Cargando…

Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children

IMPORTANCE: Duodenal biopsies from children with enteropathies associated with undernutrition, such as environmental enteropathy (EE) and celiac disease (CD), display significant histopathological overlap. OBJECTIVE: To develop a convolutional neural network (CNN) to enhance the detection of patholo...

Descripción completa

Detalles Bibliográficos
Autores principales: Syed, Sana, Al-Boni, Mohammad, Khan, Marium N., Sadiq, Kamran, Iqbal, Najeeha T., Moskaluk, Christopher A., Kelly, Paul, Amadi, Beatrice, Ali, S. Asad, Moore, Sean R., Brown, Donald E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6575155/
https://www.ncbi.nlm.nih.gov/pubmed/31199451
http://dx.doi.org/10.1001/jamanetworkopen.2019.5822
_version_ 1783427804603351040
author Syed, Sana
Al-Boni, Mohammad
Khan, Marium N.
Sadiq, Kamran
Iqbal, Najeeha T.
Moskaluk, Christopher A.
Kelly, Paul
Amadi, Beatrice
Ali, S. Asad
Moore, Sean R.
Brown, Donald E.
author_facet Syed, Sana
Al-Boni, Mohammad
Khan, Marium N.
Sadiq, Kamran
Iqbal, Najeeha T.
Moskaluk, Christopher A.
Kelly, Paul
Amadi, Beatrice
Ali, S. Asad
Moore, Sean R.
Brown, Donald E.
author_sort Syed, Sana
collection PubMed
description IMPORTANCE: Duodenal biopsies from children with enteropathies associated with undernutrition, such as environmental enteropathy (EE) and celiac disease (CD), display significant histopathological overlap. OBJECTIVE: To develop a convolutional neural network (CNN) to enhance the detection of pathologic morphological features in diseased vs healthy duodenal tissue. DESIGN, SETTING, AND PARTICIPANTS: In this prospective diagnostic study, a CNN consisting of 4 convolutions, 1 fully connected layer, and 1 softmax layer was trained on duodenal biopsy images. Data were provided by 3 sites: Aga Khan University Hospital, Karachi, Pakistan; University Teaching Hospital, Lusaka, Zambia; and University of Virginia, Charlottesville. Duodenal biopsy slides from 102 children (10 with EE from Aga Khan University Hospital, 16 with EE from University Teaching Hospital, 34 with CD from University of Virginia, and 42 with no disease from University of Virginia) were converted into 3118 images. The CNN was designed and analyzed at the University of Virginia. The data were collected, prepared, and analyzed between November 2017 and February 2018. MAIN OUTCOMES AND MEASURES: Classification accuracy of the CNN per image and per case and incorrect classification rate identified by aggregated 10-fold cross-validation confusion/error matrices of CNN models. RESULTS: Overall, 102 children participated in this study, with a median (interquartile range) age of 31.0 (20.3-75.5) months and a roughly equal sex distribution, with 53 boys (51.9%). The model demonstrated 93.4% case-detection accuracy and had a false-negative rate of 2.4%. Confusion metrics indicated most incorrect classifications were between patients with CD and healthy patients. Feature map activations were visualized and learned distinctive patterns, including microlevel features in duodenal tissues, such as alterations in secretory cell populations. CONCLUSIONS AND RELEVANCE: A machine learning–based histopathological analysis model demonstrating 93.4% classification accuracy was developed for identifying and differentiating between duodenal biopsies from children with EE and CD. The combination of the CNN with a deconvolutional network enabled feature recognition and highlighted secretory cells’ role in the model’s ability to differentiate between these histologically similar diseases.
format Online
Article
Text
id pubmed-6575155
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher American Medical Association
record_format MEDLINE/PubMed
