Artificial Intelligence in Capsule Endoscopy: A Practical Guide to Its Past and Future Challenges

Kim, Sang Hoon; Lim, Yun Jeong

doi:10.3390/diagnostics11091722

Cited by 27 publications

(14 citation statements)

References 53 publications

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“…Various algorithms, such as automated disease detection [ 6 , 24 ], a frame reduction system [ 25 , 26 ], cleansing score determination [ 27 ], and 3D reconstruction for the small bowel [ 28 ], have been developed to reduce the WCE reading time or increase convenience of reading. It is expected that these technical advances will greatly reduce the clinician's reading time and fully automate the clinician's diagnosis process, but there are several challenges, one of which is that the small bowel region must be extracted before those algorithms are applied.…”

Section: Discussionmentioning

confidence: 99%

“…Since wireless capsule endoscopy (WCE) was first introduced in 2000 [ 1 ], small bowel capsule endoscopy (SBCE) has become a major modality for the diagnosis of various small bowel diseases because of its painless and non-invasive nature [ 2 , 3 , 4 , 5 ]. However, reading numerous image frames is a time-consuming and tedious task for clinicians [ 6 ]. In addition, there may be variations among readers and fatigue may also affect diagnostic accuracy [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering

Son

et al. 2022

Diagnostics

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By automatically classifying the stomach, small bowel, and colon, the reading time of the wireless capsule endoscopy (WCE) can be reduced. In addition, it is an essential first preprocessing step to localize the small bowel in order to apply automated small bowel lesion detection algorithms based on deep learning. The purpose of the study was to develop an automated small bowel detection method from long untrimmed videos captured from WCE. Through this, the stomach and colon can also be distinguished. The proposed method is based on a convolutional neural network (CNN) with a temporal filtering on the predicted probabilities from the CNN. For CNN, we use a ResNet50 model to classify three organs including stomach, small bowel, and colon. The hybrid temporal filter consisting of a Savitzky–Golay filter and a median filter is applied to the temporal probabilities for the “small bowel” class. After filtering, the small bowel and the other two organs are differentiated with thresholding. The study was conducted on dataset of 200 patients (100 normal and 100 abnormal WCE cases), which was divided into a training set of 140 cases, a validation set of 20 cases, and a test set of 40 cases. For the test set of 40 patients (20 normal and 20 abnormal WCE cases), the proposed method showed accuracy of 99.8% in binary classification for the small bowel. Transition time errors for gastrointestinal tracts were only 38.8 ± 25.8 seconds for the transition between stomach and small bowel and 32.0 ± 19.1 seconds for the transition between small bowel and colon, compared to the ground truth organ transition points marked by two experienced gastroenterologists.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering

Son

et al. 2022

Diagnostics

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“…From 2007 to 2020, AI has developed significantly in terms of performance by the mechanism for detecting pathologies (here color-coding to detect bleeding), type, sensitivity, specificity, accuracy, low computational cost, reporting time, and applicability in daily practice [ 48 ]. However, one of the future challenges AI faces is to develop a methodology for quantifying the uncertainty of AI, which considers both the dataset and the reliability of the inner algorithmic intricacies [ 48 , 49 ].…”

Section: Reviewmentioning

confidence: 99%

Obscure Gastrointestinal Bleeding and Capsule Endoscopy: A Win-Win Situation or Not?

Patel¹,

Vedantam²,

Poman³

et al. 2022

Cureus

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Obscure gastrointestinal bleeding (OGIB) refers to bleeding of uncertain origin that persists or recurs after negative workup using any of the radiologic evaluation modalities. It can be divided into two types based on whether clinically evident bleeding is present, namely, obscure overt and obscure occult bleeding. As the visualization of the bowel mucosa is challenging, capsule endoscopy (CE) is the ideal go-to procedure as the process is wireless, ingestible, small, disposable, and, most importantly, non-invasive. This review article has compiled various studies to shed light on the guidelines for using CE, its structure and procedure, patient preferences, diagnostic yield, cost-effectiveness, and the future. The goal of this review is to show the influence of CE on OGIB on the aspects mentioned earlier.

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“…This is formalized and analyzed in [12,13] works, where the difficulties derived from imbalance, low inter-class variance, and high inter-class variance are inspected in detail. Techniques like dropout, L1 or L2 regularization, and sampling mechanisms have been applied to attempt to soften the problem [14].…”

Section: Related Workmentioning

confidence: 99%

Time-based Self-supervised Learning for Wireless Capsule Endoscopy

Pascual,

Laiz,

García

et al. 2022

Preprint

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State-of-the-art machine learning models, and especially deep learning ones, are significantly data-hungry; they require vast amounts of manually labeled samples to function correctly. However, in most medical imaging fields, obtaining said data can be challenging. Not only the volume of data is a problem, but also the imbalances within its classes; it is common to have many more images of healthy patients than of those with pathology. Computer-aided diagnostic systems suffer from these issues, usually over-designing their models to perform accurately. This work proposes using self-supervised learning for wireless endoscopy videos by introducing a custom-tailored method that does not initially need labels or appropriate balance. We prove that using the inferred inherent structure learned by our method, extracted from the temporal axis, improves the detection rate on several domain-specific applications even under severe imbalance.

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Artificial Intelligence in Capsule Endoscopy: A Practical Guide to Its Past and Future Challenges

Cited by 27 publications

References 53 publications

Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering

Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering

Obscure Gastrointestinal Bleeding and Capsule Endoscopy: A Win-Win Situation or Not?

Time-based Self-supervised Learning for Wireless Capsule Endoscopy

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