A Novel Convolutional Neural Network Model as an Alternative Approach to Bowel Preparation Evaluation Before Colonoscopy in the COVID-19 Era: A Multicenter, Single-Blinded, Randomized Study

Vision-language large learning model, GPT4V, accurately classifies the Boston Bowel Preparation Scale score

INTRODUCTION

Large learning models (LLMs) such as GPT are advanced artificial intelligence (AI) models. Originally developed for natural language processing, they have been adapted for multi-modal tasks with vision-language input. One clinically relevant task is scoring the Boston Bowel Preparation Scale (BBPS). While traditional AI techniques use large amounts of data for training, we hypothesise that vision-language LLM can perform this task with fewer examples.

METHODS

We used the GPT4V vision-language LLM developed by OpenAI, via the OpenAI application programming interface. A standardised prompt instructed the model to grade BBPS with contextual references extracted from the original paper describing the BBPS by Lai et al (GIE 2009). Performance was tested on the HyperKvasir dataset, an open dataset for automated BBPS grading.

RESULTS

Of 1794 images, GPT4V returned valid results for 1772 (98%). It had an accuracy of 0.84 for two-class classification (BBPS 0-1 vs 2-3) and 0.74 for four-class classification (BBPS 0, 1, 2, 3). Macro-averaged F1 scores were 0.81 and 0.63, respectively. Qualitatively, most errors arose from misclassification of BBPS 1 as 2. These results compare favourably with current methods using large amounts of training data, which achieve an accuracy in the range of 0.8-0.9.

CONCLUSION

This study provides proof-of-concept that a vision-language LLM is able to perform BBPS classification accurately, without large training datasets. This represents a paradigm shift in AI classification methods in medicine, where many diseases lack sufficient data to train traditional AI models. An LLM with appropriate examples may be used in such cases.

Interventions to improve bowel cleansing in colonoscopy

INTRODUCTION

Suboptimal bowel preparation adversely affects colonoscopy quality, increases healthcare costs, and prolongs waiting time. The primary contributing factors include poor tolerance to the preparation solutions, noncompliance with prescribed instructions, and suboptimal efficacy of the bowel cleansing solution itself.

AREAS COVERED

This review examined the predictive factors associated with suboptimal bowel preparation and discussed interventions aimed at improving bowel cleansing. It also provides evidence-based practical algorithms supplemented by insights from our own clinical experience. Relevant topics were reviewed using resources from the PubMed database.

EXPERT OPINION

Although current bowel preparation protocols are effective for the majority of patients, a significant proportion still present challenges for optimal preparation. These patients may benefit from personalized strategies tailored to the specific causes of preparation failure. Conducting a thorough interview is crucial for identifying the reasons for failure, particularly in patients who have previously experienced suboptimal preparation during colonoscopy. In colonoscopy-naïve patients, it is essential to assess the risk of suboptimal preparation. In both cases, interventions should be customized to either address the identified causes in the former group or employ preventive strategies to reduce the likelihood of failure in the latter.

Artificial Intelligence-Assisted Colonoscopy for Polyp Detection : A Systematic Review and Meta-analysis

BACKGROUND

Randomized clinical trials (RCTs) of computer-aided detection (CADe) system-enhanced colonoscopy compared with conventional colonoscopy suggest increased adenoma detection rate (ADR) and decreased adenoma miss rate (AMR), but the effect on detection of advanced colorectal neoplasia (ACN) is unclear.

PURPOSE

To conduct a systematic review to compare performance of CADe-enhanced and conventional colonoscopy.

DATA SOURCES

Cochrane Library, Google Scholar, Ovid EMBASE, Ovid MEDLINE, PubMed, Scopus, and Web of Science Core Collection databases were searched through February 2024.

STUDY SELECTION

Published RCTs comparing CADe-enhanced and conventional colonoscopy.

DATA EXTRACTION

Average adenoma per colonoscopy (APC) and ACN per colonoscopy were primary outcomes. Adenoma detection rate, AMR, and ACN detection rate (ACN DR) were secondary outcomes. Balancing outcomes included withdrawal time and resection of nonneoplastic polyps (NNPs). Subgroup analyses were done by neural network architecture.

