Sana Syed1,2, Lubaina Ehsan1, Aman Shrivastava1,3, Saurav Sengupta1,3, Marium Khan1, Kamran Kowsari4,5, Shan Guleria6, Rasoul Sali4, Karan Kant3, Sung-Jun Kang3, Kamran Sadiq2, Najeeha T Iqbal2, Lin Cheng7, Christopher A Moskaluk8, Paul Kelly9,10, Beatrice C Amadi9, Syed Asad Ali2, Sean R Moore1, Donald E Brown4. 1. Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, VA. 2. Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan. 3. Data Science Institute, University of Virginia, Charlottesville. 4. Systems and Information Engineering, University of Virginia, Charlottesville, VA. 5. University of California Los Angeles, Los Angeles, CA. 6. School of Medicine, University of Virginia, Charlottesville, VA. 7. Pathology Department, Rush University Medical Center, Chicago, IL. 8. Department of Pathology, University of Virginia, Charlottesville, VA. 9. Tropical Gastroenterology and Nutrition group, University of Zambia School of Medicine, Lusaka, Zambia. 10. Blizard Institute, Barts and the London School of Medicine, Queen Mary University of London, London, United Kingdom.
Abstract
OBJECTIVES: Striking histopathological overlap between distinct but related conditions poses a disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics. This need is particularly salient with small bowel enteropathies; environmental enteropathy (EE) and celiac disease (CD). We built upon our preliminary analysis by developing an artificial intelligence (AI)-based image analysis platform utilizing deep learning convolutional neural networks (CNNs) for these enteropathies. METHODS: Data for the secondary analysis was obtained from three primary studies at different sites. The image analysis platform for EE and CD was developed using CNNs including one with multizoom architecture. Gradient-weighted class activation mappings (Grad-CAMs) were used to visualize the models' decision-making process for classifying each disease. A team of medical experts simultaneously reviewed the stain color normalized images done for bias reduction and Grad-CAMs to confirm structural preservation and biomedical relevance, respectively. RESULTS: Four hundred and sixty-one high-resolution biopsy images from 150 children were acquired. Median age (interquartile range) was 37.5 (19.0-121.5) months with a roughly equal sex distribution; 77 males (51.3%). ResNet50 and shallow CNN demonstrated 98% and 96% case-detection accuracy, respectively, which increased to 98.3% with an ensemble. Grad-CAMs demonstrated models' ability to learn different microscopic morphological features for EE, CD, and controls. CONCLUSIONS: Our AI-based image analysis platform demonstrated high classification accuracy for small bowel enteropathies which was capable of identifying biologically relevant microscopic features and emulating human pathologist decision-making process. Grad-CAMs illuminated the otherwise "black box" of deep learning in medicine, allowing for increased physician confidence in adopting these new technologies in clinical practice.
OBJECTIVES: Striking histopathological overlap between distinct but related conditions poses a disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics. This need is particularly salient with small bowel enteropathies; environmental enteropathy (EE) and celiac disease (CD). We built upon our preliminary analysis by developing an artificial intelligence (AI)-based image analysis platform utilizing deep learning convolutional neural networks (CNNs) for these enteropathies. METHODS: Data for the secondary analysis was obtained from three primary studies at different sites. The image analysis platform for EE and CD was developed using CNNs including one with multizoom architecture. Gradient-weighted class activation mappings (Grad-CAMs) were used to visualize the models' decision-making process for classifying each disease. A team of medical experts simultaneously reviewed the stain color normalized images done for bias reduction and Grad-CAMs to confirm structural preservation and biomedical relevance, respectively. RESULTS: Four hundred and sixty-one high-resolution biopsy images from 150 children were acquired. Median age (interquartile range) was 37.5 (19.0-121.5) months with a roughly equal sex distribution; 77 males (51.3%). ResNet50 and shallow CNN demonstrated 98% and 96% case-detection accuracy, respectively, which increased to 98.3% with an ensemble. Grad-CAMs demonstrated models' ability to learn different microscopic morphological features for EE, CD, and controls. CONCLUSIONS: Our AI-based image analysis platform demonstrated high classification accuracy for small bowel enteropathies which was capable of identifying biologically relevant microscopic features and emulating human pathologist decision-making process. Grad-CAMs illuminated the otherwise "black box" of deep learning in medicine, allowing for increased physician confidence in adopting these new technologies in clinical practice.
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Authors: Najeeha T Iqbal; Sana Syed; Kamran Sadiq; Marium N Khan; Junaid Iqbal; Jennie Z Ma; Fayaz Umrani; Sheraz Ahmed; Elizabeth A Maier; Lee A Denson; Yael Haberman; Monica M McNeal; Kenneth D R Setchell; Xueheng Zhao; Shahida Qureshi; Lanlan Shen; Christopher A Moskaluk; Ta-Chiang Liu; Omer Yilmaz; Donald E Brown; Michael J Barratt; Vanderlene L Kung; Jeffrey I Gordon; Sean R Moore; S Asad Ali Journal: BMC Pediatr Date: 2019-07-22 Impact factor: 2.125
Authors: Nicolas Coudray; Paolo Santiago Ocampo; Theodore Sakellaropoulos; Navneet Narula; Matija Snuderl; David Fenyö; Andre L Moreira; Narges Razavian; Aristotelis Tsirigos Journal: Nat Med Date: 2018-09-17 Impact factor: 53.440
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