Pub Date : 2022-11-10DOI: 10.1109/bmeicon56653.2022.10012106
{"title":"Technical Program and Abstract","authors":"","doi":"10.1109/bmeicon56653.2022.10012106","DOIUrl":"https://doi.org/10.1109/bmeicon56653.2022.10012106","url":null,"abstract":"","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"202 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123037737","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}
Pub Date : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10012098
Shen Feng, Han Zhang, Andong Bao, Pengtao Sun, Xiaomu Luo, Guanyang Lin, Huan Cen, Sinan Chen, Yuexia Liu, Wenning He, Zhiqiang Pang
Purpose: To enable the in-home diagnosis of heart failure (HF) based on morphological features of high quality ballistocardiography (BCG) signals and respiratory effort. Methods: Non-contact vital signs including BCG and respiratory effort signals from 25 subjects (11 HF, 14 non-heart failure (Non-HF)) were collected using a force sensor-based medical equipment. By assessing the recorded BCG signals w.r.t signal quality indexes, a steady-state BCG template is modeled by using consecutive high quality BCG signals, from which morphological features including the amplitude, time, area and energy features of signal wave groups are extracted to distinguish the HF and Non-HF subjects. Results: It is validated that a total 13 morphological features of BCG and respiratory effort signals showed differences between HF and Non-HF subjects. Using typical classifiers for discriminating HF and Non-HF subjects yields the accuracy, sensitivity and specificity of 92%, 80% and 100%. Conclusion: The acquisition and analysis of high quality BCG signals has the potential of identifying HF disease.
{"title":"Diagnosis of Heart Failure using High Quality Ballistocardiography and Respiratory Effort Signals: A Pilot Study","authors":"Shen Feng, Han Zhang, Andong Bao, Pengtao Sun, Xiaomu Luo, Guanyang Lin, Huan Cen, Sinan Chen, Yuexia Liu, Wenning He, Zhiqiang Pang","doi":"10.1109/BMEiCON56653.2022.10012098","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012098","url":null,"abstract":"Purpose: To enable the in-home diagnosis of heart failure (HF) based on morphological features of high quality ballistocardiography (BCG) signals and respiratory effort. Methods: Non-contact vital signs including BCG and respiratory effort signals from 25 subjects (11 HF, 14 non-heart failure (Non-HF)) were collected using a force sensor-based medical equipment. By assessing the recorded BCG signals w.r.t signal quality indexes, a steady-state BCG template is modeled by using consecutive high quality BCG signals, from which morphological features including the amplitude, time, area and energy features of signal wave groups are extracted to distinguish the HF and Non-HF subjects. Results: It is validated that a total 13 morphological features of BCG and respiratory effort signals showed differences between HF and Non-HF subjects. Using typical classifiers for discriminating HF and Non-HF subjects yields the accuracy, sensitivity and specificity of 92%, 80% and 100%. Conclusion: The acquisition and analysis of high quality BCG signals has the potential of identifying HF disease.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130569900","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}
Pub Date : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10012104
Rujipas Janthraprasert, C. Pintavirooj
A computerized tomography scan (CT scan) is a device that uses X-ray machines and computers that will compute the data gathered from patients to create cross-sectional images. A CT scan can be used to view the inside without cutting the body open, such as blood vessels and different organs. However, most CT scan is mainly located at the hospital because it is very expensive and must be supervised at all times. So, it is hard for students to learn and understand the mechanic behind the actual CT scan. In this paper, we will build a simulated CT that can be used for educational purposes by creating an embedded photographic tomography using raspberry pi. The proposed system is capable of successfully creating a 3D model of the test object.
