The Potential Use of Artificial Intelligence in Irritable Bowel Syndrome Management.
Radu-Alexandru VulpoiMihaela LucaAdrian CiobanuAndrei OlteanuOana BărboiDiana-Elena IovLoredana NichitaIrina CiortescuCristina Cijevschi PrelipceanGabriela ȘtefnescuCătălina MihaiVasile-Liviu DrugPublished in: Diagnostics (Basel, Switzerland) (2023)
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.
Keyphrases
- artificial intelligence
- irritable bowel syndrome
- machine learning
- deep learning
- big data
- healthcare
- end stage renal disease
- physical activity
- systematic review
- chronic kidney disease
- staphylococcus aureus
- newly diagnosed
- ejection fraction
- escherichia coli
- risk factors
- clinical practice
- human health
- peritoneal dialysis
- patient reported outcomes
- high resolution
- patient reported
- social media
- high speed
- sensitive detection