Automated identification of clinical features from sparsely annotated 3-dimensional medical imaging

Abstract One of the core challenges in applying machine learning and artificial intelligence to medicine is the limited availability of annotated medical data. Unlike in other applications of machine learning, where an abundance of labeled data is available, the labeling and annotation of medical da...

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Autores principales:	Nadav Rakocz, Jeffrey N. Chiang, Muneeswar G. Nittala, Giulia Corradetti, Liran Tiosano, Swetha Velaga, Michael Thompson, Brian L. Hill, Sriram Sankararaman, Jonathan L. Haines, Margaret A. Pericak-Vance, Dwight Stambolian, Srinivas R. Sadda, Eran Halperin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/db547242a9544aa0933b586e70820352
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spelling	oai:doaj.org-article:db547242a9544aa0933b586e708203522021-12-02T15:54:14ZAutomated identification of clinical features from sparsely annotated 3-dimensional medical imaging10.1038/s41746-021-00411-w2398-6352https://doaj.org/article/db547242a9544aa0933b586e708203522021-03-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00411-whttps://doaj.org/toc/2398-6352Abstract One of the core challenges in applying machine learning and artificial intelligence to medicine is the limited availability of annotated medical data. Unlike in other applications of machine learning, where an abundance of labeled data is available, the labeling and annotation of medical data and images require a major effort of manual work by expert clinicians who do not have the time to annotate manually. In this work, we propose a new deep learning technique (SLIVER-net), to predict clinical features from 3-dimensional volumes using a limited number of manually annotated examples. SLIVER-net is based on transfer learning, where we borrow information about the structure and parameters of the network from publicly available large datasets. Since public volume data are scarce, we use 2D images and account for the 3-dimensional structure using a novel deep learning method which tiles the volume scans, and then adds layers that leverage the 3D structure. In order to illustrate its utility, we apply SLIVER-net to predict risk factors for progression of age-related macular degeneration (AMD), a leading cause of blindness, from optical coherence tomography (OCT) volumes acquired from multiple sites. SLIVER-net successfully predicts these factors despite being trained with a relatively small number of annotated volumes (hundreds) and only dozens of positive training examples. Our empirical evaluation demonstrates that SLIVER-net significantly outperforms standard state-of-the-art deep learning techniques used for medical volumes, and its performance is generalizable as it was validated on an external testing set. In a direct comparison with a clinician panel, we find that SLIVER-net also outperforms junior specialists, and identifies AMD progression risk factors similarly to expert retina specialists.Nadav RakoczJeffrey N. ChiangMuneeswar G. NittalaGiulia CorradettiLiran TiosanoSwetha VelagaMichael ThompsonBrian L. HillSriram SankararamanJonathan L. HainesMargaret A. Pericak-VanceDwight StambolianSrinivas R. SaddaEran HalperinNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Nadav Rakocz Jeffrey N. Chiang Muneeswar G. Nittala Giulia Corradetti Liran Tiosano Swetha Velaga Michael Thompson Brian L. Hill Sriram Sankararaman Jonathan L. Haines Margaret A. Pericak-Vance Dwight Stambolian Srinivas R. Sadda Eran Halperin Automated identification of clinical features from sparsely annotated 3-dimensional medical imaging
description	Abstract One of the core challenges in applying machine learning and artificial intelligence to medicine is the limited availability of annotated medical data. Unlike in other applications of machine learning, where an abundance of labeled data is available, the labeling and annotation of medical data and images require a major effort of manual work by expert clinicians who do not have the time to annotate manually. In this work, we propose a new deep learning technique (SLIVER-net), to predict clinical features from 3-dimensional volumes using a limited number of manually annotated examples. SLIVER-net is based on transfer learning, where we borrow information about the structure and parameters of the network from publicly available large datasets. Since public volume data are scarce, we use 2D images and account for the 3-dimensional structure using a novel deep learning method which tiles the volume scans, and then adds layers that leverage the 3D structure. In order to illustrate its utility, we apply SLIVER-net to predict risk factors for progression of age-related macular degeneration (AMD), a leading cause of blindness, from optical coherence tomography (OCT) volumes acquired from multiple sites. SLIVER-net successfully predicts these factors despite being trained with a relatively small number of annotated volumes (hundreds) and only dozens of positive training examples. Our empirical evaluation demonstrates that SLIVER-net significantly outperforms standard state-of-the-art deep learning techniques used for medical volumes, and its performance is generalizable as it was validated on an external testing set. In a direct comparison with a clinician panel, we find that SLIVER-net also outperforms junior specialists, and identifies AMD progression risk factors similarly to expert retina specialists.
format	article
author	Nadav Rakocz Jeffrey N. Chiang Muneeswar G. Nittala Giulia Corradetti Liran Tiosano Swetha Velaga Michael Thompson Brian L. Hill Sriram Sankararaman Jonathan L. Haines Margaret A. Pericak-Vance Dwight Stambolian Srinivas R. Sadda Eran Halperin
author_facet	Nadav Rakocz Jeffrey N. Chiang Muneeswar G. Nittala Giulia Corradetti Liran Tiosano Swetha Velaga Michael Thompson Brian L. Hill Sriram Sankararaman Jonathan L. Haines Margaret A. Pericak-Vance Dwight Stambolian Srinivas R. Sadda Eran Halperin
author_sort	Nadav Rakocz
title	Automated identification of clinical features from sparsely annotated 3-dimensional medical imaging
title_short	Automated identification of clinical features from sparsely annotated 3-dimensional medical imaging
title_full	Automated identification of clinical features from sparsely annotated 3-dimensional medical imaging
title_fullStr	Automated identification of clinical features from sparsely annotated 3-dimensional medical imaging
title_full_unstemmed	Automated identification of clinical features from sparsely annotated 3-dimensional medical imaging
title_sort	automated identification of clinical features from sparsely annotated 3-dimensional medical imaging
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/db547242a9544aa0933b586e70820352
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Automated identification of clinical features from sparsely annotated 3-dimensional medical imaging

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