{"title":"Embedded Photographic Tomography Using Raspberry Pi","authors":"Rujipas Janthraprasert, C. Pintavirooj","doi":"10.1109/BMEiCON56653.2022.10012104","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012104","url":null,"abstract":"A computerized tomography scan (CT scan) is a device that uses X-ray machines and computers that will compute the data gathered from patients to create cross-sectional images. A CT scan can be used to view the inside without cutting the body open, such as blood vessels and different organs. However, most CT scan is mainly located at the hospital because it is very expensive and must be supervised at all times. So, it is hard for students to learn and understand the mechanic behind the actual CT scan. In this paper, we will build a simulated CT that can be used for educational purposes by creating an embedded photographic tomography using raspberry pi. The proposed system is capable of successfully creating a 3D model of the test object.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124169534","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}
Pub Date : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10012078
Patcharapon Kangwarnchokchai, B. Techaumnat, N. Nuntawong, K. Mishima, T. Sharmin, Takuso Aida
This paper presents the electroporation of canine MCT cells by a low voltage in microfluidic system. We examined the electroporation efficiency with pulse conditions and correlated the results from the impedance measurement to the membrane openings. Dielectrophoretic force was applied to position the cell at a desired location. Temporary and permanent electroporation cells was discriminated by using a combination of Yo-Pro-1 and propidium iodide (PI) fluorescent dyes. From the experiment, we determined the appropriate condition for the reversible electroporation of the canine MCT cells to be 15 sets of $2.5 mathrm{V}_{p}$, 20kHz frequency, and 50-cycle pulses. The condition yielded 50% efficiency for the reversible electroporation. In addition, the area of the cell-membrane pores could be quantitatively examined from the conductance measured without a cell and those with a cell before and after applying electroporation pulses.
{"title":"Electroporation of Canine MCT Cells and the Examination by Impedance Measurement","authors":"Patcharapon Kangwarnchokchai, B. Techaumnat, N. Nuntawong, K. Mishima, T. Sharmin, Takuso Aida","doi":"10.1109/BMEiCON56653.2022.10012078","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012078","url":null,"abstract":"This paper presents the electroporation of canine MCT cells by a low voltage in microfluidic system. We examined the electroporation efficiency with pulse conditions and correlated the results from the impedance measurement to the membrane openings. Dielectrophoretic force was applied to position the cell at a desired location. Temporary and permanent electroporation cells was discriminated by using a combination of Yo-Pro-1 and propidium iodide (PI) fluorescent dyes. From the experiment, we determined the appropriate condition for the reversible electroporation of the canine MCT cells to be 15 sets of $2.5 mathrm{V}_{p}$, 20kHz frequency, and 50-cycle pulses. The condition yielded 50% efficiency for the reversible electroporation. In addition, the area of the cell-membrane pores could be quantitatively examined from the conductance measured without a cell and those with a cell before and after applying electroporation pulses.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132261301","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}
Pub Date : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10011583
Niti Petranon, Naphatsawan Vongmanee, Nutthanan Wanluk, C. Pintavirooj
Infusion Therapy is one of the main treatments today. The Infusion Therapy can be faulty, causing the solution to leak into the surrounding area and potentially damaging the surrounding tissue, known as extravasation. In clinical practice, the medical staff is responsible for checking the status of the intravenous solution. But the leakage of the intravenous solution is difficult to detect with the naked eye. in this study We therefore offer a device for detecting intravenous fluid leaks using temperature detection. and designed the device to look like a wristwatch to reduce the worry of patients wearing it. The device simulates the occurrence of intravenous fluid leaks to simulate the occurrence of Extravasation We also use IOT Monitoring using the Blynk Platform as a model to record and assist medical staff in early detection of intravenous fluid leaks to prevent potential hazards.