spelling pubmed-65751552019-07-02 Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children Syed, Sana Al-Boni, Mohammad Khan, Marium N. Sadiq, Kamran Iqbal, Najeeha T. Moskaluk, Christopher A. Kelly, Paul Amadi, Beatrice Ali, S. Asad Moore, Sean R. Brown, Donald E. JAMA Netw Open Original Investigation IMPORTANCE: Duodenal biopsies from children with enteropathies associated with undernutrition, such as environmental enteropathy (EE) and celiac disease (CD), display significant histopathological overlap. OBJECTIVE: To develop a convolutional neural network (CNN) to enhance the detection of pathologic morphological features in diseased vs healthy duodenal tissue. DESIGN, SETTING, AND PARTICIPANTS: In this prospective diagnostic study, a CNN consisting of 4 convolutions, 1 fully connected layer, and 1 softmax layer was trained on duodenal biopsy images. Data were provided by 3 sites: Aga Khan University Hospital, Karachi, Pakistan; University Teaching Hospital, Lusaka, Zambia; and University of Virginia, Charlottesville. Duodenal biopsy slides from 102 children (10 with EE from Aga Khan University Hospital, 16 with EE from University Teaching Hospital, 34 with CD from University of Virginia, and 42 with no disease from University of Virginia) were converted into 3118 images. The CNN was designed and analyzed at the University of Virginia. The data were collected, prepared, and analyzed between November 2017 and February 2018. MAIN OUTCOMES AND MEASURES: Classification accuracy of the CNN per image and per case and incorrect classification rate identified by aggregated 10-fold cross-validation confusion/error matrices of CNN models. RESULTS: Overall, 102 children participated in this study, with a median (interquartile range) age of 31.0 (20.3-75.5) months and a roughly equal sex distribution, with 53 boys (51.9%). The model demonstrated 93.4% case-detection accuracy and had a false-negative rate of 2.4%. Confusion metrics indicated most incorrect classifications were between patients with CD and healthy patients. Feature map activations were visualized and learned distinctive patterns, including microlevel features in duodenal tissues, such as alterations in secretory cell populations. CONCLUSIONS AND RELEVANCE: A machine learning–based histopathological analysis model demonstrating 93.4% classification accuracy was developed for identifying and differentiating between duodenal biopsies from children with EE and CD. The combination of the CNN with a deconvolutional network enabled feature recognition and highlighted secretory cells’ role in the model’s ability to differentiate between these histologically similar diseases. American Medical Association 2019-06-14 /pmc/articles/PMC6575155/ /pubmed/31199451 http://dx.doi.org/10.1001/jamanetworkopen.2019.5822 Text en Copyright 2019 Syed S et al. JAMA Network Open. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the CC-BY License.
spellingShingle Original Investigation
Syed, Sana
Al-Boni, Mohammad
Khan, Marium N.
Sadiq, Kamran
Iqbal, Najeeha T.
Moskaluk, Christopher A.
Kelly, Paul
Amadi, Beatrice
Ali, S. Asad
Moore, Sean R.
Brown, Donald E.
Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children
title Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children
title_full Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children
title_fullStr Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children
title_full_unstemmed Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children
title_short Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children
title_sort assessment of machine learning detection of environmental enteropathy and celiac disease in children
topic Original Investigation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6575155/
https://www.ncbi.nlm.nih.gov/pubmed/31199451
http://dx.doi.org/10.1001/jamanetworkopen.2019.5822
work_keys_str_mv AT syedsana assessmentofmachinelearningdetectionofenvironmentalenteropathyandceliacdiseaseinchildren
AT albonimohammad assessmentofmachinelearningdetectionofenvironmentalenteropathyandceliacdiseaseinchildren
AT khanmariumn assessmentofmachinelearningdetectionofenvironmentalenteropathyandceliacdiseaseinchildren
AT sadiqkamran assessmentofmachinelearningdetectionofenvironmentalenteropathyandceliacdiseaseinchildren
AT iqbalnajeehat assessmentofmachinelearningdetectionofenvironmentalenteropathyandceliacdiseaseinchildren
AT moskalukchristophera assessmentofmachinelearningdetectionofenvironmentalenteropathyandceliacdiseaseinchildren
AT kellypaul assessmentofmachinelearningdetectionofenvironmentalenteropathyandceliacdiseaseinchildren
AT amadibeatrice assessmentofmachinelearningdetectionofenvironmentalenteropathyandceliacdiseaseinchildren
AT alisasad assessmentofmachinelearningdetectionofenvironmentalenteropathyandceliacdiseaseinchildren
AT mooreseanr assessmentofmachinelearningdetectionofenvironmentalenteropathyandceliacdiseaseinchildren
AT browndonalde assessmentofmachinelearningdetectionofenvironmentalenteropathyandceliacdiseaseinchildren