ABSTRACT
Constipation is a pretty common and sometimes complicated health condition around the world which is characterized by an inability to have regular bowel movements. In response to this worrying trend, various pharmacological and non-pharmacological interventions have been embraced to seek to produce promising outcomes, yet patient dissatisfaction continues to be reported. The main aim of this review paper was to determine the effectiveness and safety of the vibrating capsule in treating constipated patients. The key databases that were consulted to get articles on this subject include Google Scholar, Embase, and PubMed. Specific keywords were used in the database search to get the relevant articles. Based on the exclusion criterion, articles that were excluded include conference abstracts, commentaries, preclinical research articles, articles where full texts were inaccessible, and those that had been published in a language other than English. From the results, the safety profile of the vibrating capsule suggests that the intervention is generally well-tolerated, with only mild and transient side effects or adverse events noted, including abdominal discomfort and sensations of mild vibration. However, the impact of these adverse events (although mild to moderate) on the efficacy of the capsule remains unknown, an area requiring further scholarly attention in the future. Concerning the efficacy of the intervention, most studies were found to affirm that the vibrating capsule enhances the physiologic effects of meals and waking on bowel movements, but the need for providers in clinical environments to note the interplay between the number of vibrations and the effectiveness of the capsule or onset of complete spontaneous bowel movements could not be overemphasized. In conclusion, this paper established that the vibrating capsule is an effective and promising technology through which constipated patients could be treated while experiencing minimal or no adverse events, but future research efforts ought to seek to uncover the interplay between the mechanism of action of the capsule and any moderating role played by factors internal or external to patients, including their emotional, mental, and psychological statuses, as well as the type and quantity of food consumed before and after the vibration sessions.
ABSTRACT
Colorectal cancer (CRC) is one of the most common forms of cancer. Therefore, diagnosing the condition early and accurately is critical for improved patient outcomes and effective treatment. Recently, artificial intelligence (AI) algorithms such as support vector machine (SVM) and convolutional neural network (CNN) have demonstrated promise in medical image analysis. This paper, conducted from a systematic review perspective, aimed to determine the effectiveness of AI integration in CRC diagnosis, emphasizing accuracy, sensitivity, and specificity. From a methodological perspective, articles that were included were those that had been conducted in the past decade. Also, the articles needed to have been documented in English, with databases such as Embase, PubMed, and Google Scholar used to obtain relevant research studies. Similarly, keywords were used to arrive at relevant articles. These keywords included AI, CRC, specificity, sensitivity, accuracy, efficacy, effectiveness, disease diagnosis, screening, machine learning, area under the curve (AUC), and deep learning. From the results, most scholarly studies contend that AI is superior in medical image analysis, the development of subtle patterns, and decision support. However, while deploying these algorithms, a key theme is that the collaboration between medical experts and AI systems needs to be seamless. In addition, the AI algorithms ought to be refined continuously in the current world of big data and ensure that they undergo rigorous validation to provide more informed decision-making for or against adopting those AI tools in clinical settings. In conclusion, therefore, balancing between human expertise and technological innovation is likely to pave the way for the realization of AI's full potential concerning its promising role in improving CRC diagnosis, upon which there might be significant patient outcome improvements, disease detection, and the achievement of a more effective healthcare system.