{"title":"Prototype – Wearable Device for Detecting Extravasation Using Temperature Sensor and IOT Monitoring System","authors":"Niti Petranon, Naphatsawan Vongmanee, Nutthanan Wanluk, C. Pintavirooj","doi":"10.1109/BMEiCON56653.2022.10011583","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10011583","url":null,"abstract":"Infusion Therapy is one of the main treatments today. The Infusion Therapy can be faulty, causing the solution to leak into the surrounding area and potentially damaging the surrounding tissue, known as extravasation. In clinical practice, the medical staff is responsible for checking the status of the intravenous solution. But the leakage of the intravenous solution is difficult to detect with the naked eye. in this study We therefore offer a device for detecting intravenous fluid leaks using temperature detection. and designed the device to look like a wristwatch to reduce the worry of patients wearing it. The device simulates the occurrence of intravenous fluid leaks to simulate the occurrence of Extravasation We also use IOT Monitoring using the Blynk Platform as a model to record and assist medical staff in early detection of intravenous fluid leaks to prevent potential hazards.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125434285","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}
Skin cancer is the most frequent malignancy worldwide, with the number of new cases increasing yearly. Computer-aided diagnosis from skin images has recently become a critical procedure to detect early melanoma stages before becoming metastasis. This study intended to classify three stages of skin cancer: solar lentigo (SL), lentigo maligna (LM), and lentigo maligna melanoma (LMM) using transfer learning and semi-supervised transfer learning of a convolutional neural network algorithm based on VGG-16 and VGG-19. Skin images were obtained from various databases, including labeled and unlabeled data, and were preprocessed using hair removal software and a data balancing technique. The image data were then trained in ten experiments: supervised learning, supervised transfer learning, and semi-supervised transfer learning using VGG-16 and VGG-19 with and without augmentation. The results show that supervised learning gives an accuracy of 0.47. Based on VGG-16 and VGG19 which are comparable in performance, the accuracies increase to 0.72 and 0.72 for supervised transfer learning, and 0.92 and 0.98 for semi-supervised transfer learning, respectively. However, when applying augmentation, the accuracies decrease. Therefore, the use of semi-supervised transfer learning based on VGG-19 gives the best prediction in our study.
{"title":"Using Semi-supervised Transfer Learning for Classification of Solar Lentigo, Lentigo Maligna, and Lentigo Maligna Melanoma","authors":"Nattapong Thungprue, Nathakorn Tamronganunsakul, Manasanun Hongchukiat, Kanes Sumetpipat, Tanawan Leeboonngam","doi":"10.1109/BMEiCON56653.2022.10011586","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10011586","url":null,"abstract":"Skin cancer is the most frequent malignancy worldwide, with the number of new cases increasing yearly. Computer-aided diagnosis from skin images has recently become a critical procedure to detect early melanoma stages before becoming metastasis. This study intended to classify three stages of skin cancer: solar lentigo (SL), lentigo maligna (LM), and lentigo maligna melanoma (LMM) using transfer learning and semi-supervised transfer learning of a convolutional neural network algorithm based on VGG-16 and VGG-19. Skin images were obtained from various databases, including labeled and unlabeled data, and were preprocessed using hair removal software and a data balancing technique. The image data were then trained in ten experiments: supervised learning, supervised transfer learning, and semi-supervised transfer learning using VGG-16 and VGG-19 with and without augmentation. The results show that supervised learning gives an accuracy of 0.47. Based on VGG-16 and VGG19 which are comparable in performance, the accuracies increase to 0.72 and 0.72 for supervised transfer learning, and 0.92 and 0.98 for semi-supervised transfer learning, respectively. However, when applying augmentation, the accuracies decrease. Therefore, the use of semi-supervised transfer learning based on VGG-19 gives the best prediction in our study.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123242051","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}
Pub Date : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10012070
Miss Kamonchat Apivanichkul, P. Phasukkit, Dankulchai Pittaya
This paper proposed to insert additional feature into input datasets (i.e., CT scans) for automatic femur segmentation model, U-Net, with respect to increase the accuracy of model performance. An additional feature is available reference information representing identity on each CT scans and has an effect on results of deep learning model training. In this experiment, choose the left-femur as the target organ, which is common organs-at-risk (OARs) for lower abdominal cancers. The automatic femur segmentation model training was separately executed through two different datasets, one cropped-dataset with additional feature and one original dimension dataset without additional feature. For additional feature, lying posture of patient when entered the CT scanner was selected. The performance results of both trained U-Net models were compered in order to observe the difference of effect. Evaluation results reported that the additional feature could increase an accuracy and precision including support prediction for the left-femur segmentation, with the Dice Similarity Coefficient (DSC) of 61.573% and Intersection Over Union (IoU) of 45.621%, respectively. Specifically, deep learning combining additional feature insertion on cropped-datasets was the novelty in this experiment to effectively segment the left femur.
