Computer vision and augmented reality in gastrointestinal endoscopy: Figure 1.

Mahmud, Nadim; Cohen, Jonah; Tsourides, Kleovoulos; Berzin, Tyler M.

doi:10.1093/gastro/gov027

Cited by 74 publications

(49 citation statements)

References 70 publications

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“…Colonoscopy is the gold-standard diagnostic tool for colorectal cancer (CRC) and precancerous lesions. However, the adenoma miss-rate (AMR) ranges from 6% to 27% [1]. It is noteworthy that precancerous lesions with a flat morphology, smooth surface, indistinct boundaries or isochromatic with background are more susceptible to be missed [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Evidence has shown that with a second party observing the monitor synchronously during colonoscopy, polyp detection rate could be improved by means of addressing the unrecognized polyps within the visual field [1]. However, in comparison with polypoid lesions, to detect LSTs and SSA/Ps is a more challenging task, it is likely that adding an additional human observer would not completely overcome the challenges.…”

Section: Introductionmentioning

confidence: 99%

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Computer aided detection for laterally spreading tumors and sessile serrated adenomas during colonoscopy

Zhou

Xiao

et al. 2020

PLoS ONE

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Background Evidence has shown that deep learning computer aided detection (CADe) system achieved high overall detection accuracy for polyp detection during colonoscopy. Aim The detection performance of CADe system on non-polypoid laterally spreading tumors (LSTs) and sessile serrated adenomas/polyps (SSA/Ps), with higher risk for malignancy transformation and miss rate, has not been exclusively investigated.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computer aided detection for laterally spreading tumors and sessile serrated adenomas during colonoscopy

Zhou

Xiao

et al. 2020

PLoS ONE

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“…A denomas are routinely missed during colonoscopy by individual endoscopists. 1 Although colonoscopy remains the gold standard for screening cancer and precancerous lesions in the colon, 2 colonoscopy can be technically demanding because it requires both manipulation and observation at the same time, and there is significant variation in how colonoscopy is performed and how lesions are detected between individual endoscopists.…”

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confidence: 99%

“…Such lesions could be better exposed by means of high-quality bowel cleansing, endoscopic cameras with wider viewing angles, and meticulous mucosal inspection techniques. 1 However, the adenoma miss rate (AMR) still ranges from 6% to 41% using white-light colonoscopy. 1,3 Studies using full-spectrum colonoscopy (FUSE), which provides 330 angle of view, show an AMR of between 7.0% 4 and 20.5%.…”

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confidence: 99%

Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study

et al. 2020

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See Covering the Cover synopsis on page 1193. BACKGROUND AND AIMS: Up to 30% of adenomas might be missed during screening colonoscopy-these could be polyps that appear on-screen but are not recognized by endoscopists or polyps that are in locations that do not appear on the screen at all. Computer-aided detection (CADe) systems, based on deep learning, might reduce rates of missed adenomas by displaying visual alerts that identify precancerous polyps on the endoscopy monitor in real time. We compared adenoma miss rates of CADe colonoscopy vs routine white-light colonoscopy. METHODS: We performed a prospective study of patients, 18-75 years old, referred for diagnostic, screening, or surveillance colonoscopies at a single endoscopy center of Sichuan Provincial People's Hospital from June 3, 2019 through September 24, 2019. Same day, tandem colonoscopies were performed for each participant by the same endoscopist. Patients were randomly assigned to groups that received either CADe colonoscopy (n¼184) or routine colonoscopy (n¼185) first, followed immediately by the other procedure. Endoscopists were blinded to the group each patient was assigned to until immediately before the start of each colonoscopy. Polyps that were missed by the CADe system but detected by endoscopists were classified as missed polyps. False polyps were those continuously traced by the CADe system but then determined not to be polyps by the endoscopists. The primary endpoint was adenoma miss rate, which was defined as the number of adenomas detected in the second-pass colonoscopy divided by the total number of adenomas detected in both passes. RESULTS: The adenoma miss rate was significantly lower with CADe colonoscopy (13.89%; 95% CI, 8.24%-19.54%) than with routine colonoscopy (40.00%; 95% CI, 31.23%-48.77%, P<.0001). The polyp miss rate was significantly lower with CADe colonoscopy (12.98%; 95% CI, 9.08%-16.88%) than with routine colonoscopy (45.90%; 95% CI, 39.65%-52.15%) (P<.0001). Adenoma miss rates in ascending, transverse, and descending colon were significantly lower with CADe colonoscopy than with routine colonoscopy (ascending colon 6.67% vs 39.13%; P¼.0095; transverse colon 16.33% vs 45.16%; P¼.0065; and descending colon 12.50% vs 40.91%, P¼.0364). CONCLUSIONS: CADe colonoscopy reduced the overall miss rate of adenomas by endoscopists using white-light endoscopy. Routine use of CADe might reduce the incidence of interval colon cancers. chictr.org.cn study no: ChiCTR1900023086

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“…After that, there were more CAD applications based on specific databases [8][9][10][11], but the number of images in these databases was limited, mostly less than 20. In 2015, Tyler Berzin's team put forward the product idea of AI assisted endoscopy diagnosis [12].…”

Section: Introductionmentioning

confidence: 99%

An improved deep learning approach and its applications on colonic polyp images detection

et al. 2020

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Background: Colonic polyps are more likely to be cancerous, especially those with large diameter, large number and atypical hyperplasia. If colonic polyps cannot be treated in early stage, they are likely to develop into colon cancer. Colonoscopy is easily limited by the operator's experience, and factors such as inexperience and visual fatigue will directly affect the accuracy of diagnosis. Cooperating with Hunan children's hospital, we proposed and improved a deep learning approach with global average pooling (GAP) in colonoscopy for assisted diagnosis. Our approach for assisted diagnosis in colonoscopy can prompt endoscopists to pay attention to polyps that may be ignored in real time, improve the detection rate, reduce missed diagnosis, and improve the efficiency of medical diagnosis. Methods: We selected colonoscopy images from the gastrointestinal endoscopy room of Hunan children's hospital to form the colonic polyp datasets. And we applied the image classification method based on Deep Learning to the classification of Colonic Polyps. The classic networks we used are VGGNets and ResNets. By using global average pooling, we proposed the improved approaches: VGGNets-GAP and ResNets-GAP. Results: The accuracies of all models in datasets exceed 98%. The TPR and TNR are above 96 and 98% respectively. In addition, VGGNets-GAP networks not only have high classification accuracies, but also have much fewer parameters than those of VGGNets. Conclusions: The experimental results show that the proposed approach has good effect on the automatic detection of colonic polyps. The innovations of our method are in two aspects: (1) the detection accuracy of colonic polyps has been improved. (2) our approach reduces the memory consumption and makes the model lightweight. Compared with the original VGG networks, the parameters of our VGG19-GAP networks are greatly reduced.

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Computer vision and augmented reality in gastrointestinal endoscopy: Figure 1.

Cited by 74 publications

References 70 publications

Computer aided detection for laterally spreading tumors and sessile serrated adenomas during colonoscopy

Computer aided detection for laterally spreading tumors and sessile serrated adenomas during colonoscopy

Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study

An improved deep learning approach and its applications on colonic polyp images detection

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