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Deep learning in interstitial lung disease-how long until daily practice.

ABSTRACT: Interstitial lung diseases are a diverse group of disorders that involve inflammation and fibrosis of interstitium, with clinical, radiological, and pathological overlapping features. These are an important cause of morbidity and mortality among lung diseases. This review describes computer-aided diagnosis systems centered on deep learning approaches that improve the diagnostic of interstitial lung diseases. We highlighted the challenges and the implementation of important daily practice, especially in the early diagnosis of idiopathic pulmonary fibrosis (IPF). Developing a convolutional neuronal network (CNN) that could be deployed on any computer station and be accessible to non-academic centers is the next frontier that needs to be crossed. In the future, early diagnosis of IPF should be possible. CNN might not only spare the human resources but also will reduce the costs spent on all the social and healthcare aspects of this deadly disease.Key Points• Deep learning algorithms are used in pattern recognition of different interstitial lung diseases.• High-resolution computed tomography plays a central role in the diagnosis and in the management of all interstitial lung diseases, especially fibrotic lung disease.• Developing an accessible algorithm that could be deployed on any computer station and be used in non-academic centers is the next frontier in the early diagnosis of idiopathic pulmonary fibrosis.

SUBMITTER: Trusculescu AA

PROVIDER: S-EPMC7554005 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Deep learning in interstitial lung disease-how long until daily practice.

Trusculescu Ana Adriana AA Manolescu Diana D Tudorache Emanuela E Oancea Cristian C

European radiology 20200614 11

Interstitial lung diseases are a diverse group of disorders that involve inflammation and fibrosis of interstitium, with clinical, radiological, and pathological overlapping features. These are an important cause of morbidity and mortality among lung diseases. This review describes computer-aided diagnosis systems centered on deep learning approaches that improve the diagnostic of interstitial lung diseases. We highlighted the challenges and the implementation of important daily practice, especi ...[more]

PMID: 32537728

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Project description:PurposeTo develop a deep learning algorithm for the automatic assessment of the extent of systemic sclerosis (SSc)-related interstitial lung disease (ILD) on chest CT images.Materials and methodsThis retrospective study included 208 patients with SSc (median age, 57 years; 167 women) evaluated between January 2009 and October 2017. A multicomponent deep neural network (AtlasNet) was trained on 6888 fully annotated CT images (80% for training and 20% for validation) from 17 patients with no, mild, or severe lung disease. The model was tested on a dataset of 400 images from another 20 patients, independently partially annotated by three radiologist readers. The ILD contours from the three readers and the deep learning neural network were compared by using the Dice similarity coefficient (DSC). The correlation between disease extent obtained from the deep learning algorithm and that obtained by using pulmonary function tests (PFTs) was then evaluated in the remaining 171 patients and in an external validation dataset of 31 patients based on the analysis of all slices of the chest CT scan. The Spearman rank correlation coefficient (ρ) was calculated to evaluate the correlation between disease extent and PFT results.ResultsThe median DSCs between the readers and the deep learning ILD contours ranged from 0.74 to 0.75, whereas the median DSCs between contours from radiologists ranged from 0.68 to 0.71. The disease extent obtained from the algorithm, by analyzing the whole CT scan, correlated with the diffusion lung capacity for carbon monoxide, total lung capacity, and forced vital capacity (ρ = -0.76, -0.70, and -0.62, respectively; P < .001 for all) in the dataset for the correlation with PFT results. The disease extents correlated with diffusion lung capacity for carbon monoxide, total lung capacity, and forced vital capacity were ρ = -0.65, -0.70, and -0.57, respectively, in the external validation dataset (P < .001 for all).ConclusionThe developed algorithm performed similarly to radiologists for disease-extent contouring, which correlated with pulmonary function to assess CT images from patients with SSc-related ILD.Supplemental material is available for this article.© RSNA, 2020.

Project description:Background: Interstitial lung diseases (ILDs) are often associated with rheumatic diseases. Their early diagnosis and management are not only difficult, but also crucial, because they are associated with major morbidity and mortality and can be the first cause of death in autoimmune rheumatic diseases (ARDs). Objectives: By using methodologies, such as Nominal Group Technique (NGT) and Delphi Survey, the aims of this study were (1) to measure consensus between pulmonologists, radiologists, and rheumatologists experienced in the management of ARD-ILD; (2) to highlight the importance of a multidisciplinary approach; and (3) to provide clinicians with a practical tool aimed at improving the prompt recognition and follow-up of ILD associated with ARDs and of any possible rheumatic conditions underlying ILD. Results: During the NGT round, the Steering Committee defined 57 statements to be used in the Delphi survey. A total of 78 experts participated in the Delphi survey, namely 28 pulmonologists, 33 rheumatologists, and 17 radiologists. During this round, consensus on agreement was reached in 47 statements, while disagreement was not reached in any statements. A secondary questionnaire was drafted by the Steering Committee to obtain clearer indications on ILD-ARD "red-flags" and follow-up. Delphi Panelists took part also in the second-questionnaire survey. Answers from both surveys were used to draft two checklists of "red flags" sign or symptom suggestive of ILD and ARD, respectively, and two checklists on identification and monitoring of rheumatoid arthritis (RA) and systemic sclerosis (SSc) ILD. Limitations: This study is a consensus work, which cannot produce empiric data, and is limited to the Italian scenario. Conclusions: This work showed a high level of agreement, but also shows some divergent opinions between different experts. This underlines the importance of a multidisciplinary approach. Eventually, we believe the drafted checklists can help clinicians in the diagnosis and follow-up of ILD-ARD.

Project description:Interstitial pneumonia is a heterogeneous disease with a progressive course and poor prognosis, at times even worse than those in the main cancer types. Histopathological examination is crucial for its diagnosis and estimation of prognosis. However, the evaluation strongly depends on the experience of pathologists, and the reproducibility of diagnosis is low. Herein, we propose MIXTURE (huMan-In-the-loop eXplainable artificial intelligence Through the Use of REcurrent training), an original method to develop deep learning models for extracting pathologically significant findings based on an expert pathologist's perspective with a small annotation effort. The procedure of MIXTURE consists of three steps as follows. First, we created feature extractors for tiles from whole slide images using self-supervised learning. The similar looking tiles were clustered based on the output features and then pathologists integrated the pathologically synonymous clusters. Using the integrated clusters as labeled data, deep learning models to classify the tiles into pathological findings were created by transfer-learning the feature extractors. We developed three models for different magnifications. Using these extracted findings, our model was able to predict the diagnosis of usual interstitial pneumonia, a finding suggestive of progressive disease, with high accuracy (AUC 0.90 in validation set and AUC 0.86 in test set). This high accuracy could not be achieved without the integration of findings by pathologists. The patients predicted as UIP had poorer prognosis (5-year overall survival [OS]: 55.4%) than those predicted as non-UIP (OS: 95.2%). The Cox proportional hazards model for each microscopic finding and prognosis pointed out dense fibrosis, fibroblastic foci, elastosis, and lymphocyte aggregation as independent risk factors. We suggest that MIXTURE may serve as a model approach to different diseases evaluated by medical imaging, including pathology and radiology, and be the prototype for explainable artificial intelligence that can collaborate with humans.

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Deep learning in interstitial lung disease-how long until daily practice.

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Deep learning in interstitial lung disease-how long until daily practice.

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