High pooled performance of convolutional neural networks in computer-aided diagnosis of GI ulcers and/or hemorrhage on wireless capsule endoscopy images: a systematic review and meta-analysis

Adler, Douglas G.; Khan, Shahab R.; Kassab, Lena L.; Ponnada, Suresh; Chandan, Saurabh; Ali, Tauseef; Dulai, Parambir S.; Kochhar, Gursimran

doi:10.1016/j.gie.2020.07.038

Cited by 21 publications

(19 citation statements)

References 20 publications

(19 reference statements)

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“…A previous work reported the automatic detection of hookworms based on an edge extraction network and classification network in 2016; however, although the preliminary results confirmed the ability of CNN to detect hookworms, consistent with other literature findings [ 2 ], fewer images of hookworms were used, limiting the ability further clinically validate the findings [ 25 ]. Since 2015, some studies have reported the effectiveness of the deep learning-based analysis of CE images for identifying intestinal lesions such as angioectasia, ulceration, erosion, polyps, hemorrhages, and protruding masses [ 9 , 11 ]. However, there have been no clinical studies or reports on intestinal parasites such as hookworms, roundworms, and tapeworms.…”

Section: Discussionmentioning

confidence: 99%

“…In order to reduce the burden on physicians and improve the efficiency and accuracy of endoscopic diagnosis, computer software technology has begun to be applied to this field. With the continuous development of the combination of computer software technology and endoscopic diagnosis [ 5 ], many computer-aided methods have been formed, and such methods are promising for the detection of many small intestinal abnormalities [ 6 – 8 ], such as bleeding [ 9 ], erosions [ 1 ], ulcerations [ 1 ], angioectasias [ 10 ], and protruding lesions [ 11 ], such as polyps, nodules, epithelial tumors, stromal tumors, and venous structures.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Small Intestinal Hookworms in Capsule Endoscopy Images Based on a Convolutional Neural Network

Gan

Yang

Liu

et al. 2021

Gastroenterology Research and Practice

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Ancylostomiasis is a fairly common small bowel parasite disease identified by capsule endoscopy (CE) for which a computer-aided clinical detection method has not been established. We sought to develop an artificial intelligence system with a convolutional neural network (CNN) to automatically detect hookworms in CE images. We trained a deep CNN system based on a YOLO-V4 (You Look Only Once-Version4) detector using 11236 CE images of hookworms. We assessed its performance by calculating the area under the receiver operating characteristic curve and its sensitivity, specificity, and accuracy using an independent test set of 10,529 small-bowel images including 531 images of hookworms. The trained CNN system required 403 seconds to evaluate 10,529 test images. The area under the curve for the detection of hookworms was 0.972 (95% confidence interval (CI), 0.967-0.978). The sensitivity, specificity, and accuracy of the CNN system were 92.2%, 91.1%, and 91.2%, respectively, at a probability score cut-off of 0.485. We developed and validated a CNN-based system for detecting hookworms in CE images. By combining this high-accuracy, high-speed, and oversight-preventing system with other CNN systems, we hope it will become an important supplement for detecting intestinal abnormalities in CE images. This trial is registered with ChiCTR2000034546 (a clinical research of artificial-intelligence-aided diagnosis for hookworms in small intestine by capsule endoscope images).

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“…35 To overcome these limitations, an increasing number of studies have already addressed the potential of AI in the field of CE. Recent advances in deep learning algorithms in CE have been summarized in a 2021 systematic review with meta-analysis by Mohan et al, 36 As regards the specific IBD focus of our systematic review, a full application of an AI system based on a hybrid adaptive filtering and differential lacunarity (HFA DLac) architecture was explored in 2016 by Charisis and Hadjileontiadis for describing and detecting CD-associated lesions in CE. 37 The system was trained with a CE image database containing 400 images depicting CD-related lesions with the lowest possible similarity and 400 lesion-free frames which were judged by two independent endoscopists.…”

Section: Capsule Endoscopymentioning

confidence: 99%

Artificial intelligence in gastrointestinal endoscopy for inflammatory bowel disease: a systematic review and new horizons