本文提出在自动股骨分割模型U-Net的输入数据集(即CT扫描)中插入额外的特征，以提高模型性能的准确性。另一个特征是每次CT扫描上可用的代表身份的参考信息，并对深度学习模型训练的结果产生影响。本实验选择左侧股骨作为靶器官，左侧股骨是下腹部肿瘤常见的高危器官。通过两个不同的数据集分别进行自动股骨分割模型训练，一个是带有附加特征的裁剪数据集，另一个是没有附加特征的原始维度数据集。附加特征选择患者进入CT扫描仪时的躺姿。比较了两种训练后的U-Net模型的性能结果，以观察效果的差异。评价结果表明，该附加特征可以提高左股骨分割的准确度和精度，包括支持预测，Dice相似系数(DSC)为61.573%，Intersection Over Union (IoU)为45.621%。具体来说，在裁剪数据集上结合附加特征插入的深度学习是本实验的新颖之处，可以有效地分割左股骨。
{"title":"CT Dataset Enhancement using Additional Feature Insertion for Automatic Femur Segmentation Model Based on Deep Learning","authors":"Miss Kamonchat Apivanichkul, P. Phasukkit, Dankulchai Pittaya","doi":"10.1109/BMEiCON56653.2022.10012070","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012070","url":null,"abstract":"This paper proposed to insert additional feature into input datasets (i.e., CT scans) for automatic femur segmentation model, U-Net, with respect to increase the accuracy of model performance. An additional feature is available reference information representing identity on each CT scans and has an effect on results of deep learning model training. In this experiment, choose the left-femur as the target organ, which is common organs-at-risk (OARs) for lower abdominal cancers. The automatic femur segmentation model training was separately executed through two different datasets, one cropped-dataset with additional feature and one original dimension dataset without additional feature. For additional feature, lying posture of patient when entered the CT scanner was selected. The performance results of both trained U-Net models were compered in order to observe the difference of effect. Evaluation results reported that the additional feature could increase an accuracy and precision including support prediction for the left-femur segmentation, with the Dice Similarity Coefficient (DSC) of 61.573% and Intersection Over Union (IoU) of 45.621%, respectively. Specifically, deep learning combining additional feature insertion on cropped-datasets was the novelty in this experiment to effectively segment the left femur.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121184727","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}
Pub Date : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10012105
Siratchakrit Shinnakerdchoke, Kitsada Thadson, Suejit Pechprasarn, T. Treebupachatsakul
Surface plasmon resonance (SPR) paves the way for several cutting-edge sensing technologies well-known for being label-free and real-time monitoring. The angular scanning technique, one of the most common SPR applications, was performed by illuminating the SPR-based sensor with multiple incident angles of a single-wavelength laser beam. For refractive index sensing, the optical reflectance is absorbed in a specific angle, known as a plasmonic angle, which can be observed as a dark band when captured using a camera. Various methods have been proposed to locate the plasmonic position based on the detected image. This manuscript presented an analysis of the performance of machine learning on the identification of plasmonic angles based on the reflectance spectra for refractive index sensing. The reflectance curves are generated using Fresnel equations and the transfer matrix method with shot noise. After training and validating, the rational quadratic gaussian process regression model provides the most accurate model for predicting the plasmonic angle positions. The model can predict the plasmonic angles accurately for all studied refractive indices with a root mean square error of $3.83 times 10^{mathbf{-4}}$ RIU. Furthermore, the analysis of noise performance illustrated that a low number of photons could significantly degrade the model’s accuracy and precision. The theoretical performance can be achieved at the photon energy level of 8.14 pJ.
