Deep Learning Techniques for Real Time Computer-Aided Diagnosis in Colorectal Cancer

Nogueira-Rodríguez, Alba; López-Fernández, Hugo; Glez-Peña, Daniel

doi:10.1007/978-3-030-23946-6_27

Cited by 4 publications

(11 citation statements)

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Section: Methodsmentioning

confidence: 99%

“…PolyDeep is an artificial intelligence CADe/x system for the detection and characterization of colorectal polyps ( 3 , 9 , 11 , 12 ). This system is composed of two DL models, capable of detecting and classifying polypoid lesions in real time during colonoscopy ( 11 ). A collection of colorectal polyp videos and images known as the Polyp Image BAnk database (PIBAdb) was used to train the PolyDeep models ( 13 ).…”

Section: Methodsmentioning

confidence: 99%

“…water, instrumental, etc.). From the polyp video segments, 44,477 still images with polyp and 14,124 without polyp were obtained ( 3 , 9 , 11 ). Polyp images were extracted (i) systematically, one image by second, focusing on detection model development, and (ii) manually, by experts endoscopists, looking for higher-quality still polyp images, focusing on the classification model development.…”

Section: Methodsmentioning

confidence: 99%

“…The neural network architecture ResNet50 was used for the development of the colorectal polyp classification model ( 11 ). This model is integrated in the PolyDeep CAD and was used in the present work.…”

Section: Methodsmentioning

confidence: 99%

“…Given the clinical interest in the prevention of lower gastrointestinal disease and the increasing adoption of CADe/x systems, we decided to perform an in vitro analysis on still images of colorectal polyps to compare the optical diagnosis of expert endoscopists and PolyDeep, a CADe/x system developed by our research group in previous works ( 3 , 9 , 11 , 12 ).…”

Section: Introductionmentioning

confidence: 99%

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Optical diagnosis in still images of colorectal polyps: comparison between expert endoscopists and PolyDeep, a Computer-Aided Diagnosis system

Davila-Piñón,

Nogueira-Rodríguez,

Díez-Martín

et al. 2024

Front. Oncol.

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BackgroundPolyDeep is a computer-aided detection and classification (CADe/x) system trained to detect and classify polyps. During colonoscopy, CADe/x systems help endoscopists to predict the histology of colonic lesions.ObjectiveTo compare the diagnostic performance of PolyDeep and expert endoscopists for the optical diagnosis of colorectal polyps on still images.MethodsPolyDeep Image Classification (PIC) is an in vitro diagnostic test study. The PIC database contains NBI images of 491 colorectal polyps with histological diagnosis. We evaluated the diagnostic performance of PolyDeep and four expert endoscopists for neoplasia (adenoma, sessile serrated lesion, traditional serrated adenoma) and adenoma characterization and compared them with the McNemar test. Receiver operating characteristic curves were constructed to assess the overall discriminatory ability, comparing the area under the curve of endoscopists and PolyDeep with the chi- square homogeneity areas test.ResultsThe diagnostic performance of the endoscopists and PolyDeep in the characterization of neoplasia is similar in terms of sensitivity (PolyDeep: 89.05%; E1: 91.23%, p=0.5; E2: 96.11%, p<0.001; E3: 86.65%, p=0.3; E4: 91.26% p=0.3) and specificity (PolyDeep: 35.53%; E1: 33.80%, p=0.8; E2: 34.72%, p=1; E3: 39.24%, p=0.8; E4: 46.84%, p=0.2). The overall discriminative ability also showed no statistically significant differences (PolyDeep: 0.623; E1: 0.625, p=0.8; E2: 0.654, p=0.2; E3: 0.629, p=0.9; E4: 0.690, p=0.09). In the optical diagnosis of adenomatous polyps, we found that PolyDeep had a significantly higher sensitivity and a significantly lower specificity. The overall discriminative ability of adenomatous lesions by expert endoscopists is significantly higher than PolyDeep (PolyDeep: 0.582; E1: 0.685, p < 0.001; E2: 0.677, p < 0.0001; E3: 0.658, p < 0.01; E4: 0.694, p < 0.0001).ConclusionPolyDeep and endoscopists have similar diagnostic performance in the optical diagnosis of neoplastic lesions. However, endoscopists have a better global discriminatory ability than PolyDeep in the optical diagnosis of adenomatous polyps.

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