Recent Development of Computer Vision Technology to Improve Capsule Endoscopy

Park, Junseok; Hwang, Youngbae; Yoon, Ju Hong; Park, Min-Gyu; Kim, Jungho; Lim, Yun Jeong; Chun, Hoon Jai

doi:10.5946/ce.2018.172

Cited by 33 publications

(18 citation statements)

References 34 publications

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“…The calculating system should be based on clinically validated preparation scales and consistent with experienced CE readers’ assessment. Meanwhile, newly introduced deep leaning-based computational analysis of CE images allows more accurate detection of SB lesions with reduced reading time than conventional CE reading 19 – 22 . However, as long as the CE subsequently analyze passively obtained images, the performance of deep learning for lesion detection still depends on the quality of bowel preparation.…”

Section: Discussionmentioning

confidence: 99%

Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy

Nam

Hwang

et al. 2021

Sci Rep

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A standardized small bowel (SB) cleansing scale is currently not available. The aim of this study was to develop an automated calculation software for SB cleansing score using deep learning. Consecutively performed capsule endoscopy cases were enrolled from three hospitals. A 5-step scoring system based on mucosal visibility was trained for deep learning in the training set. Performance of the trained software was evaluated in the validation set. Average cleansing score (1.0 to 5.0) by deep learning was compared to clinical grading (A to C) reviewed by clinicians. Cleansing scores decreased as clinical grading worsened (scores of 4.1, 3.5, and 2.9 for grades A, B, and C, respectively, P < 0.001). Adequate preparation was achieved for 91.7% of validation cases. The average cleansing score was significantly different between adequate and inadequate group (4.0 vs. 2.9, P < 0.001). ROC curve analysis revealed that a cut-off value of cleansing score at 3.25 had an AUC of 0.977. Diagnostic yields for small, hard-to-find lesions were associated with high cleansing scores (4.3 vs. 3.8, P < 0.001). We developed a novel scoring software which calculates objective, automated cleansing scores for SB preparation. The cut-off value we suggested provides a standard criterion for adequate bowel preparation as a quality indicator.

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Section: Discussionmentioning

confidence: 99%

Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy

Nam

Hwang

et al. 2021

Sci Rep

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“…Detection of abnormal findings different from those of normal mucosa is crucial in the reading of CE images [ 15 ]. In this study, we achieved remarkable results via the deep learning-based AI to improve the lesion detection rates of reviewers.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence that determines the clinical significance of capsule endoscopy images can increase the efficiency of reading

Park¹,

Hwang²,

Nam³

et al. 2020

PLoS ONE

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Artificial intelligence (AI), which has demonstrated outstanding achievements in image recognition, can be useful for the tedious capsule endoscopy (CE) reading. We aimed to develop a practical AI-based method that can identify various types of lesions and tried to evaluate the effectiveness of the method under clinical settings. A total of 203,244 CE images were collected from multiple centers selected considering the regional distribution. The AI based on the Inception-Resnet-V2 model was trained with images that were classified into two categories according to their clinical significance. The performance of AI was evaluated with a comparative test involving two groups of reviewers with different experiences. The AI summarized 67,008 (31.89%) images with a probability of more than 0.8 for containing lesions in 210,100 frames of 20 selected CE videos. Using the AI-assisted reading model, reviewers in both the groups exhibited increased lesion detection rates compared to those achieved using the conventional reading model (experts; 34.3%–73.0%; p = 0.029, trainees; 24.7%–53.1%; p = 0.029). The improved result for trainees was comparable to that for the experts (p = 0.057). Further, the AI-assisted reading model significantly shortened the reading time for trainees (1621.0–746.8 min; p = 0.029). Thus, we have developed an AI-assisted reading model that can detect various lesions and can successfully summarize CE images according to clinical significance. The assistance rendered by AI can increase the lesion detection rates of reviewers. Especially, trainees could improve their efficiency of reading as a result of reduced reading time using the AI-assisted model.

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“…Regardless of promising initial results, there is room for improvements in detection rate, reduced manual labour, and AI explainability. Large amounts of data are needed 37 , 38 , particularly annotated data 35 , and access to these data are often scarce 39 . As shown in Table 1 , very few, small VCE datasets are made publicly available, and several have become unavailable.…”

Section: Background and Summarymentioning

confidence: 99%

Kvasir-Capsule, a video capsule endoscopy dataset

et al. 2021

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Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.

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Recent Development of Computer Vision Technology to Improve Capsule Endoscopy

Cited by 33 publications

References 34 publications

Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy

Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy

Artificial intelligence that determines the clinical significance of capsule endoscopy images can increase the efficiency of reading

Kvasir-Capsule, a video capsule endoscopy dataset

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