Multimodal learning for clinical decision support and clinical image-based procedures : 10th International Workshop, ML-CDS 2020, and 9th International Workshop, CLIP 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, ...最新文献
Pub Date : 2020-10-01DOI: 10.1007/978-3-030-60946-7_5
Jonas Bianchi, Beatriz Paniagua, Antonio Carlos De Oliveira Ruellas, Jean-Christophe Fillion-Robin, Juan C Prietro, João Roberto Gonçalves, James Hoctor, Marília Yatabe, Martin Styner, TengFei Li, Marcela Lima Gurgel, Cauby Maia Chaves, Camila Massaro, Daniela Gamba Garib, Lorena Vilanova, Jose Fernando Castanha Henriques, Aron Aliaga-Del Castillo, Guilherme Janson, Laura R Iwasaki, Jeffrey C Nickel, Karine Evangelista, Lucia Cevidanes
The biggest challenge to improve the diagnosis and therapies of Craniomaxillofacial conditions is to translate algorithms and software developments towards the creation of holistic patient models. A complete picture of the individual patient for treatment planning and personalized healthcare requires a compilation of clinician-friendly algorithms to provide minimally invasive diagnostic techniques with multimodal image integration and analysis. We describe here the implementation of the open-source Craniomaxillofacial module of the 3D Slicer software, as well as its clinical applications. This paper proposes data management approaches for multisource data extraction, registration, visualization, and quantification. These applications integrate medical images with clinical and biological data analytics, user studies, and other heterogeneous data.
要改进颅颌面疾病的诊断和治疗，最大的挑战是将算法和软件开发转化为创建患者整体模型。要为治疗计划和个性化医疗保健提供完整的患者个人图像，需要汇集便于临床医生使用的算法，以提供具有多模态图像集成和分析功能的微创诊断技术。我们在此介绍开源 3D Slicer 软件颅颌面模块的实施及其临床应用。本文提出了用于多源数据提取、配准、可视化和量化的数据管理方法。这些应用将医学图像与临床和生物数据分析、用户研究以及其他异构数据整合在一起。
{"title":"3D Slicer Craniomaxillofacial Modules Support Patient-Specific Decision-Making for Personalized Healthcare in Dental Research.","authors":"Jonas Bianchi, Beatriz Paniagua, Antonio Carlos De Oliveira Ruellas, Jean-Christophe Fillion-Robin, Juan C Prietro, João Roberto Gonçalves, James Hoctor, Marília Yatabe, Martin Styner, TengFei Li, Marcela Lima Gurgel, Cauby Maia Chaves, Camila Massaro, Daniela Gamba Garib, Lorena Vilanova, Jose Fernando Castanha Henriques, Aron Aliaga-Del Castillo, Guilherme Janson, Laura R Iwasaki, Jeffrey C Nickel, Karine Evangelista, Lucia Cevidanes","doi":"10.1007/978-3-030-60946-7_5","DOIUrl":"10.1007/978-3-030-60946-7_5","url":null,"abstract":"<p><p>The biggest challenge to improve the diagnosis and therapies of Craniomaxillofacial conditions is to translate algorithms and software developments towards the creation of holistic patient models. A complete picture of the individual patient for treatment planning and personalized healthcare requires a compilation of clinician-friendly algorithms to provide minimally invasive diagnostic techniques with multimodal image integration and analysis. We describe here the implementation of the open-source Craniomaxillofacial module of the 3D Slicer software, as well as its clinical applications. This paper proposes data management approaches for multisource data extraction, registration, visualization, and quantification. These applications integrate medical images with clinical and biological data analytics, user studies, and other heterogeneous data.</p>","PeriodicalId":93218,"journal":{"name":"Multimodal learning for clinical decision support and clinical image-based procedures : 10th International Workshop, ML-CDS 2020, and 9th International Workshop, CLIP 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, ...","volume":"12445 ","pages":"44-53"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7786614/pdf/nihms-1656400.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38794840","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}
Pub Date : 2020-10-01DOI: 10.1007/978-3-030-60946-7_2
Yucheng Tang, Riqiang Gao, Ho Hin Lee, Quinn Stanton Wells, Ashley Spann, James G Terry, John J Carr, Yuankai Huo, Shunxing Bao, Bennett A Landman