{"title":"Performance Analysis of Machine Learning Models for Angular Interrogation of Surface Plasmon Resonance","authors":"Siratchakrit Shinnakerdchoke, Kitsada Thadson, Suejit Pechprasarn, T. Treebupachatsakul","doi":"10.1109/BMEiCON56653.2022.10012105","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012105","url":null,"abstract":"Surface plasmon resonance (SPR) paves the way for several cutting-edge sensing technologies well-known for being label-free and real-time monitoring. The angular scanning technique, one of the most common SPR applications, was performed by illuminating the SPR-based sensor with multiple incident angles of a single-wavelength laser beam. For refractive index sensing, the optical reflectance is absorbed in a specific angle, known as a plasmonic angle, which can be observed as a dark band when captured using a camera. Various methods have been proposed to locate the plasmonic position based on the detected image. This manuscript presented an analysis of the performance of machine learning on the identification of plasmonic angles based on the reflectance spectra for refractive index sensing. The reflectance curves are generated using Fresnel equations and the transfer matrix method with shot noise. After training and validating, the rational quadratic gaussian process regression model provides the most accurate model for predicting the plasmonic angle positions. The model can predict the plasmonic angles accurately for all studied refractive indices with a root mean square error of $3.83 times 10^{mathbf{-4}}$ RIU. Furthermore, the analysis of noise performance illustrated that a low number of photons could significantly degrade the model’s accuracy and precision. The theoretical performance can be achieved at the photon energy level of 8.14 pJ.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125628540","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}
Pub Date : 2022-11-10DOI: 10.1109/BMEiCON56653.2022.10012085
Netnapa Sittihakote, Sirirat Anutrakulchai, A. Tuantranont, Pobporn Danvirutai, Chavis Srichan
Acute kidney injury (AKI) is not a specified symptom in the early stages. Frequency of AKI occurrence is highly correlated to Chronic Kidney Disease (CKD). Therefore, development of non-invasive, ultra-sensitive, and highly accurate sensing platform is crucial for early AKI diagnosis. Serum creatinine (SCr) level usually takes 24-72 hours to response to the incident of AKI. Meanwhile, urine Neutrophil Gelatinase-Associated Lipocalin (NGAL) takes only 2 hours to response after the AKI occurrence. In this work, we investigated the use of microporous graphene and dipole-dipole enhancement between graphene/nickel layers to enhance electrode sensitivity for urine NGAL level determination. Selectivity was assured using enzymatic electrochemistry. Once NGAL level was measured, a doctor can diagnose AKI under additional information on patient’s conditions. The result is promising since the detection range was 0.110 to 93.9 ng/ml and the correlation coefficient is 0.8235. The detection covered AKI primary diagnostic cutoff level at 87 ng/ml in urine. The electrochemical immunosensor was able to determine NGAL in Urine with results compared to those provided by the standard ELISA method. This work is a part of development of handheld NGAL determination strip in human urine samples and prepared portable NGAL sensing devices. Despite our investigation’s limitation, the acquired data indicates that non-invasive acute kidney injury detection using actual human urine with graphene foam/nickel-based electrochemical sensor should be further explored as an auxiliary diagnostic tool for AKI.
