Artificial intelligence for the assessment of bowel preparation

Lee, Ji Young; Calderwood, Audrey H.; Karnes, William E.; Requa, James; Jacobson, Brian C.; Wallace, Michael B.

doi:10.1016/j.gie.2021.11.041

Cited by 20 publications

(10 citation statements)

References 12 publications

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“…The application of artificial intelligence (AI) algorithms to improve the effectiveness and caliber of bowel cleansing before colonoscopy examinations is one notable development. The effectiveness of an AI‐driven approach in streamlining bowel preparation processes was shown in a study published in the Journal of Gastrointestinal Endoscopy 47 . This led to enhanced colon visualization and higher rates of colorectal lesion diagnosis.…”

Section: Discussionmentioning

confidence: 99%

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Split doses versus whole dose bowel preparation using polyethylene glycol for colonoscopy: A multicentric prospective Lebanese randomized trial between 2021 and 2023

Sara,

Ghinwa,

Layla

et al. 2024

Health Science Reports

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Background and AimsBowel preparation is considered as major obstacle before colonoscopy, and it is often reported as the most feared part of the procedure. The aim of this study is to determine the difference in efficacy between a split dose of PEG and the previous day regimen in cleaning the colon, using Boston bowel preparation scale. In addition, also to evaluate patient satisfaction regarding the modality of preparation.MethodsThe study included 200 hospitalized patients undergoing colonoscopy at Beirut hospitals between 2021 and 2023. One of the two regimens will be prescribed randomly to the patients before colonoscopy: 98 (49%) in Group A (patients treated with PEG preparation as a split dose for 2 days), and 102 (51%) in Group B (patients taking PEG preparation as a whole dose). Data was analyzed using SPSS version 25.ResultsPatients were distributed between 105 (52.5%) males and 95 (47.5%) females. The top two indications for colonoscopy were bleeding (34%), change in bowel habits (constipation/diarrhea) (32%). Patients experienced adverse events noting cramps (48.5%), stomach ache (32%), headache (31%), vomiting (53%), nausea (53%), sleep disturbance (27%), bloating (26.5%), and malaise (26%). A statistically significant difference (p = 0.040) was detected in sleep disturbance: 20.4% of patients in group A and 33.3% of patients in group B. The average satisfaction score was 3.02 ± 1.03 over 4 (Group A) and 3.04 ± 0.99 over 4 (Group B) (p = 0.896). The average BBPS was 8.07 ± 1.14 (Group A) and 8.28 ± 1.0 (Group B) (p = 0.162).ConclusionThe two administrations were almost similar in term of satisfaction and BBPS. As multiple factors like age, sexe, comorbidities may contribute in altering how much a given drug is safe and efficace, more research is needed to choose the best 3regimen for each patient.

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Section: Discussionmentioning

confidence: 99%

“…The effectiveness of an AI‐driven approach in streamlining bowel preparation processes was shown in a study published in the Journal of Gastrointestinal Endoscopy. 47 This led to enhanced colon visualization and higher rates of colorectal lesion diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Split doses versus whole dose bowel preparation using polyethylene glycol for colonoscopy: A multicentric prospective Lebanese randomized trial between 2021 and 2023

Sara,

Ghinwa,

Layla

et al. 2024

Health Science Reports

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“…Several studies have been carried out to train and validate CNNs for detecting bowel cleansing during colonoscopy based on validated cleansing scales [ 75 ]. These systems can also overcome the limitations of interobserver variability in rating colon cleansing during colonoscopy.…”

Section: Computer-aided Polyp Detection (Cade)mentioning

confidence: 99%

Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward?

Gimeno–García

Hernández-Pérez

Díaz

et al. 2023

Cancers

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Growing evidence indicates that artificial intelligence (AI) applied to medicine is here to stay. In gastroenterology, AI computer vision applications have been stated as a research priority. The two main AI system categories are computer-aided polyp detection (CADe) and computer-assisted diagnosis (CADx). However, other fields of expansion are those related to colonoscopy quality, such as methods to objectively assess colon cleansing during the colonoscopy, as well as devices to automatically predict and improve bowel cleansing before the examination, predict deep submucosal invasion, obtain a reliable measurement of colorectal polyps and accurately locate colorectal lesions in the colon. Although growing evidence indicates that AI systems could improve some of these quality metrics, there are concerns regarding cost-effectiveness, and large and multicentric randomized studies with strong outcomes, such as post-colonoscopy colorectal cancer incidence and mortality, are lacking. The integration of all these tasks into one quality-improvement device could facilitate the incorporation of AI systems in clinical practice. In this manuscript, the current status of the role of AI in colonoscopy is reviewed, as well as its current applications, drawbacks and areas for improvement.

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“…The accuracy of these AI models applied to images can reach 93.3%–95.3% but decrease to 88.6%–89.0% in real colonoscopy video evaluation ( 23 , 24 ). Another image-based model developed by Lee had 85.3% accuracy in discriminating inadequate bowel preparation in the validation set of 10 s videos ( 25 ). One possible reason for the relatively low accuracy of video evaluation may be due to the video data samples not being learned by the system.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a three-dimensional deep learning-based system for assessing bowel preparation on colonoscopy video

Feng,

Xu,

et al. 2023

Front. Med.

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BackgroundThe performance of existing image-based training models in evaluating bowel preparation on colonoscopy videos was relatively low, and only a few models used external data to prove their generalization. Therefore, this study attempted to develop a more precise and stable AI system for assessing bowel preparation of colonoscopy video.MethodsWe proposed a system named ViENDO to assess the bowel preparation quality, including two CNNs. First, Information-Net was used to identify and filter out colonoscopy video frames unsuitable for Boston bowel preparation scale (BBPS) scoring. Second, BBPS-Net was trained and tested with 5,566 suitable short video clips through three-dimensional (3D) convolutional neural network (CNN) technology to detect BBPS-based insufficient bowel preparation. Then, ViENDO was applied to complete withdrawal colonoscopy videos from multiple centers to predict BBPS segment scores in clinical settings. We also conducted a human-machine contest to compare its performance with endoscopists.ResultsIn video clips, BBPS-Net for determining inadequate bowel preparation generated an area under the curve of up to 0.98 and accuracy of 95.2%. When applied to full-length withdrawal colonoscopy videos, ViENDO assessed bowel cleanliness with an accuracy of 93.8% in the internal test set and 91.7% in the external dataset. The human-machine contest demonstrated that the accuracy of ViENDO was slightly superior compared to most endoscopists, though no statistical significance was found.ConclusionThe 3D-CNN-based AI model showed good performance in evaluating full-length bowel preparation on colonoscopy video. It has the potential as a substitute for endoscopists to provide BBPS-based assessments during daily clinical practice.

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Artificial intelligence for the assessment of bowel preparation

Cited by 20 publications

References 12 publications

Split doses versus whole dose bowel preparation using polyethylene glycol for colonoscopy: A multicentric prospective Lebanese randomized trial between 2021 and 2023

Split doses versus whole dose bowel preparation using polyethylene glycol for colonoscopy: A multicentric prospective Lebanese randomized trial between 2021 and 2023

Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward?

Development and validation of a three-dimensional deep learning-based system for assessing bowel preparation on colonoscopy video

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