency sinusoidal envelope can achieve more erosion than that produced by single-frequency excitation at the same power output in a wide range of PRFs.
{"title":"Experimental Investigation of Excitation Strategies for Erosion by Cavitation Histotripsy","authors":"Yufeng Zhou","doi":"10.1115/1.4064769","DOIUrl":"https://doi.org/10.1115/1.4064769","url":null,"abstract":"\u0000 Cavitation histotripsy has been applied to the disintegration on the surface of soft tissue in a well-controlled manner. Its performance was assumed to be determined by the acoustic pressure alone. Long pulse duration with low pulse repetition frequency (PRF) can also successfully generate erosion. This study was designed to investigate the excitation strategies for cavitation histotripsy-induced erosion. The erosion area and volumes produced by cavitation histotripsy on the alginate gel phantom using single-frequency, dual-frequency, and two pulsed excitations at the same power output at the PRF of 1 Hz and 200 Hz were compared. Dual-frequency excitation can improve the erosion at all PRFs, while pulsed excitations decrease it at the PRF of 200 Hz. Using both pulsed and dual-frequency excitations has more erosion areas than using single-frequency at a PRF of 1 Hz. In comparison, although the induced erosion areas using the pulsed excitations are larger than those of single-frequency at the PRF of 200 Hz, the erosion volumes are much lower than that of dual-frequency excitation. It suggests that a sufficient long pulse duration is another important factor for the performance of cavitation histotripsy. Dual-frequency excitation or amplitude modulation by the low-frequency sinusoidal envelope can achieve more erosion than that produced by single-frequency excitation at the same power output in a wide range of PRFs.","PeriodicalId":73734,"journal":{"name":"Journal of engineering and science in medical diagnostics and therapy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139958911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maggie Oliver, Senthil Kumar, Gregory F. Petroski, Noah Manring
This paper demonstrates a new method for assessing total cardiovascular stiffness using the following five hemodynamic parameters gathered during a routine echocardiogram: (1) left ventricular stroke volume, (2) left ventricular ejection period, (3) heart rate, (4) systolic blood pressure, and (5) diastolic blood pressure. This study uses eight volunteer patients undergoing a routine echocardiogram at the University of Missouri Hospitals. Pulse wave velocity (PWV) data was taken immediately after the echocardiogram and compared to the cardiovascular stiffness result obtained from the echocardiogram data. The R2 value for this comparison was 0.8499 which shows a good correlation. We hypothesize that our new method for assessing total cardiovascular stiffness may be considered equivalent to that of the PWV method.
{"title":"A New Method for Assessing Total Cardiovascular Stiffness—Preliminary Data","authors":"Maggie Oliver, Senthil Kumar, Gregory F. Petroski, Noah Manring","doi":"10.1115/1.4064287","DOIUrl":"https://doi.org/10.1115/1.4064287","url":null,"abstract":"\u0000 This paper demonstrates a new method for assessing total cardiovascular stiffness using the following five hemodynamic parameters gathered during a routine echocardiogram: (1) left ventricular stroke volume, (2) left ventricular ejection period, (3) heart rate, (4) systolic blood pressure, and (5) diastolic blood pressure. This study uses eight volunteer patients undergoing a routine echocardiogram at the University of Missouri Hospitals. Pulse wave velocity (PWV) data was taken immediately after the echocardiogram and compared to the cardiovascular stiffness result obtained from the echocardiogram data. The R2 value for this comparison was 0.8499 which shows a good correlation. We hypothesize that our new method for assessing total cardiovascular stiffness may be considered equivalent to that of the PWV method.","PeriodicalId":73734,"journal":{"name":"Journal of engineering and science in medical diagnostics and therapy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139847853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gurmanik Kaur Mann, R. Busi, Satyanarayana Talam, Krishna Marlapalli
One of the prevalent, life-threatening disorders that have been on the rise in recent years is thyroid nodule. A frequent diagnostic technique for locating and identifying thyroid nodules is ultrasound imaging. However, it takes time and presents difficulties for the specialists to evaluate all of the slide images. Automated, reliable, and objective methods are required for accurately evaluating ultrasound images. Recent developments in deep learning have completely changed several facets of image analysis and computer-aided diagnostic (CAD) techniques that deal with the issue of identifying thyroid nodules. We reviewed the literature on the potential, constraints, and present applications of deep learning in thyroid cancer imaging and discussed the study's goals. We provided an overview of latest developments in the diagnosis of thyroid cancer using deep learning techniques and addressed about numerous difficulties and practical issues that can restrict the development of deep learning and its incorporation into healthcare setting.