{"title":"Acute Kidney Injury Detection using Real Human Urine NGAL Biomarker Sensor based on 3D Graphene","authors":"Netnapa Sittihakote, Sirirat Anutrakulchai, A. Tuantranont, Pobporn Danvirutai, Chavis Srichan","doi":"10.1109/BMEiCON56653.2022.10012085","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012085","url":null,"abstract":"Acute kidney injury (AKI) is not a specified symptom in the early stages. Frequency of AKI occurrence is highly correlated to Chronic Kidney Disease (CKD). Therefore, development of non-invasive, ultra-sensitive, and highly accurate sensing platform is crucial for early AKI diagnosis. Serum creatinine (SCr) level usually takes 24-72 hours to response to the incident of AKI. Meanwhile, urine Neutrophil Gelatinase-Associated Lipocalin (NGAL) takes only 2 hours to response after the AKI occurrence. In this work, we investigated the use of microporous graphene and dipole-dipole enhancement between graphene/nickel layers to enhance electrode sensitivity for urine NGAL level determination. Selectivity was assured using enzymatic electrochemistry. Once NGAL level was measured, a doctor can diagnose AKI under additional information on patient’s conditions. The result is promising since the detection range was 0.110 to 93.9 ng/ml and the correlation coefficient is 0.8235. The detection covered AKI primary diagnostic cutoff level at 87 ng/ml in urine. The electrochemical immunosensor was able to determine NGAL in Urine with results compared to those provided by the standard ELISA method. This work is a part of development of handheld NGAL determination strip in human urine samples and prepared portable NGAL sensing devices. Despite our investigation’s limitation, the acquired data indicates that non-invasive acute kidney injury detection using actual human urine with graphene foam/nickel-based electrochemical sensor should be further explored as an auxiliary diagnostic tool for AKI.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134540731","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}
Recent studies have suggested that the boundary between data-driven deep-learning non-Cartesian magnetic resonance imaging (MRI) reconstruction methods and conventional optimization-based, iterative reconstruction methods is becoming blurred. For instance, the unrolled iterative reconstruction method can be regarded as a trainable neural network. Another example is that the Moore-Penrose pseudoinverse plays a central role in finding the predefined solution to many imaging processes. However, the application of pseudoinverse in MRI reconstruction was obstructed in clinical imaging, mostly due to the excessive storage required for singular vectors. Since the spatial encoding of MRI is fully determined by the known k-space trajectory, the generalized inverse can be ”iteratively learning in a data-free fashion”, which leads to surprising but realizable properties. To compare our method with other conventional methods, numerical simulations were performed using in vivo MRI. The proposed method leads to nearly equivalent image quality with a much shorter run-time (only 0.68%) than the conjugate gradient (CG) method. We discuss the potential impact of the generalized inverse as a feasible reconstruction method for non-Cartesian MRI.
{"title":"On the generalized inverse for MRI reconstruction","authors":"Tzu-Hsueh Tsai, Hsin-Chia Chen, Hao Yang, Yu-Chieh Chao, Jyh-Miin Lin, Chih-Ching Chen, Hing-Chiu Chang, Chin-Kuo Chang, Wei-Hsuan Yu, F. Hwang, M. Graves","doi":"10.1109/BMEiCON56653.2022.10012099","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012099","url":null,"abstract":"Recent studies have suggested that the boundary between data-driven deep-learning non-Cartesian magnetic resonance imaging (MRI) reconstruction methods and conventional optimization-based, iterative reconstruction methods is becoming blurred. For instance, the unrolled iterative reconstruction method can be regarded as a trainable neural network. Another example is that the Moore-Penrose pseudoinverse plays a central role in finding the predefined solution to many imaging processes. However, the application of pseudoinverse in MRI reconstruction was obstructed in clinical imaging, mostly due to the excessive storage required for singular vectors. Since the spatial encoding of MRI is fully determined by the known k-space trajectory, the generalized inverse can be ”iteratively learning in a data-free fashion”, which leads to surprising but realizable properties. To compare our method with other conventional methods, numerical simulations were performed using in vivo MRI. The proposed method leads to nearly equivalent image quality with a much shorter run-time (only 0.68%) than the conjugate gradient (CG) method. We discuss the potential impact of the generalized inverse as a feasible reconstruction method for non-Cartesian MRI.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114788689","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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