A Rapid and Accurate Method to Detect Active Small Bowel Gastrointestinal Bleeding on Video Capsule Endoscopy

Stein, Adam C.; Appannagari, Anoop; Habib, Ibrahim; Semrad, Carol E.; Rubin, David T.

doi:10.1007/s10620-014-3327-8

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…Previously, Stein et al utilized the Quickview function in the RAPID Reader software v6.0 in conjunction with the SBI function and found an overall sensitivity of 100% in detecting 28 cases of active small bowel bleeding [ 7 ]. Tal et al utilized the SBI function alone and also found 100% sensitivity in detecting 42 cases of active small bowel bleeding, with a mean of 46.6 SBI bars in major bleeding cases and a mean of 36 SBI bars in minor bleeding cases [ 8 ].…”

Section: Discussionmentioning

confidence: 99%

Suspected Blood Indicator to Identify Active Gastrointestinal Bleeding: A Prospective Validation

Han¹,

Fahed²,

Cave³

2018

Gastroenterol Res

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BackgroundThe suspected blood indicator (SBI) function in the RAPID Reader v8.3 program was designed to quickly identify the presence of blood in video capsule endoscopy. While previous retrospective studies have shown that the SBI function was accurate in detecting the presence of active bleeding in the small bowel, its specificity and sensitivity were poor.MethodsAn initial retrospective review (phase 1) compared 115 patients with active gastrointestinal bleeding seen on video capsule endoscopy (VCE) to 115 patients with no active bleeding seen on VCE to produce a highly accurate algorithm. A prospective study (phase 2) was then performed by applying the algorithm to 100 consecutive patients who received VCE for the following indications: obscure bleeding, iron deficiency anemia, melena, and hematochezia.ResultsThe initial retrospective review found that eight contiguous SBI markers had a specificity of 100% in identifying active gastrointestinal bleeding regardless of the total number of SBI markers, while two or more contiguous SBI markers had a sensitivity of 96.5%. Using a cutoff of eight contiguous SBI markers, the prospective arm found that there was a 100% sensitivity and specificity in detecting active gastrointestinal bleeding (P < 0.001).ConclusionsThe SBI function can greatly facilitate the identification of active gastrointestinal bleeding on VCE by using eight contiguous SBI markers as a cutoff for active bleeding.

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Section: Discussionmentioning

confidence: 99%

Suspected Blood Indicator to Identify Active Gastrointestinal Bleeding: A Prospective Validation

Han¹,

Fahed²,

Cave³

2018

Gastroenterol Res

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“…In order to reduce the possibility of missing lesions and the reading time required, several software modes have been developed including the Blood indicator 10 11 12 , Blue mode 13 14 , Flexible spectral Imaging Color Enhancement (FICE) 15 , Automatic mode 7 , and Quickview 8 16 . The Blood indicator selects red (blood) images 10 11 12 while Blue mode enhances lesions that are blue 14 15 . FICE is based on the arithmetic narrowing of the bandwidth of a conventional endoscopic image using computerized spectral estimation technology to enhance the lesion, thus simplifying detection 17 .…”

Section: Discussionmentioning

confidence: 99%

Evaluation of performance of the Omni mode for detecting video capsule endoscopy images: A multicenter randomized controlled trial

et al. 2016

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Background and study aims: Olympus recently developed a new algorithm called Omni mode that discards redundant video capsule endoscopy (VCE) images. The current study aimed to demonstrate the non-inferiority of the Omni mode in terms of true positives (TPs) and the superiority of the Omni mode with regard to reading time against a control (ordinary ES-10 system). Patients and methods: This multicenter prospective study included 40 patients with various small bowel diseases. VCE images were evaluated by 7 readers and 3 judging committee members. Two randomly allocated readers assessed the VCE images obtained using the 2 modalities for each patient. The order of the modalities was switched between the 2 readers and the interval between readings by the same reader was 2 weeks. The judging committee predefined clinically relevant lesions as major lesions and irrelevant lesions as minor lesions. The number of TPs for major and minor lesions and the reading times were compared between the modalities. The predefined non-inferiority margin for the TP ratio of the Omni mode compared with the control was 0.9. Results: The estimated TP ratios and 95 % confidence intervals for total, major, and minor lesions were 0.87 (0.80 – 0.95), 0.93 (0.83 – 1.04), and 0.83 (0.74 – 0.94), respectively. Although non-inferiority was not demonstrated, the rate of detection of major lesions was not significantly different between the modalities. The reading time was significantly lower when using the Omni mode than when using the control. Conclusions: The Omni mode may be only appropriate for the assessment of major lesions.

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“…To overcome SBCE limitations and reduce reporting time while reviewing SBCEs, several artificial intelligence-based viewing modes are currently available on SBCE reading platforms [54][55][56]. Quickview automatically skips similar images and the suspected blood indicator (SBI) is a pixel analysis programme that identifies suspected blood.…”

Section: Challenges Associated With Artificial Intelligencementioning

confidence: 99%

Artificial intelligence within the small bowel: are we lagging behind?

Zammit

Sidhu

2022

Current Opinion in Gastroenterology

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Purpose of reviewThe use of artificial intelligence in small bowel capsule endoscopy is expanding. This review focusses on the use of artificial intelligence for small bowel pathology compared with human data and developments to date. Recent findingsThe diagnosis and management of small bowel disease has been revolutionized with the advent of capsule endoscopy. Reading of capsule endoscopy videos however is time consuming with an average reading time of 40 min. Furthermore, the fatigued human eye may miss subtle lesions including indiscreet mucosal bulges. In recent years, artificial intelligence has made significant progress in the field of medicine including gastroenterology. Machine learning has enabled feature extraction and in combination with deep neural networks, image classification has now materialized for routine endoscopy for the clinician.

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A Rapid and Accurate Method to Detect Active Small Bowel Gastrointestinal Bleeding on Video Capsule Endoscopy

Abstract: The QV + SBI reading format of VCE is an efficient, highly sensitive modality to assess for potential small bowel bleeding.

Cited by 8 publications

References 12 publications

Suspected Blood Indicator to Identify Active Gastrointestinal Bleeding: A Prospective Validation

Suspected Blood Indicator to Identify Active Gastrointestinal Bleeding: A Prospective Validation

Evaluation of performance of the Omni mode for detecting video capsule endoscopy images: A multicenter randomized controlled trial

Artificial intelligence within the small bowel: are we lagging behind?

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