Computer-aided diagnosis for characterization of colorectal lesions: comprehensive software that includes differentiation of serrated lesions

Pu, Leonardo Zorrón Cheng Tao; Maicas, Gabriel; Tian, Yu; Yamamura, Takeshi; Nakamura, Masanao; Suzuki, Hiroto; Singh, Gurfarmaan; Rana, Khizar; Hirooka, Yoshiki; Burt, Alastair D.; Fujishiro, Mitsuhiro; Carneiro, Gustavo; Singh, Rajvinder

doi:10.1016/j.gie.2020.02.042

Cited by 40 publications

(15 citation statements)

References 25 publications

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“…Prior studies, with accuracies between 82.5 % and 97.8 % which used BLI or NBI in their algorithm were based on magnified images or videos [11,12,14,16,21,22,24,25].…”

Section: Discussionmentioning

confidence: 99%

Algorithm combining virtual chromoendoscopy features for colorectal polyp classification

et al. 2021

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Background and study aims Colonoscopy is considered the gold standard for decreasing colorectal cancer incidence and mortality. Optical diagnosis of colorectal polyps (CRPs) is an ongoing challenge in clinical colonoscopy and its accuracy among endoscopists varies widely. Computer-aided diagnosis (CAD) for CRP characterization may help to improve this accuracy. In this study, we investigated the diagnostic accuracy of a novel algorithm for polyp malignancy classification by exploiting the complementary information revealed by three specific modalities. Methods We developed a CAD algorithm for CRP characterization based on high-definition, non-magnified white light (HDWL), Blue light imaging (BLI) and linked color imaging (LCI) still images from routine exams. All CRPs were collected prospectively and classified into benign or premalignant using histopathology as gold standard. Images and data were used to train the CAD algorithm using triplet network architecture. Our training dataset was validated using a threefold cross validation. Results In total 609 colonoscopy images of 203 CRPs of 154 consecutive patients were collected. A total of 174 CRPs were found to be premalignant and 29 were benign. Combining the triplet network features with all three image enhancement modalities resulted in an accuracy of 90.6 %, 89.7 % sensitivity, 96.6 % specificity, a positive predictive value of 99.4 %, and a negative predictive value of 60.9 % for CRP malignancy classification. The classification time for our CAD algorithm was approximately 90 ms per image. Conclusions Our novel approach and algorithm for CRP classification differentiates accurately between benign and premalignant polyps in non-magnified endoscopic images. This is the first algorithm combining three optical modalities (HDWL/BLI/LCI) exploiting the triplet network approach.

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“…Prior studies, with accuracies between 82.5 % and 97.8 % which used BLI or NBI in their algorithm were based on magnified images or videos [11,12,14,16,21,22,24,25].…”

Section: Discussionmentioning

confidence: 99%

Algorithm combining virtual chromoendoscopy features for colorectal polyp classification

et al. 2021

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“…Using both NBI and blue light imaging (BLI), Zorron Cheng Tao Pu developed a CADx based on the modified Sano (MS) classification and validated it with two internal and external polyp image data sets. 39 , 42 The CADx had a mean area under the curve (AUC) of 94.3% for the internal set, and 84.5% and 90.3% for the external sets (NBI and BLI, respectively). A unique feature of this study was to show an equal highly accurate CADx prediction across two different imaging technologies (NBI and BLI), suggesting the potential to have a CADx trained and used with two different technologies, even when the predicted endoscopy imaging technology is not part of the training set.…”

Section: Ai For Characterization Of Colorectal Polyps (Cadx)mentioning

confidence: 99%

“…Using standard non-magnified NBI, Chen and colleagues31 developed a CNN-based CADx that had sensitivity, specificity, positive predictive value (PPV), NPV, and accuracy of 96.This study resulted in accurate automatic classification of diminutive polyps, irrespective of endoscopists' experience and NBI usage, which could potentially be a positive factor for the community endoscopists. Using both NBI and blue light imaging (BLI), Zorron Cheng Tao Pu developed a CADx based on the modified Sano (MS) classification and validated it with two internal and external polyp image data sets 39,42. The CADx had a mean area under the curve (AUC) of 94.3% for the internal set, and 84.5% and 90.3% for the external sets (NBI and BLI, respectively).…”

mentioning

confidence: 99%

Artificial intelligence for identification and characterization of colonic polyps

Parsa

Byrne

2021

Clin Med Insights Gastroenterol

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Colonoscopy remains the gold standard exam for colorectal cancer screening due to its ability to detect and resect pre-cancerous lesions in the colon. However, its performance is greatly operator dependent. Studies have shown that up to one-quarter of colorectal polyps can be missed on a single colonoscopy, leading to high rates of interval colorectal cancer. In addition, the American Society for Gastrointestinal Endoscopy has proposed the “resect-and-discard” and “diagnose-and-leave” strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the performance of optical biopsy has been suboptimal in community practice. With recent improvements in machine-learning techniques, artificial intelligence–assisted computer-aided detection and diagnosis have been increasingly utilized by endoscopists. The application of computer-aided design on real-time colonoscopy has been shown to increase the adenoma detection rate while decreasing the withdrawal time and improve endoscopists’ optical biopsy accuracy, while reducing the time to make the diagnosis. These are promising steps toward standardization and improvement of colonoscopy quality, and implementation of “resect-and-discard” and “diagnose-and-leave” strategies. Yet, issues such as real-world applications and regulatory approval need to be addressed before artificial intelligence models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of artificial intelligence for detection and characterization of colorectal polyps and review the limitation of existing artificial intelligence technologies and future directions for this field.

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“…In consideration of these fields of study, bias towards sensitivity is accepted. Trails concerning NBI colonoscopy were also performed and exhibited expectable results[ 7 , 96 - 98 ].…”

Section: Achievements Of Ann Research In Gi Diseasesmentioning

confidence: 99%

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Cao¹,

Zhang²,

Wei³

et al. 2021

WJG

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Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.

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Computer-aided diagnosis for characterization of colorectal lesions: comprehensive software that includes differentiation of serrated lesions

Cited by 40 publications

References 25 publications

Algorithm combining virtual chromoendoscopy features for colorectal polyp classification

Algorithm combining virtual chromoendoscopy features for colorectal polyp classification

Artificial intelligence for identification and characterization of colonic polyps

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

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