A comparison of artificial intelligence algorithms in diagnosing and predicting gastric cancer: a review study

Today, artificial intelligence is considered a powerful tool that can help physicians identify and diagnose and predict diseases. Gastric cancer has been the fourth most common malignancy and the second leading cause of cancer mortality in the world. Thus, timely diagnosis of this type of cancer could effectively control it. This paper compares AI (artificial intelligence) algorithms in diagnosing and predicting gastric cancer based on types of AI algorithms, sample size, accuracy, sensitivity, and specificity.  This narrative-review paper aims to explore AI algorithms in diagnosing and predicting gastric cancer. To achieve this goal, we reviewed English articles published between 2011 and 2021 in PubMed and Science direct databases. According to the reviews conducted on the published papers, the endoscopic method has been the most used method to collect and incorporate samples into designed models. Also, the SVM (support vector machine), convolutional neural network (CNN), and deep-type CNN have been used the most; therefore, we propose the usage of these algorithms in medical subjects, especially in gastric cancer

Designing and psychometric analysis of an instrument to assess learning process in a virtual environment

Abstract Background Today, methods that enable students to benefit from online programs to the fullest and learn independently and self-directed are of critical importance. Many scales have been developed to measure self-directed learning in the physical classroom. This study was conducted to design and assess the psychometric properties of an instrument to assess learning process in a virtual environment. Materials and methods A questionnaire for assessing s learning process in a virtual environment was developed following six steps. The process began with a systematic search for related articles. A qualitative study was then conducted to identify self-directed learning strategies and processes in virtual environments. The identified strategies were then compared with those from a literature review, and the scale items were developed accordingly. Expert validation, exploratory factor analysis, and reliability analysis were conducted to ensure questionnaire validity and reliability. This study included online postgraduate students from Iranian medical science universities in 2019. Results The scale consisted of 5 factors and 44 items. In exploratory factor analysis, five subscales explained 90% of the total variance. Cronbach’s alpha was 0.91 for the total scale. The interclass correlation coefficient between the test and retest was 0.77. Conclusion A questionnaire designed to assess learning process in a virtual environment for postgraduate virtual students has reasonable psychometric properties, including reasonable internal reliability and construct validity

Additional file 1 of Designing and psychometric analysis of an instrument to assess learning process in a virtual environment

Additional file 1. SDL Scale in Virtual Environment Questionnaire (SDLIVE)