A deep learning‐based model for characterization of atherosclerotic plaque in coronary arteries using optical coherence tomography images

2021 ◽  
Author(s):  
Atefeh Abdolmanafi ◽  
Luc Duong ◽  
Ragui Ibrahim ◽  
Nagib Dahdah
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Coronary Arteries ◽  
Atherosclerotic Plaque ◽  
Optical Coherence

scholarly journals Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning

2021 ◽  
Vol 11 (7) ◽  
pp. 3119
Author(s):  
Cristina L. Saratxaga ◽  
Jorge Bote ◽  
Juan F. Ortega-Morán ◽  
Artzai Picón ◽  
Elena Terradillos ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Data Augmentation ◽  
Optical Biopsy ◽  
Colon Polyps ◽  
Optical Coherence ◽  
Colon Lesion ◽  
Patient Prognosis ◽  
B Scan Images

(1) Background: Clinicians demand new tools for early diagnosis and improved detection of colon lesions that are vital for patient prognosis. Optical coherence tomography (OCT) allows microscopical inspection of tissue and might serve as an optical biopsy method that could lead to in-situ diagnosis and treatment decisions; (2) Methods: A database of murine (rat) healthy, hyperplastic and neoplastic colonic samples with more than 94,000 images was acquired. A methodology that includes a data augmentation processing strategy and a deep learning model for automatic classification (benign vs. malignant) of OCT images is presented and validated over this dataset. Comparative evaluation is performed both over individual B-scan images and C-scan volumes; (3) Results: A model was trained and evaluated with the proposed methodology using six different data splits to present statistically significant results. Considering this, 0.9695 (±0.0141) sensitivity and 0.8094 (±0.1524) specificity were obtained when diagnosis was performed over B-scan images. On the other hand, 0.9821 (±0.0197) sensitivity and 0.7865 (±0.205) specificity were achieved when diagnosis was made considering all the images in the whole C-scan volume; (4) Conclusions: The proposed methodology based on deep learning showed great potential for the automatic characterization of colon polyps and future development of the optical biopsy paradigm.

scholarly journals In Vivo Characterization of Coronary Atherosclerotic Plaque by Use of Optical Coherence Tomography

Circulation ◽  
2005 ◽  
Vol 111 (12) ◽  
pp. 1551-1555 ◽  
Author(s):  
Ik-Kyung Jang ◽  
Guillermo J. Tearney ◽  
Briain MacNeill ◽  
Masamichi Takano ◽  
Fabian Moselewski ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Atherosclerotic Plaque ◽  
Optical Coherence ◽  
Coronary Atherosclerotic Plaque ◽  
In Vivo Characterization

scholarly journals Morpho-molecular ex vivo detection and grading of non-muscle-invasive bladder cancer using forward imaging probe based multimodal optical coherence tomography and Raman spectroscopy

The Analyst ◽  
2020 ◽  
Vol 145 (4) ◽  
pp. 1445-1456 ◽  
Author(s):  
Fabian Placzek ◽  
Eliana Cordero Bautista ◽  
Simon Kretschmer ◽  
Lara M. Wurster ◽  
Florian Knorr ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Optical Coherence Tomography ◽  
Imaging System ◽  
Ex Vivo ◽  
Large Field ◽  
Optical Coherence ◽  
Fiber Optic Probe ◽  
Optic Probe ◽  
Muscle Invasive

Characterization of bladder biopsies, using a combined fiber optic probe-based optical coherence tomography and Raman spectroscopy imaging system that allows a large field-of-view imaging and detection and grading of cancerous bladder lesions.

scholarly journals Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images

2020 ◽  
pp. bjophthalmol-2020-317825
Author(s):  
Yonghao Li ◽  
Weibo Feng ◽  
Xiujuan Zhao ◽  
Bingqian Liu ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
High Myopia ◽  
Large Scale ◽  
Learning System ◽  
Youden Index ◽  
Optical Coherence ◽  
Test Dataset ◽  
Independent Test ◽  
Independent Test Dataset

Background/aimsTo apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images.MethodsIn this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index.ResultsIn the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps.ConclusionsWe used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

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