DATA SYNTHESIS

Forty-four RCTs with 36 201 cases were included. Computer-aided detection-enhanced colonoscopies have higher average APC (12 090 of 12 279 [0.98] vs. 9690 of 12 292 [0.78], incidence rate difference [IRD] = 0.22 [95% CI, 0.16 to 0.28]) and higher ADR (7098 of 16 253 [44.7%] vs. 5825 of 15 855 [36.7%], rate ratio [RR] = 1.21 [CI, 1.15 to 1.28]). Average ACN per colonoscopy was similar (1512 of 9296 [0.16] vs. 1392 of 9121 [0.15], IRD = 0.01 [CI, -0.01 to 0.02]), but ACN DR was higher with CADe system use (1260 of 9899 [12.7%] vs. 1119 of 9746 [11.5%], RR = 1.16 [CI, 1.02 to 1.32]). Using CADe systems resulted in resection of almost 2 extra NNPs per 10 colonoscopies and longer total withdrawal time (0.53 minutes [CI, 0.30 to 0.77]).

LIMITATION

Statistically significant heterogeneity in quality and sample size and inability to blind endoscopists to the intervention in included studies may affect the performance estimates.

CONCLUSION

Computer-aided detection-enhanced colonoscopies have increased APC and detection rate but no difference in ACN per colonoscopy and a small increase in ACN DR. There is minimal increase in procedure time and no difference in performance across neural network architectures.

PRIMARY FUNDING SOURCE

None. (PROSPERO: CRD42023422835).

Conversational LLM Chatbot ChatGPT-4 for Colonoscopy Boston Bowel Preparation Scoring: An Artificial Intelligence-to-Head Concordance Analysis

BACKGROUND/OBJECTIVES

To date, no studies have evaluated Chat Generative Pre-Trained Transformer (ChatGPT) as a large language model chatbot in optical applications for digestive endoscopy images. This study aimed to weigh the performance of ChatGPT-4 in assessing bowel preparation (BP) quality for colonoscopy.

METHODS

ChatGPT-4 analysed 663 anonymised endoscopic images, scoring each according to the Boston BP scale (BBPS). Expert physicians scored the same images subsequently.

RESULTS

ChatGPT-4 deemed 369 frames (62.9%) to be adequately prepared (i.e., BBPS > 1) compared to 524 frames (89.3%) assessed by human assessors. The agreement was slight (κ: 0.099, p = 0.0001). The raw human BBPS score was higher at 3 (2-3) than that of ChatGPT-4 at 2 (1-3), demonstrating moderate concordance (W: 0.554, p = 0.036).

CONCLUSIONS

ChatGPT-4 demonstrates some potential in assessing BP on colonoscopy images, but further refinement is still needed.

Text Message System for the Prediction of Colonoscopy Bowel Preparation Adequacy Before Colonoscopy: An Artificial Intelligence Image Classification Algorithm Based on Images of Stool Output

Background and Aims

Inadequate bowel preparation which occurs in 25% of colonoscopies is a major barrier to the effectiveness of screening for colorectal cancer. We aim to develop an artificial intelligence (machine learning) algorithm to assess photos of stool output after bowel preparation to predict inadequate bowel preparation before colonoscopy.

Methods

Patients were asked to text a photo of their stool in the commode when they believed that they neared completion of their colonoscopy bowel preparation. Boston Bowel Preparation Scores of 7 and below were labeled as inadequate or fair. Boston Bowel Preparation Scores of 8 and 9 were considered good. A binary classification image-based machine learning algorithm was designed.

Results

In a test set of 61 images, the binary classification machine learning algorithm was able to distinguish inadequate/fair preparation from good preparation with a positive predictive value of 78.6% and a negative predictive value of 60.8%. In a test set of 56 images, the algorithm was able to distinguish normal colonoscopy duration (<25 minutes) from long colonoscopy duration (>25 minutes) with a positive predictive value of 78.6% and a negative predictive value of 65.5%.

Conclusion

Patients are willing to submit photos of their stool output during bowel preparation through text messages before colonoscopy. This machine learning algorithm demonstrates the ability to predict inadequate/fair preparation from good preparation based on image classification of stool output. It was less accurate to predict long duration of colonoscopy.

Risk factors for inadequate bowel preparation before colonoscopy: A meta-analysis

OBJECTIVE

This meta-analysis aimed to comprehensively explore the risk factors for inadequate bowel preparation (IBP).

METHODS

We searched the Embase, PubMed, Web of Science, and The Cochrane Library databases up to August 24, 2023, to identify observational studies and randomized controlled trials (RCTs) that examined risk factors for IBP. A random effects model was used to pool the adjusted odds ratios and 95% confidence intervals.