{"title":"Deep Learning Methods for Diagnosing Thyroid Cancer","authors":"Gurmanik Kaur Mann, R. Busi, Satyanarayana Talam, Krishna Marlapalli","doi":"10.1115/1.4064705","DOIUrl":"https://doi.org/10.1115/1.4064705","url":null,"abstract":"\u0000 One of the prevalent, life-threatening disorders that have been on the rise in recent years is thyroid nodule. A frequent diagnostic technique for locating and identifying thyroid nodules is ultrasound imaging. However, it takes time and presents difficulties for the specialists to evaluate all of the slide images. Automated, reliable, and objective methods are required for accurately evaluating ultrasound images. Recent developments in deep learning have completely changed several facets of image analysis and computer-aided diagnostic (CAD) techniques that deal with the issue of identifying thyroid nodules. We reviewed the literature on the potential, constraints, and present applications of deep learning in thyroid cancer imaging and discussed the study's goals. We provided an overview of latest developments in the diagnosis of thyroid cancer using deep learning techniques and addressed about numerous difficulties and practical issues that can restrict the development of deep learning and its incorporation into healthcare setting.","PeriodicalId":73734,"journal":{"name":"Journal of engineering and science in medical diagnostics and therapy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139790029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gurmanik Kaur Mann, R. Busi, Satyanarayana Talam, Krishna Marlapalli
One of the prevalent, life-threatening disorders that have been on the rise in recent years is thyroid nodule. A frequent diagnostic technique for locating and identifying thyroid nodules is ultrasound imaging. However, it takes time and presents difficulties for the specialists to evaluate all of the slide images. Automated, reliable, and objective methods are required for accurately evaluating ultrasound images. Recent developments in deep learning have completely changed several facets of image analysis and computer-aided diagnostic (CAD) techniques that deal with the issue of identifying thyroid nodules. We reviewed the literature on the potential, constraints, and present applications of deep learning in thyroid cancer imaging and discussed the study's goals. We provided an overview of latest developments in the diagnosis of thyroid cancer using deep learning techniques and addressed about numerous difficulties and practical issues that can restrict the development of deep learning and its incorporation into healthcare setting.
{"title":"Deep Learning Methods for Diagnosing Thyroid Cancer","authors":"Gurmanik Kaur Mann, R. Busi, Satyanarayana Talam, Krishna Marlapalli","doi":"10.1115/1.4064705","DOIUrl":"https://doi.org/10.1115/1.4064705","url":null,"abstract":"\u0000 One of the prevalent, life-threatening disorders that have been on the rise in recent years is thyroid nodule. A frequent diagnostic technique for locating and identifying thyroid nodules is ultrasound imaging. However, it takes time and presents difficulties for the specialists to evaluate all of the slide images. Automated, reliable, and objective methods are required for accurately evaluating ultrasound images. Recent developments in deep learning have completely changed several facets of image analysis and computer-aided diagnostic (CAD) techniques that deal with the issue of identifying thyroid nodules. We reviewed the literature on the potential, constraints, and present applications of deep learning in thyroid cancer imaging and discussed the study's goals. We provided an overview of latest developments in the diagnosis of thyroid cancer using deep learning techniques and addressed about numerous difficulties and practical issues that can restrict the development of deep learning and its incorporation into healthcare setting.","PeriodicalId":73734,"journal":{"name":"Journal of engineering and science in medical diagnostics and therapy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139849780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maggie Oliver, Senthil Kumar, Gregory F. Petroski, Noah Manring
This paper demonstrates a new method for assessing total cardiovascular stiffness using the following five hemodynamic parameters gathered during a routine echocardiogram: (1) left ventricular stroke volume, (2) left ventricular ejection period, (3) heart rate, (4) systolic blood pressure, and (5) diastolic blood pressure. This study uses eight volunteer patients undergoing a routine echocardiogram at the University of Missouri Hospitals. Pulse wave velocity (PWV) data was taken immediately after the echocardiogram and compared to the cardiovascular stiffness result obtained from the echocardiogram data. The R2 value for this comparison was 0.8499 which shows a good correlation. We hypothesize that our new method for assessing total cardiovascular stiffness may be considered equivalent to that of the PWV method.
{"title":"A New Method for Assessing Total Cardiovascular Stiffness—Preliminary Data","authors":"Maggie Oliver, Senthil Kumar, Gregory F. Petroski, Noah Manring","doi":"10.1115/1.4064287","DOIUrl":"https://doi.org/10.1115/1.4064287","url":null,"abstract":"\u0000 This paper demonstrates a new method for assessing total cardiovascular stiffness using the following five hemodynamic parameters gathered during a routine echocardiogram: (1) left ventricular stroke volume, (2) left ventricular ejection period, (3) heart rate, (4) systolic blood pressure, and (5) diastolic blood pressure. This study uses eight volunteer patients undergoing a routine echocardiogram at the University of Missouri Hospitals. Pulse wave velocity (PWV) data was taken immediately after the echocardiogram and compared to the cardiovascular stiffness result obtained from the echocardiogram data. The R2 value for this comparison was 0.8499 which shows a good correlation. We hypothesize that our new method for assessing total cardiovascular stiffness may be considered equivalent to that of the PWV method.","PeriodicalId":73734,"journal":{"name":"Journal of engineering and science in medical diagnostics and therapy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139787970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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