Type II diabetes mellitus (T2DM) is a significant public health concern with multiple known risk factors (e.g., body mass index (BMI), body fat distribution, glucose levels). Improved prediction or prognosis would enable earlier intervention before possibly irreversible damage has occurred. Meanwhile, abdominal computed tomography (CT) is a relatively common imaging technique. Herein, we explore secondary use of the CT imaging data to refine the risk profile of future diagnosis of T2DM. In this work, we delineate quantitative information and imaging slices of patient history to predict onset T2DM retrieved from ICD-9 codes at least one year in the future. Furthermore, we investigate the role of five different types of electronic medical records (EMR), specifically 1) demographics; 2) pancreas volume; 3) visceral/subcutaneous fat volumes in L2 region of interest; 4) abdominal body fat distribution and 5) glucose lab tests in prediction. Next, we build a deep neural network to predict onset T2DM with pancreas imaging slices. Finally, motivated by multi-modal machine learning, we construct a merged framework to combine CT imaging slices with EMR information to refine the prediction. We empirically demonstrate our proposed joint analysis involving images and EMR leads to 4.25% and 6.93% AUC increase in predicting T2DM compared with only using images or EMR. In this study, we used case-control dataset of 997 subjects with CT scans and contextual EMR scores. To the best of our knowledge, this is the first work to show the ability to prognose T2DM using the patients' contextual and imaging history. We believe this study has promising potential for heterogeneous data analysis and multi-modal medical applications.
{"title":"Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records.","authors":"Yucheng Tang, Riqiang Gao, Ho Hin Lee, Quinn Stanton Wells, Ashley Spann, James G Terry, John J Carr, Yuankai Huo, Shunxing Bao, Bennett A Landman","doi":"10.1007/978-3-030-60946-7_2","DOIUrl":"10.1007/978-3-030-60946-7_2","url":null,"abstract":"<p><p>Type II diabetes mellitus (T2DM) is a significant public health concern with multiple known risk factors (<i>e.g.</i>, body mass index (BMI), body fat distribution, glucose levels). Improved prediction or prognosis would enable earlier intervention before possibly irreversible damage has occurred. Meanwhile, abdominal computed tomography (CT) is a relatively common imaging technique. Herein, we explore secondary use of the CT imaging data to refine the risk profile of future diagnosis of T2DM. In this work, we delineate quantitative information and imaging slices of patient history to predict onset T2DM retrieved from ICD-9 codes at least one year in the future. Furthermore, we investigate the role of five different types of electronic medical records (EMR), specifically 1) demographics; 2) pancreas volume; 3) visceral/subcutaneous fat volumes in L2 region of interest; 4) abdominal body fat distribution and 5) glucose lab tests in prediction. Next, we build a deep neural network to predict onset T2DM with pancreas imaging slices. Finally, motivated by multi-modal machine learning, we construct a merged framework to combine CT imaging slices with EMR information to refine the prediction. We empirically demonstrate our proposed joint analysis involving images and EMR leads to 4.25% and 6.93% AUC increase in predicting T2DM compared with only using images or EMR. In this study, we used case-control dataset of 997 subjects with CT scans and contextual EMR scores. To the best of our knowledge, this is the first work to show the ability to prognose T2DM using the patients' contextual and imaging history. We believe this study has promising potential for heterogeneous data analysis and multi-modal medical applications.</p>","PeriodicalId":93218,"journal":{"name":"Multimodal learning for clinical decision support and clinical image-based procedures : 10th International Workshop, ML-CDS 2020, and 9th International Workshop, CLIP 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, ...","volume":"12445 ","pages":"13-23"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8188902/pdf/nihms-1687707.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39100992","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}
Pub Date : 2020-01-01DOI: 10.1007/978-3-030-60946-7
T. Syeda-Mahmood, K. Drechsler, E. Bertino
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Multimodal learning for clinical decision support and clinical image-based procedures : 10th International Workshop, ML-CDS 2020, and 9th International Workshop, CLIP 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, ...
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