scholarly journals Artificial intelligence deep learning algorithm for discriminating ungradable optical coherence tomography three-dimensional volumetric optic disc scans

2019 ◽  
Vol 6 (04) ◽  
pp. 1 ◽  
Author(s):  
An Ran Ran ◽  
Jian Shi ◽  
Amanda K. Ngai ◽  
Wai-Yin Chan ◽  
Poemen P. Chan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Optic Disc ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Optical Coherence ◽  
Deep Learning Algorithm

scholarly journals Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans

2020 ◽  
Vol 138 (4) ◽  
pp. 333 ◽  
Author(s):  
Atalie C. Thompson ◽  
Alessandro A. Jammal ◽  
Samuel I. Berchuck ◽  
Eduardo B. Mariottoni ◽  
Felipe A. Medeiros
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Optical Coherence ◽  
Deep Learning Algorithm

scholarly journals Identification of Coronary Calcifications in Optical Coherence Tomography Imaging Using Deep Learning

2020 ◽  
Author(s):  
Yarden Avital ◽  
Akiva Madar ◽  
Shlomi Arnon ◽  
Edward Koifman
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Image Acquisition ◽  
Percutaneous Treatment ◽  
Optical Coherence ◽  
Optimal Method ◽  
Deep Learning Algorithm ◽  
Coronary Calcifications ◽  
Artery Disease

Abstract Coronary calcifications are an obstacle for successful percutaneous treatment of coronary artery disease patients. The optimal method for delineating calcifications extent is optical coherence tomography (coronary OCT). To identify calcification on OCT and subsequently tailor the appropriate treatment, requires expertise in both image acquisition and interpretation. Image acquisition consists from system calibration, blood clearance by a contrast agent along with synchronization of the pullback process. Accurate interpretation demands careful review by the operator of a segment of 50-75mm of the coronary vessel at steps of 0.5-1mm accounting for 75-100 images in each OCT run, which is time consuming and necessitates some expertise in OCT analysis.In this paper we developed a new deep learning algorithm to assist the physician to identify and quantify coronary calcifications promptly, efficiently and accurately. Our algorithm achieves an accuracy of 0.9903 ± 0.009 over the test set at size of 1500 frames and even managed to find calcifications that weren’t recognized manually by the physician. For the best knowledge of the authors our algorithm achieves high accuracy which was never achieved in the past.

scholarly journals Histopathology-Based Deep-Learning Predicts Atherosclerotic Lesions in Intravascular Imaging

2021 ◽  
Vol 8 ◽  
Author(s):  
Olle Holmberg ◽  
Tobias Lenz ◽  
Valentin Koch ◽  
Aseel Alyagoob ◽  
Léa Utsch ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Expert Knowledge ◽  
Learning Algorithm ◽  
Atherosclerotic Lesion ◽  
Prediction Algorithm ◽  
Optical Coherence ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Atherosclerotic Lesions

Background: Optical coherence tomography is a powerful modality to assess atherosclerotic lesions, but detecting lesions in high-resolution OCT is challenging and requires expert knowledge. Deep-learning algorithms can be used to automatically identify atherosclerotic lesions, facilitating identification of patients at risk. We trained a deep-learning algorithm (DeepAD) with co-registered, annotated histopathology to predict atherosclerotic lesions in optical coherence tomography (OCT).Methods: Two datasets were used for training DeepAD: (i) a histopathology data set from 7 autopsy cases with 62 OCT frames and co-registered histopathology for high quality manual annotation and (ii) a clinical data set from 51 patients with 222 OCT frames in which manual annotations were based on clinical expertise only. A U-net based deep convolutional neural network (CNN) ensemble was employed as an atherosclerotic lesion prediction algorithm. Results were analyzed using intersection over union (IOU) for segmentation.Results: DeepAD showed good performance regarding the prediction of atherosclerotic lesions, with a median IOU of 0.68 ± 0.18 for segmentation of atherosclerotic lesions. Detection of calcified lesions yielded an IOU = 0.34. When training the algorithm without histopathology-based annotations, a performance drop of >0.25 IOU was observed. The practical application of DeepAD was evaluated retrospectively in a clinical cohort (n = 11 cases), showing high sensitivity as well as specificity and similar performance when compared to manual expert analysis.Conclusion: Automated detection of atherosclerotic lesions in OCT is improved using a histopathology-based deep-learning algorithm, allowing accurate detection in the clinical setting. An automated decision-support tool based on DeepAD could help in risk prediction and guide interventional treatment decisions.

scholarly journals Identification of coronary calcifications in optical coherence tomography imaging using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yarden Avital ◽  
Akiva Madar ◽  
Shlomi Arnon ◽  
Edward Koifman
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Image Acquisition ◽  
Percutaneous Treatment ◽  
Optical Coherence ◽  
Optimal Method ◽  
Deep Learning Algorithm ◽  
Coronary Calcifications ◽  
Artery Disease

AbstractCoronary calcifications are an obstacle for successful percutaneous treatment of coronary artery disease patients. The optimal method for delineating calcifications extent is coronary optical coherence tomography (OCT). To identify calcification on OCT and subsequently tailor the appropriate treatment, requires expertise in both image acquisition and interpretation. Image acquisition consists from system calibration, blood clearance by a contrast agent along with synchronization of the pullback process. Accurate interpretation demands careful review by the operator of a segment of 50–75 mm of the coronary vessel at steps of 5–10 frames per mm accounting for 375–540 images in each OCT run, which is time consuming and necessitates some expertise in OCT analysis. In this paper we developed a new deep learning algorithm to assist the physician to identify and quantify coronary calcifications promptly, efficiently and accurately. Our algorithm achieves an accuracy of 0.9903 ± 0.009 over the test set at size of 1500 frames and even managed to find calcifications that were not recognized manually by the physician. For the best knowledge of the authors our algorithm achieves high accuracy which was never achieved in the past.

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