Document Type : Systematic reviews


1 Health Management and Economics Research Center, Faculty of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran

2 Department of Health Information Management, Faculty of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran


Background: Nowadays, it can be seen that changes have taken place in the process of diseases and their clinical parameters. Accordingly, in some cases, general medical science and the use of clinical statistics based on the experiences of the physicians are not enough for the provision of sufficient tools for an early and accurate diagnosis. Therefore, medical science increasingly seeks to use unconventional methods and machine learning techniques. The issue of diagnosis in the medical world and the error rate of physicians in this regard are among the main challenges of the condition of patients and diseases. For this reason, in recent years, artificial intelligence tools have been used to help physicians. However, one of the main problems is that the effectiveness of machine learning tools is not studied much. Due to the sensitivity and high prevalence of diseases, especially gastrointestinal cancer, there is a need for a systematic review to identify methods of machine learning and artificial intelligence and compare their impact on the diagnosis of lower gastrointestinal cancers.
Objectives: This systematic review aimed to identify the machine learning methods used for the diagnosis of lower gastrointestinal cancers. Moreover, it aimed to classify the presented methods and compare their effectiveness and evaluation indicators.
Methods: This systematic review was conducted using six databases. The systematic literature review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement for systematic reviews. The search strategy consisted of four expressions, namely "machine learning algorithm", "lower gastrointestinal", "cancer", and "diagnosis and screening", in that order. It should be mentioned that studies based on treatment were excluded from this review. Similarly, studies that presented guidelines, protocols, and instructions were excluded since they only require the focus of clinicians and do not provide progression along an active chain of reasoning. Finally, studies were excluded if they had not undergone a peer-review process. The following aspects were extracted from each article: authors, year, country, machine learning model and algorithm, sample size, the type of data, and the results of the model. The selected studies were classified based on three criteria: 1) machine learning model, 2) cancer type, and 3) effect of machine learning on cancer diagnosis.
Results: In total, 44 studies were included in this systematic literature review. The earliest article was published in 2010, and the most recent was from 2019. Among the studies reviewed in this systematic review, one study was performed on the rectum (rectal cancer), one was about the small bowel (small bowel cancer), and 42 studies were on the colon (colon cancer, colorectal cancer, and colonic polyps). In total, 19 out of the 44 (43%) articles from the systematic literature review presented a deep learning model, and 25 (57%) articles used classic machine learning. The models worked mostly on image and all of them were supervised learning models. All studies with deep learning models used Convolutional Neural Network and were published between 2016 and 2019. The studies with classic machine learning models used diverse methods, mostly Support Vector Machine, K-Nearest Neighbors, and Artificial Neural Network.
Conclusion: Machine learning methods are suitable tools in the field of cancer diagnosis, especially in cases related to the lower gastrointestinal tract. These methods can not only increase the accuracy of diagnosis and help the doctor to make the right decision, but also help in the early diagnosis of cancer and reduce treatment costs. The methods presented so far have focused more on image data and more than anything else have helped to increase the accuracy of physicians in making the correct diagnosis. Achievement of the right method for early diagnosis requires more accurate data sets and analyses.


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