The impact of artificial intelligence algorithms on management of patients with irritable bowel syndrome: A systematic review

Kordi, Marzieh; Dehghan, Mohammad Jafar; Shayesteh, Ali Akbar; Azizi, Amirabbas

doi:10.1016/j.imu.2022.100891

Cited by 5 publications

(5 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy of distinguishing patients with FD from healthy controls is clinically relevant. Several AI-based models previously reported for diagnosing IBS and FD have an accuracy of approximately 80% [ 14 ]. Our classification model is thus acceptable for integrating clinical conditions, considering its 77% accuracy in distinguishing patients with FD from healthy controls and the procedure’s non-invasiveness and ease of use.…”

Section: Discussionmentioning

confidence: 99%

“…Artificial intelligence (AI) has been used to support the imaging diagnosis of GI disorders, including gastric and colon cancers using endoscopic findings and hepatic cancer using computed tomography imaging [ 12 , 13 ]. There are several AI-based diagnostic systems for DGBIs using gut-microbiome, pulse signal, bowel sound features, and endoscopic findings [ 14 , 15 ], and in terms of food, the efficacy of an AI-based personalized diet has been confirmed [ 16 ]. However, a model that uses food preference and brain activity in response to food images has not yet been established.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence-Based Diagnostic Support System for Functional Dyspepsia Based on Brain Activity and Food Preference

Katsumata,

Hosokawa,

Kamada

2023

Cureus

View full text Add to dashboard Cite

Background and aim Disorders of gut-brain interaction (DGBI) are disorders where no organic clinical abnormalities are detected such as functional dyspepsia (FD) and irritable bowel syndrome (IBS). The brain activity of individuals with FD and IBS differs from that of healthy controls. Artificial intelligence can distinguish healthy controls from individuals with DGBI using several biomarkers. This study aimed to establish an artificial intelligence-based diagnostic support system using food preferences and brain activity in patients with DGBI. Methods ROME IV criteria were used to diagnose patients with FD and IBS. Their food preference was scored using a visual analog scale, and brain activity in the prefrontal cortex was investigated using functional near-infrared spectroscopy (fNIRS). The diagnostic model was developed based on the brain activity and visual analog scale scores for food using an artificial neural network model. Results Forty-one participants, including 25 patients with DGBI were enrolled in the study. The accuracy of the artificial intelligence-based diagnostic model using an artificial neural network in differentiating between healthy controls and patients with DGBI and between healthy controls and those with FD were 72.3% and 77.1%, respectively. Conclusions The artificial intelligence-based diagnostic model using brain activity and preference to food images showed sufficiently high accuracy in distinguishing patients with DGBI from healthy controls, and those with FD from healthy controls. Therefore, the fNIRS system provides objective evidence for diagnosing DGBI.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Diagnostic Support System for Functional Dyspepsia Based on Brain Activity and Food Preference

Katsumata,

Hosokawa,

Kamada

2023

Cureus

View full text Add to dashboard Cite

show abstract

“…As new technologies are developed in medicine, some potential new tools may become available. To the best of our knowledge, there has only been one systematic review published on AI and IBS in 2022 by Marzieh Kordi et al [41]. They were only able to include 20 papers in their research.…”

Section: Future Trendsmentioning

confidence: 99%

“…They were only able to include 20 papers in their research. Reviewing some of these papers revealed a more technical approach to IBS and AI than a clinical one [41]. Even though the authors concluded that AI algorithms can play an important role in predicting, diagnosing, and managing IBS, further studies involving close collaboration between physicians and computer experts are necessary.…”

Section: Future Trendsmentioning

confidence: 99%

The Potential Use of Artificial Intelligence in Irritable Bowel Syndrome Management

Vulpoi,

Luca,

Ciobanu

et al. 2023

Diagnostics

View full text Add to dashboard Cite

Irritable bowel syndrome (IBS) has a global prevalence of around 4.1% and is associated with a low quality of life and increased healthcare costs. Current guidelines recommend that IBS is diagnosed using the symptom-based Rome IV criteria. Despite this, when patients seek medical attention, they are usually over-investigated. This issue might be resolved by novel technologies in medicine, such as the use of Artificial Intelligence (AI). In this context, this paper aims to review AI applications in IBS. AI in colonoscopy proved to be useful in organic lesion detection and diagnosis and in objectively assessing the quality of the procedure. Only a recently published study talked about the potential of AI-colonoscopy in IBS. AI was also used to study biofilm characteristics in the large bowel and establish a potential relationship with IBS. Moreover, an AI algorithm was developed in order to correlate specific bowel sounds with IBS. In addition to that, AI-based smartphone applications have been developed to facilitate the monitoring of IBS symptoms. From a therapeutic standpoint, an AI system was created to recommend specific diets based on an individual’s microbiota. In conclusion, future IBS diagnosis and treatment may benefit from AI.

show abstract

“…An ensemble of machines is used in the classification method known as AdaBoost [ 31 ], which is a boosting classifier. Unlike random forests and decision trees, this kind of forest is ordered.…”

Section: Proposed Metabolic Classification Frameworkmentioning

confidence: 99%

Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based Classification

et al. 2022

View full text Add to dashboard Cite

Metabolic syndrome (MetS) is a cluster of risk factors including hypertension, hyperglycemia, dyslipidemia, and abdominal obesity. Metabolism-related risk factors include diabetes and heart disease. MetS is also linked to numerous cancers and chronic kidney disease. All of these variables raise medical costs. Developing a prediction model that can quickly identify persons at high risk of MetS and offer them a treatment plan is crucial. Early prediction of metabolic syndrome will highly impact the quality of life of patients as it gives them a chance for making a change to the bad habit and preventing a serious illness in the future. In this paper, we aimed to assess the performance of various algorithms of machine learning in order to decrease the cost of predictive diagnoses of metabolic syndrome. We employed ten machine learning algorithms along with different metaheuristics for feature selection. Moreover, we examined the effects of data augmentation in the prediction accuracy. The statistics show that the augmentation of data after applying feature selection on the data highly improves the performance of the classifiers.

show abstract

The impact of artificial intelligence algorithms on management of patients with irritable bowel syndrome: A systematic review

Cited by 5 publications

References 37 publications

Artificial Intelligence-Based Diagnostic Support System for Functional Dyspepsia Based on Brain Activity and Food Preference

Artificial Intelligence-Based Diagnostic Support System for Functional Dyspepsia Based on Brain Activity and Food Preference

The Potential Use of Artificial Intelligence in Irritable Bowel Syndrome Management

Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based Classification

Contact Info

Product

Resources

About