RESULTS

A total of 125 studies (91 observational studies, 34 RCTs) were included. Meta-analyses of observational studies revealed that three preparation-related factors, namely, characteristics of last stool (solid or brown liquid), incomplete preparation intake, and incorrect diet restriction, were strong predictors of IBP. The other factors were moderately correlated with IBP incidence, including demographic variables (age, body mass index, male sex, Medicaid insurance, and current smoking), comorbidities (diabetes, liver cirrhosis, psychiatric disease, Parkinson's disease, previous IBP, poor mobility, inpatient, and Bristol stool form 1/2), medications (tricyclic antidepressants, opioids, antidepressants, narcotics, antipsychotics, and calcium channel blockers), and preparation-related factors (preparation-to-colonoscopy interval not within 3 to 5/6 h, nonsplit preparation, and preparation instructions not followed). No colonoscopy indications were found to be related to IBP. Meta-analyses of RCTs showed that education, constipation, stroke/dementia, and discomfort during preparation were also moderately associated with IBP. Most of the other findings were consistent with the pooled results of observational studies. However, primarily due to imprecision and inconsistency, the certainty of evidence for most factors was very low to moderate.

CONCLUSIONS

We summarized five categories of risk factors for IBP. Compared to demographic variables, comorbidities, medications, and colonoscopy indications, preparation-related elements were more strongly associated with IBP. These findings may help clinicians identify high-risk individuals and provide guidance for IBP prevention.

Design and validation of an artificial intelligence system to detect the quality of colon cleansing before colonoscopy

BACKGROUND AND AIMS

Patients' perception of their bowel cleansing quality may guide rescue cleansing strategies before colonoscopy. The main aim of this study was to train and validate a convolutional neural network (CNN) for classifying rectal effluent during bowel preparation intake as "adequate" or "inadequate" cleansing before colonoscopy.

PATIENTS AND METHODS

Patients referred for outpatient colonoscopy were asked to provide images of their rectal effluent during the bowel preparation process. The images were categorized as adequate or inadequate cleansing based on a predefined 4-picture quality scale. A total of 1203 images were collected from 660 patients. The initial dataset (799 images), was split into a training set (80%) and a validation set (20%). The second dataset (404 images) was used to develop a second test of the CNN accuracy. Afterward, CNN prediction was prospectively compared with the Boston Bowel Preparation Scale (BBPS) in 200 additional patients who provided a picture of their last rectal effluent.

RESULTS

On the initial dataset, a global accuracy of 97.49%, a sensitivity of 98.17% and a specificity of 96.66% were obtained using the CNN model. On the second dataset, an accuracy of 95%, a sensitivity of 99.60% and a specificity of 87.41% were obtained. The results from the CNN model were significantly associated with those from the BBPS (P<0.001), and 77.78% of the patients with poor bowel preparation were correctly classified.

CONCLUSION

The designed CNN is capable of classifying "adequate cleansing" and "inadequate cleansing" images with high accuracy.

Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77-94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.

Colon Bowel Preparation in the Era of Artificial Intelligence: Is There Potential for Enhancing Colon Bowel Cleansing?

BACKGROUND AND OBJECTIVES

Proper bowel preparation is of paramount importance for enhancing adenoma detection rates and reducing postcolonoscopic colorectal cancer risk. Despite recommendations from gastroenterology societies regarding the optimal rates of successful bowel preparation, these guidelines are frequently unmet. Various approaches have been employed to enhance the rates of successful bowel preparation, yet the quality of cleansing remains suboptimal. Intensive bowel preparation techniques, supplementary administration of bowel solutions, and educational interventions aimed at improving patient adherence to instructions have been commonly utilized, particularly among patients at a high risk of inadequate bowel preparation. Expedited strategies conducted on the same day as the procedure have also been endorsed by scientific organizations. More recently, the utilization of artificial intelligence (AI) has emerged for the preprocedural detection of inadequate bowel preparation, holding the potential to guide the preparation process immediately preceding colonoscopy. This manuscript comprehensively reviews the current strategies employed to optimize bowel cleansing, with a specific focus on patients with elevated risks for inadequate bowel preparation. Additionally, the prospective role of AI in this context is thoroughly examined.

CONCLUSIONS

While a majority of outpatients may achieve cleanliness with standard cleansing protocols, dealing with hard-to-prepare patients remains a challenge. Rescue strategies based on AI are promising, but such evidence remains limited. To ensure proper bowel cleansing, a combination of strategies should be performed.

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

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.

Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect

In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.

Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions

Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC.