scholarly journals Confocal Laser Endomicroscopy in the Diagnosis of Biliary and Pancreatic Disorders: A Systematic Analysis

2021 ◽  
Author(s):  
Do Han Kim ◽  
Somashekar G. Krishna ◽  
Emmanuel Coronel ◽  
Paul T. Kröner ◽  
Herbert C. Wolfsen ◽  
...  
Keyword(s):  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Systematic Analysis ◽  
Pancreatic Disorders

scholarly journals A novel approach to the diagnosis of pancreatic serous cystadenoma: needle-based confocal laser endomicroscopy

Endoscopy ◽  
2014 ◽  
Vol 47 (01) ◽  
pp. 26-32 ◽  
Author(s):  
Bertrand Napoléon ◽  
Anne-Isabelle Lemaistre ◽  
Bertrand Pujol ◽  
Fabrice Caillol ◽  
Damien Lucidarme ◽  
...  
Keyword(s):  
Serous Cystadenoma ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Novel Approach

scholarly journals Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shan Guleria ◽  
Tilak U. Shah ◽  
J. Vincent Pulido ◽  
Matthew Fasullo ◽  
Lubaina Ehsan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Accuracy ◽  
High Sensitivity ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Learning Approaches ◽  
Learning Models ◽  
Whole Slide Image ◽  
Slide Image ◽  
Level Model

AbstractProbe-based confocal laser endomicroscopy (pCLE) allows for real-time diagnosis of dysplasia and cancer in Barrett’s esophagus (BE) but is limited by low sensitivity. Even the gold standard of histopathology is hindered by poor agreement between pathologists. We deployed deep-learning-based image and video analysis in order to improve diagnostic accuracy of pCLE videos and biopsy images. Blinded experts categorized biopsies and pCLE videos as squamous, non-dysplastic BE, or dysplasia/cancer, and deep learning models were trained to classify the data into these three categories. Biopsy classification was conducted using two distinct approaches—a patch-level model and a whole-slide-image-level model. Gradient-weighted class activation maps (Grad-CAMs) were extracted from pCLE and biopsy models in order to determine tissue structures deemed relevant by the models. 1970 pCLE videos, 897,931 biopsy patches, and 387 whole-slide images were used to train, test, and validate the models. In pCLE analysis, models achieved a high sensitivity for dysplasia (71%) and an overall accuracy of 90% for all classes. For biopsies at the patch level, the model achieved a sensitivity of 72% for dysplasia and an overall accuracy of 90%. The whole-slide-image-level model achieved a sensitivity of 90% for dysplasia and 94% overall accuracy. Grad-CAMs for all models showed activation in medically relevant tissue regions. Our deep learning models achieved high diagnostic accuracy for both pCLE-based and histopathologic diagnosis of esophageal dysplasia and its precursors, similar to human accuracy in prior studies. These machine learning approaches may improve accuracy and efficiency of current screening protocols.

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