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Semi-supervised lea...
Semi-supervised learning with natural language processing for right ventricle classification in echocardiography—a scalable approach
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- Hagberg, Eva (author)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,University of Gothenburg
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- Hagerman, David, 1987 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,Chalmers tekniska högskola,Chalmers University of Technology
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- Johansson, Richard, 1975 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU),University of Gothenburg
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- Hosseini, N. (author)
- Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital
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- Liu, J. (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Björnsson, E. (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Alvén, Jennifer, 1989 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,Chalmers tekniska högskola,Chalmers University of Technology
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- Hjelmgren, Ola (author)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,University of Gothenburg,Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital
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(creator_code:org_t)
- Elsevier BV, 2022
- 2022
- English.
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In: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825 .- 1879-0534. ; 143
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Abstract
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- We created a deep learning model, trained on text classified by natural language processing (NLP), to assess right ventricular (RV) size and function from echocardiographic images. We included 12,684 examinations with corresponding written reports for text classification. After manual annotation of 1489 reports, we trained an NLP model to classify the remaining 10,651 reports. A view classifier was developed to select the 4-chamber or RV-focused view from an echocardiographic examination (n = 539). The final models were two image classification models trained on the predicted labels from the combined manual annotation and NLP models and the corresponding echocardiographic view to assess RV function (training set n = 11,008) and size (training set n = 9951. The text classifier identified impaired RV function with 99% sensitivity and 98% specificity and RV enlargement with 98% sensitivity and 98% specificity. The view classification model identified the 4-chamber view with 92% accuracy and the RV-focused view with 73% accuracy. The image classification models identified impaired RV function with 93% sensitivity and 72% specificity and an enlarged RV with 80% sensitivity and 85% specificity; agreement with the written reports was substantial (both κ = 0.65). Our findings show that models for automatic image assessment can be trained to classify RV size and function by using model-annotated data from written echocardiography reports. This pipeline for auto-annotation of the echocardiographic images, using a NLP model with medical reports as input, can be used to train an image-assessment model without manual annotation of images and enables fast and inexpensive expansion of the training dataset when needed. © 2022
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Language Technology (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Keyword
- Algorithm
- Automation
- Echocardiography
- Machine learning
- Natural language processing
- Right ventricle
- Scalable
- Semi-supervised
- Classification (of information)
- Deep learning
- Image annotation
- Learning algorithms
- Medical imaging
- Text processing
- Classification models
- Echocardiographic images
- Images classification
- Manual annotation
- Processing model
- Right ventricular
- Right ventricular functions
- Natural language processing systems
- Algorithm
Publication and Content Type
- ref (subject category)
- art (subject category)
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Hagberg, Eva
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Hagerman, David, ...
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Johansson, Richa ...
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Hosseini, N.
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Liu, J.
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Björnsson, E.
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show more...
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Alvén, Jennifer, ...
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Hjelmgren, Ola
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- ENGINEERING AND TECHNOLOGY
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ENGINEERING AND ...
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Clinical Medicin ...
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MEDICAL AND HEAL ...
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NATURAL SCIENCES
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ENGINEERING AND ...
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Computers in Bio ...
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University of Gothenburg
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Chalmers University of Technology