Tontini

Rimondi

Vernero³

et al. 2021

Therap Adv Gastroenterol

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Introduction: Since the advent of artificial intelligence (AI) in clinical studies, luminal gastrointestinal endoscopy has made great progress, especially in the detection and characterization of neoplastic and preneoplastic lesions. Several studies have recently shown the potential of AI-driven endoscopy for the investigation of inflammatory bowel disease (IBD). This systematic review provides an overview of the current position and future potential of AI in IBD endoscopy. Methods: A systematic search was carried out in PubMed and Scopus up to 2 December 2020 using the following search terms: artificial intelligence, machine learning, computer-aided, inflammatory bowel disease, ulcerative colitis (UC), Crohn’s disease (CD). All studies on human digestive endoscopy were included. A qualitative analysis and a narrative description were performed for each selected record according to the Joanna Briggs Institute methodologies and the PRISMA statement. Results: Of 398 identified records, 18 were ultimately included. Two-thirds of these (12/18) were published in 2020 and most were cross-sectional studies (15/18). No relevant bias at the study level was reported, although the risk of publication bias across studies cannot be ruled out at this early stage. Eleven records dealt with UC, five with CD and two with both. Most of the AI systems involved convolutional neural network, random forest and deep neural network architecture. Most studies focused on capsule endoscopy readings in CD ( n = 5) and on the AI-assisted assessment of mucosal activity in UC ( n = 10) for automated endoscopic scoring or real-time prediction of histological disease. Discussion: AI-assisted endoscopy in IBD is a rapidly evolving research field with promising technical results and additional benefits when tested in an experimental clinical scenario. External validation studies being conducted in large and prospective cohorts in real-life clinical scenarios will help confirm the added value of AI in assessing UC mucosal activity and in CD capsule reading. Plain language summary Artificial intelligence for inflammatory bowel disease endoscopy Artificial intelligence (AI) is a promising technology in many areas of medicine. In recent years, AI-assisted endoscopy has been introduced into several research fields, including inflammatory bowel disease (IBD) endoscopy, with promising applications that have the potential to revolutionize clinical practice and gastrointestinal endoscopy. We have performed the first systematic review of AI and its application in the field of IBD and endoscopy. A formal process of paper selection and analysis resulted in the assessment of 18 records. Most of these (12/18) were published in 2020 and were cross-sectional studies (15/18). No relevant biases were reported. All studies showed positive results concerning the novel technology evaluated, so the risk of publication bias cannot be ruled out at this early stage. Eleven records dealt with UC, five with CD and two with both. Most studies focused on capsule endoscopy reading in CD patients ( n = 5) and on AI-assisted assessment of mucosal activity in UC patients ( n = 10) for automated endoscopic scoring and real-time prediction of histological disease. We found that AI-assisted endoscopy in IBD is a rapidly growing research field. All studies indicated promising technical results. When tested in an experimental clinical scenario, AI-assisted endoscopy showed it could potentially improve the management of patients with IBD. Confirmatory evidence from real-life clinical scenarios should be obtained to verify the added value of AI-assisted IBD endoscopy in assessing UC mucosal activity and in CD capsule reading.

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“…Enlargement of application ranges: ANNs have been used for GI diagnosis, prognosis and risk prediction, and therapeutic guidance, achieving relatively high accuracies. Significance is limited to constructing more models that have been proposed in the same fields[ 184 , 185 ]. Future studies should focus on expanding ANN application ranges.…”

Section: Features Limitations and Future Perspectivesmentioning

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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“…In other words, the meta-analysis was based on only selected images, not fulllength videos. Evaluation of videos would afford some advantages: (1) The CNN threshold could be optimized. The thresholds used to report sensitivity and specificity vary; image-based studies cannot optimize these for real-world CE reading; and (2) the clinical utility of a CNN system could be directly compared with that of other software.…”

mentioning

confidence: 99%

The exceptional performance of deep learning for capsule endoscopy: Will such quality be maintained in clinical scenarios?

Aoki

Yamada

Koike

2021

Gastrointestinal Endoscopy

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Capsule endoscopy (CE) has revolutionized the investigation of various small-bowel abnormalities. About 10 devices fabricated by 5 companies are commercially available. As the capsule travels through the GI tract, thousands of pictures are automatically captured and transmitted to a recorder. Although the procedure is minimally invasive, physicians must review over 10,000 images per patient, which is obviously very time consuming. Physicians also fear the risk of oversight; any abnormality may be evident in only a few frames. Computer-aided diagnosis would be extremely helpful.Despite several attempts, reliability remains unacceptable. For example, the QuickView mode, originally used to remove uninformative CE images of the PillCam system, is not often used in practice because of unacceptably high miss rates for noteworthy abnormalities. Systems based on conventional machine-learning methods (eg, support vector machines, neural networks, or binary classifiers) are not yet commercially available, probably because they remain inaccurate. The difficulties include the relatively poor image quality caused by inaccurate focusing; light limitations; low resolution; the presence of bile, debris, and bubbles; and the fact that various types of abnormalities must be detected. Recently, state-of-the-art deep learning-based methods have significantly improved recognition performance in various medical fields and are expected to resolve the abovementioned problems in CE reading. Convolutional neural networks (CNNs) lead the field.In this systematic review with a meta-analysis in Gastrointestinal Endoscopy, Mohan et al 1 report a high pooled performance of CNN-based systems in terms of computer-aided diagnosis of GI ulcers and/or hemorrhage on CE images. From the 9 studies included in the final analysis, 20, 24, 23, 9, and 9 datasets were extracted to calculate accuracy, sensitivity, specificity, the positive predictive value, and the negative predictive value, respectively. All studies were retrospective in nature. The CNN systems exhibited a pooled accuracy of 95.4% (95% confidence interval [CI], 94.3-96.3), a sensitivity of 95.5% (95% CI, 94-96.5), a specificity of 95.8% (95% CI, 94.7-96.6), a positive predic-The abstracted data pertained to ulcers, nonbleeding angioectasias, and hemorrhages identified on CE images. Although these are the most common small-bowel abnormalities, CE can also identify other serious findings, including protruding lesions such as smallbowel adenocarcinomas, lymphomas, and polyps.

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High pooled performance of convolutional neural networks in computer-aided diagnosis of GI ulcers and/or hemorrhage on wireless capsule endoscopy images: a systematic review and meta-analysis

Cited by 21 publications

References 20 publications

Automatic Detection of Small Intestinal Hookworms in Capsule Endoscopy Images Based on a Convolutional Neural Network

Artificial intelligence in gastrointestinal endoscopy for inflammatory bowel disease: a systematic review and new horizons

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

The exceptional performance of deep learning for capsule endoscopy: Will such quality be maintained in clinical scenarios?

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