2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)最新文献_第7页

Image Based Virtual Reality Haptic Simulation for Multimodal Skin Tumor Surgery Training 基于图像的多模态皮肤肿瘤手术训练虚拟现实触觉仿真

2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART) Pub Date : 2021-12-08 DOI: 10.1109/BioSMART54244.2021.9677802

Jin Woo Kim, Hyunjae Jeong, Kwangtaek Kim, Dustin P. DeMeo, B. Carroll

{"title":"Image Based Virtual Reality Haptic Simulation for Multimodal Skin Tumor Surgery Training","authors":"Jin Woo Kim, Hyunjae Jeong, Kwangtaek Kim, Dustin P. DeMeo, B. Carroll","doi":"10.1109/BioSMART54244.2021.9677802","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677802","url":null,"abstract":"We present the Virtual Reality Haptic Surgery Platform (VRHSP), a multimodal haptic virtual reality training simulator for elliptical skin excisions (i.e., skin tumor surgeries). Using a haptic device and a head mounted display, participants interact with actual skin images mapped to a 3D simulated surgical suite. In this study, the primary aim is to build the VRHSP with an initial narrow focus of simulating the outlining and incision steps of skin tumor surgeries with realistic tactile and visual feedback collocated in a 3D clinical scene. The secondary aim is to investigate the effectiveness of the VRHSP's haptic feedback capability, which we hypothesized would play an important role because skin tumor surgery is a tactile skill. The results of user studies on non-medical and medical participants from Kent State University and University Hospitals Cleveland Medical Center, respectively. The qualitative results suggest that the VRHSP has potential for high adoption, especially with haptic feedback. The quantitative results demonstrate the VRHSP's ability to discern experts from non-experts. Finally, the improved performance of participants with feedback suggests that haptic feedback can be used as a teaching tool as well as a realism tool.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121218907","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}

引用次数: 2

Interpretable AI Model-Based Predictions of ECG changes in COVID-recovered patients 基于可解释AI模型的新冠肺炎康复患者心电图变化预测

2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART) Pub Date : 2021-12-08 DOI: 10.1109/BioSMART54244.2021.9677747

Anubha Gupta, Jayant Jain, Shubhankar Poundrik, M. Shetty, M. Girish, M. Gupta

{"title":"Interpretable AI Model-Based Predictions of ECG changes in COVID-recovered patients","authors":"Anubha Gupta, Jayant Jain, Shubhankar Poundrik, M. Shetty, M. Girish, M. Gupta","doi":"10.1109/BioSMART54244.2021.9677747","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677747","url":null,"abstract":"COVID-19 has caused immense social and economic losses throughout the world. Subjects recovered from COVID are learned to have complications. Some studies have shown a change in the heart rate variability (HRV) in COVID-recovered subjects compared to the healthy ones. This change indicates an increased risk of heart problems among the survivors of moderate-to-severe COVID. Hence, this study is aimed at finding HRV features that get altered in COVID-recovered subjects compared to healthy subjects. Data of COVID-recovered and healthy subjects were collected from two hospitals in Delhi, India. Seven ML models have been built to classify healthy versus COVID-recovered subjects. The best-performing model was further analyzed to explore the ranking of altered heart features in COVID-recovered subjects via AI interpretability. Ranking of these features can indicate cardiovascular health status to doctors, who can provide support to the COVID-recovered subjects for timely safeguard from heart disorders. To the best of our knowledge, this is the first study with an in-depth analysis of the heart status of COVID-recovered subjects via ECG analysis.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123387749","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}

引用次数: 3

Automated Cell Segmentation for Phase-Contrast Images of Adhesion Cell Culture 粘附细胞培养相衬图像的自动细胞分割

2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART) Pub Date : 2021-12-08 DOI: 10.1109/BioSMART54244.2021.9677717

Guochang Ye, Mehmet Kaya

引用次数: 0

Intracranial Pressure Prediction with a Recurrent Neural Network Model 用递归神经网络模型预测颅内压

2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART) Pub Date : 2021-12-08 DOI: 10.1109/BioSMART54244.2021.9677652

Guochang Ye, Vignesh Balasubramanian, J. Li, M. Kaya

引用次数: 2

PAANet: Progressive Alternating Attention for Automatic Medical Image Segmentation PAANet:渐进式交替关注自动医学图像分割

2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART) Pub Date : 2021-11-20 DOI: 10.1109/BioSMART54244.2021.9677844

Abhishek Srivastava, S. Chanda, Debesh Jha, M. Riegler, P. Halvorsen, Dag Johansen, U. Pal

{"title":"PAANet: Progressive Alternating Attention for Automatic Medical Image Segmentation","authors":"Abhishek Srivastava, S. Chanda, Debesh Jha, M. Riegler, P. Halvorsen, Dag Johansen, U. Pal","doi":"10.1109/BioSMART54244.2021.9677844","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677844","url":null,"abstract":"Medical image segmentation can provide detailed information for clinical analysis which can be useful for scenarios where the detailed location of a finding is important. Knowing the location of a disease can play a vital role in treatment and decision-making. Convolutional neural network (CNN) based encoder-decoder techniques have advanced the performance of automated medical image segmentation systems. Several such CNN-based methodologies utilize techniques such as spatial- and channel-wise attention to enhance performance. Another technique that has drawn attention in recent years is residual dense blocks (RDBs). The successive convolutional layers in densely connected blocks are capable of extracting diverse features with varied receptive fields and thus, enhancing performance. However, consecutive stacked convolutional operators may not necessarily generate features that facilitate the identification of the target structures. In this paper, we propose a progressive alternating attention network (PAANet). We develop progressive alternating attention dense (PAAD) blocks, which construct a guiding attention map (GAM) after every convolutional layer in the dense blocks using features from all scales. The GAM allows the following layers in the dense blocks to focus on the spatial locations relevant to the target region. Every alternate PAAD block inverts the GAM to generate a reverse attention map which guides ensuing layers to extract boundary and edge-related information, refining the segmentation process. Our experiments on three different biomedical image segmentation datasets exhibit that our PAANet achieves favorable performance when compared to other state-of-the-art methods.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128015927","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}

引用次数: 4

Skeleton-Split Framework using Spatial Temporal Graph Convolutional Networks for Action Recognition 基于时空图卷积网络的动作识别骨架分割框架

2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART) Pub Date : 2021-11-04 DOI: 10.1109/BioSMART54244.2021.9677634

Motasem S. Alsawadi, Miguel Rio

引用次数: 3