scholarly journals Deep Learning Applied to White Light and Narrow Band Imaging Videolaryngoscopy: Toward Real‐Time Laryngeal Cancer Detection

2021 ◽  
Author(s):  
Muhammad Adeel Azam ◽  
Claudio Sampieri ◽  
Alessandro Ioppi ◽  
Stefano Africano ◽  
Alberto Vallin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Laryngeal Cancer ◽  
Cancer Detection ◽  
White Light ◽  
Narrow Band ◽  
Narrow Band Imaging

A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy

Endoscopy ◽  
2020 ◽  
Author(s):  
Tingsheng Ling ◽  
Lianlian Wu ◽  
Yiwei Fu ◽  
Qinwei Xu ◽  
Ping An ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Deep Learning ◽  
Early Gastric Cancer ◽  
Real Time ◽  
Narrow Band ◽  
Narrow Band Imaging ◽  
Superior Performance ◽  
Accurate Identification ◽  
Real Time System ◽  
Differentiation Status

Abstract Background Accurate identification of the differentiation status and margins for early gastric cancer (EGC) is critical for determining the surgical strategy and achieving curative resection in EGC patients. The aim of this study was to develop a real-time system to accurately identify differentiation status and delineate the margins of EGC on magnifying narrow-band imaging (ME-NBI) endoscopy. Methods 2217 images from 145 EGC patients and 1870 images from 139 EGC patients were retrospectively collected to train and test the first convolutional neural network (CNN1) to identify EGC differentiation status. The performance of CNN1 was then compared with that of experts using 882 images from 58 EGC patients. Finally, 928 images from 132 EGC patients and 742 images from 87 EGC patients were used to train and test CNN2 to delineate the EGC margins. Results The system correctly predicted the differentiation status of EGCs with an accuracy of 83.3 % (95 % confidence interval [CI] 81.5 % – 84.9 %) in the testing dataset. In the man – machine contest, CNN1 performed significantly better than the five experts (86.2 %, 95 %CI 75.1 % – 92.8 % vs. 69.7 %, 95 %CI 64.1 % – 74.7 %). For delineating EGC margins, the system achieved an accuracy of 82.7 % (95 %CI 78.6 % – 86.1 %) in differentiated EGC and 88.1 % (95 %CI 84.2 % – 91.1 %) in undifferentiated EGC under an overlap ratio of 0.80. In unprocessed EGC videos, the system achieved real-time diagnosis of EGC differentiation status and EGC margin delineation in ME-NBI endoscopy. Conclusion We developed a deep learning-based system to accurately identify differentiation status and delineate the margins of EGC in ME-NBI endoscopy. This system achieved superior performance when compared with experts and was successfully tested in real EGC videos.

Deep Learning for nasopharyngeal Carcinoma Identification Using Both White Light and Narrow‐Band Imaging Endoscopy

2021 ◽  
Author(s):  
Jianwei Xu ◽  
Jun Wang ◽  
Xianzhang Bian ◽  
Ji‐Qing Zhu ◽  
Cheng‐Wei Tie ◽  
...  
Keyword(s):  
Deep Learning ◽  
Nasopharyngeal Carcinoma ◽  
White Light ◽  
Narrow Band ◽  
Narrow Band Imaging

scholarly journals Comparison Between 5-Aminolevulinic Acid Photodynamic Diagnosis and Narrow- Band Imaging for Bladder Cancer Detection: A Single-Center Retrospective Cohort Study

2021 ◽  
Author(s):  
Hiroki Hagimoto ◽  
Noriyuki Makita ◽  
Yuta Mine ◽  
Hidetoshi Kokubun ◽  
Shiori Murata ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Cancer Detection ◽  
White Light ◽  
Transurethral Resection ◽  
Aminolevulinic Acid ◽  
Narrow Band ◽  
Narrow Band Imaging ◽  
Photodynamic Diagnosis ◽  
Bladder Tumors

Abstract BackgroundNo comparative studies exist between 5-aminolevulinic acid-photodynamic diagnosis (PDD) and narrow-band imaging (NBI) for the detection of urothelial carcinoma. Therefore, we compared 5-aminolevulinic acid-mediated PDD with NBI for cancer detection during transurethral resection of bladder tumors.MethodsBetween June 2018 and October 2020, 114 patients and 282 lesions were included in the analysis. Patients were orally administered 5-aminolevulinic acid (20 mg/kg) 2 h before transurethral resection of bladder tumors. The bladder was inspected with white light, PDD, and NBI for each patient and all areas that were positive by at least one method were resected or biopsied. The imaging data were then compared to the pathology results.ResultsThe sensitivity, specificity, positive predictive value, and negative predictive value for detecting urothelial carcinoma were 88.1%, 47.5%, 80.9%, and 61.3% for white light; 89.6%, 22.5%, 74.5%, and 46.2% for PDD; and 76.2%, 46.3%, 78.2%, and 43.5% for NBI, respectively. PPD was significantly more sensitive than NBI for all lesions (p<0.001), including carcinoma in situ lesions (94.6% vs. 54.1%, p<0.001).ConclusionsPDD can increase the detection rate of bladder cancer compared to NBI by greater than 10%. Adding PDD to white light can detect 100% of carcinoma in situ lesions.

scholarly journals PICCOLO White-Light and Narrow-Band Imaging Colonoscopic Dataset: A Performance Comparative of Models and Datasets

2020 ◽  
Vol 10 (23) ◽  
pp. 8501
Author(s):  
Luisa F. Sánchez-Peralta ◽  
J. Blas Pagador ◽  
Artzai Picón ◽  
Ángel José Calderón ◽  
Francisco Polo ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
White Light ◽  
Narrow Band ◽  
Narrow Band Imaging ◽  
Polyp Detection ◽  
Test Set ◽  
Learning Techniques ◽  
Adenoma Detection ◽  
Comparison Results

Colorectal cancer is one of the world leading death causes. Fortunately, an early diagnosis allows for effective treatment, increasing the survival rate. Deep learning techniques have shown their utility for increasing the adenoma detection rate at colonoscopy, but a dataset is usually required so the model can automatically learn features that characterize the polyps. In this work, we present the PICCOLO dataset, that comprises 3433 manually annotated images (2131 white-light images 1302 narrow-band images), originated from 76 lesions from 40 patients, which are distributed into training (2203), validation (897) and test (333) sets assuring patient independence between sets. Furthermore, clinical metadata are also provided for each lesion. Four different models, obtained by combining two backbones and two encoder–decoder architectures, are trained with the PICCOLO dataset and other two publicly available datasets for comparison. Results are provided for the test set of each dataset. Models trained with the PICCOLO dataset have a better generalization capacity, as they perform more uniformly along test sets of all datasets, rather than obtaining the best results for its own test set. This dataset is available at the website of the Basque Biobank, so it is expected that it will contribute to the further development of deep learning methods for polyp detection, localisation and classification, which would eventually result in a better and earlier diagnosis of colorectal cancer, hence improving patient outcomes.

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