OBJECTIVE: In this systematic review, we aimed to measure and analyze 4 dimensions of pervasive games through development, technology, experience, and evaluation. Moreover, we also aimed to discover and interpret their relationship with game, interaction, experience, and service design.
METHODS: We first chose 3 well-known databases, Web of Science, Scopus, and EBSCO, and searched from 2013 to April 2022. A strictly thorough Boolean search for research keywords such as "pervasive game," "design," and "interactive" resulted in 394 relevant articles. These articles were identified, screened, and checked for eligibility to find valid and useful articles, which were then categorized and analyzed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method.
RESULTS: The systematic selection was finally left with 40 valid and valuable articles. After categorization and analysis, all articles were classified according to 4 main themes, which were design and development (11/40, 28%), interaction and technology (15/40, 38%), users and experience (9/40, 23%), and evaluation and service (5/40, 13%). These 4 main areas can be subdivided into several smaller areas.
CONCLUSIONS: In the 4 areas of game design, interaction design, experience design, and service design, many scholars have studied pervasive games and made contributions. Although the development and technology of pervasive games have evolved with the times, there is still a need to strengthen emerging design concepts within pervasive games.
METHODS: A retrospective study was conducted on 543 mammograms of 467 Malays, 48 Chinese, and 28 Indians in a middle-income nation. Three breast radiologists interpreted the examinations independently in two reading sessions (with and without AI support). Breast density and BI-RADS categories were assessed, comparing the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) results.
RESULTS: Of 543 mammograms, 69.2% had lesions detected. Biopsies were performed on 25%(n=136), with 66(48.5%) benign and 70(51.5%) malignant. Substantial agreement in density assessment between the radiologist and AI software (κ =0.606, p < 0.001) and the BI-RADS category with and without AI (κ =0.74, p < 0.001). The performance of the AI software was comparable to the traditional methods. The sensitivity, specificity, PPV, and NPV or radiologists alone, radiologist + AI, and AI alone were 81.9%,90.4%,56.0%, and 97.1%; 81.0%, 93.1%,55.5%, and 97.0%; and 90.0%,76.5%,36.2%, and 98.1%, respectively. AI software enhances the accuracy of lesion diagnosis and reduces unnecessary biopsies, particularly for BI-RADS 4 lesions. The AI software results for synthetic were almost similar to the original 2D mammography, with AUC of 0.925 and 0.871, respectively.
CONCLUSION: AI software may assist in the accurate diagnosis of breast lesions, enhancing the efficiency of breast lesion diagnosis in a mixed population of opportunistic screening and diagnostic patients.
KEY MESSAGES: • The use of artificial intelligence (AI) in mammography for population-based breast cancer screening has been validated in high-income nations, with reported improved diagnostic performance. Our study evaluated the usage of an AI tool in an opportunistic screening setting in a multi-ethnic and middle-income nation. • The application of AI in mammography enhances diagnostic accuracy, potentially leading to reduced unnecessary biopsies. • AI integration into the workflow did not disrupt the performance of trained breast radiologists, as there is a substantial inter-reader agreement for BI-RADS category assessment and breast density.