Breast cancer is the most common cancer among women around the world. Despite enormous medical progress, breast cancer has still remained the second leading cause of death worldwide; thus, its early diagnosis has a significant impact on reducing mortality. However, it is often difficult to diagnose breast abnormalities. Different tools such as mammography, ultrasound, and thermography have been developed to screen breast cancer. In this way, the computer helps radiologists identify chest abnormalities more efficiently using image processing and artificial intelligence (AI) tools. This article examined various methods of AI using image processing to diagnose breast cancer. It was a review study through library and Internet searches. By searching the databases such as Medical Literature Analysis and Retrieval System Online (MEDLINE) via PubMed, Springer, IEEE, ScienceDirect, and Gray Literature (including Google Scholar, articles published in conferences, government technical reports, and other materials not controlled by scientific publishers) and searching for breast cancer keywords, AI and medical image processing techniques were extracted. The results were provided in tables to demonstrate different techniques and their results over recent years. In this study, 18,651 articles were extracted from 2007 to 2017. Among them, those that used similar techniques and reported similar results were excluded and 40 articles were finally examined. Since each of the articles used image processing, a list of features related to the image used in each article was also provided. The results showed that support vector machines had the highest accuracy percentage for different types of images (ultrasound =95.85%, mammography =93.069%, thermography =100%). Computerized diagnosis of breast cancer has greatly contributed to the development of medicine, is constantly being used by radiologists, and is clear in ethical and medical fields with regard to its effects. Computer-assisted methods increase diagnosis accuracy by reducing false positives.

Attention is a basic and main mental task and can play an important role in the functioning of other brain abilities such as intelligence, memory, learning, and perception, and its deficit occurs in 80% of patients with traumatic brain injury. The use of game-based tools for rehabilitation is rapidly expanding. Cognitive rehabilitation via video games is an emerging hot topic in cognitive science. Serious games serve a specific purpose in addition to entertainment. They can be more engaging than exercises since they replace reward and motivation systems with real-world motivations as a complement for rehabilitation activities. This study was aimed at identifying and categorizing serious computer games used for attention rehabilitation and evaluating their effects. Six electronic databases (Scopus, PubMed, ISI, Embase, IEEE, and Cochrane) were searched in August 2021. The search strategy consisted of three main concepts of “serious game”, “cognitive deficits”, and “cognitive rehabilitation”. The inclusion criteria were (1) journal articles, (2) English language, (3) being published in the last 10 years, (4) human participants, and (5) game-based intervention. In the 30 included studies, 22 unique games were utilized for attention rehabilitation. Lumosity (20%), Brain Age (Dr. Kawashima’s Brain Training) (10%), and MoHRS (6.66%) were the most common games among the studies. There were (57%) casual, (23%) action, (10%) simulation, and (10%) multiple genres. Of the 47 tools used in the studies, 5 utilized cross-modal oddball attention tasks, 4 utilized game performance, 3 utilized the paced auditory serial additional test (PASAT), and the rest employed other tools. A total of 73 outcome measures were related to attention, 42 measures did not have significant results, 30 were significantly improved, 1 was significantly deteriorated, and 4 articles did not have any specific measures for attention evaluation. Thus, the results revealed the positive effect of serious games on attention. However, issues such as absence of scientific teams, the variety of the disorders that cause defects, the variety of criteria, differences in measurements, lack of long-term follow-up, insufficient RCT studies, and small sample sizes should be considered when designing, developing, and using game-based systems to prevent bias.

Fuzzy expert system for diagnosing diabetic neuropathy

Background: Mobile applications and social media serve their users as convenient tools to improve and monitor diseases and conditions such as pregnancy. These tools also exert a positive impact on Gestational diabetes mellitus (GDM) self-management. Introduction: Despite the expansion of mobile health apps for the management of GDM, no study has evaluated these apps using a valid tool. This study aimed to search and review the apps developed for this purpose, providing overall and specific rating scores for each aspect of MARS. Methods: Two cases of app stores (IOS and Google Play) were searched in January 2019 for apps related to GDM. Search keywords included “gestational diabetes”, “pregnant diabetes”, and “Health apps”. Eligibility criteria include: capable of running on Android or IOS operating systems, in the English language, especially for GDM, and available in Iran. After removal of duplicates, the apps were reviewed, rated, and evaluated independently by two reviewers with Mobile App Rating Scale (MARS) tools. Results: Initially, 102 apps were identified after the exclusion process, five selected apps were downloaded and analyzed. All apps were classified into four categories according to contents and their interactive capabilities. In most quadrants of MARS, the Pregnant with Diabetes app received the highest scores. Also, in general, the maximum app quality mean score belonged to Pregnant with Diabetes (3.10 / 5.00). Conclusion: Findings revealed that apps designed for GDM are small in number and poor in quality based on MARS tools. Therefore, considering pregnant women’s need for using the capabilities of these apps in pregnancy management and promoting community-based care, it seems essential to develop and design a series of high-quality apps in all four specified categories (only giving comments, obtaining data and giving comments, diagnosis of GDM, and diet calculator).

Mobile health as one of the new technologies can be a proper solution to support care provision for the elderly and provide personalized care for them. This study is aimed at reviewing the benefits and challenges of personalized mobile health (PMH) for elderly home care. With a systematic review methodology, 1895 records were retrieved by searching four databases. After removing duplicates, 1703 articles remained. Following full-text examination, 21 articles that met the inclusion criteria were studied in detail, and the output was presented in different tables. The results indicated that 25% of the challenges were related to privacy, cybersecurity, and data ownership (10%), technology (7.5%), and implementation (7.5%). The most frequent benefits were related to cost-saving (17.5%), nurse engagement improvement (10%), and caregiver stress reduction (7.5%). In general, the number of benefits in this study was slightly higher than the challenges, but in order to use PMH technologies, the challenges presented in this study must be carefully considered and a suitable solution must be adopted. Benefits can also be helpful in persuading individuals and health-care providers. This study shed light on those points that need to be highlighted for further work in order to convert the challenges toward benefits.

Background: Traumatic brain injury (TBI) resulting from a forceful impact to the head can cause severe functional disabilities, with cognitive impairment being a major hindrance to patients' return to daily life. Encouraging patients to engage in rehabilitation programs consistently poses a significant challenge for therapists. To address this issue, gamification has gained momentum as an effective approach. This study aims to develop a serious game-based cognitive rehabilitation system tailored for patients with brain injury. Methods: The study included four stages. Initially, the requirements were analyzed through focus groups. Then the system structure and game content were discussed and was agreed as a conceptual model. In second stage, the system design was drawn using various modeling diagrams. In third stage, a system prototype was developed using the Unity game engine and C# programming. Finally, a heuristic evaluation method was employed to assess usability. Results: Based on the focus group meetings with seven participants, a conceptual model of the system structure and game content was designed. Game's interface was developed for both the therapist and patient versions. The focus groups determined a 2D casual gaming genre with a postman character and 10 missions on the smartphone platform. For example, in the first mission, the postman must move from mailboxes 1 to 10 and pick up the letters. This is according to Trail Making Test task. The 16 tasks in different subcategories of attention were selected to make these missions. The usability evaluation highlighted privacy, help and documentation, and aesthetic and minimalist design as the areas with the highest percentage of problems. Conclusions: Cognitive rehabilitation is vital in facilitating patients' faster return to daily routines and enhancing their quality-of-life following brain injury. Incorporating a game-based system provides patients with increased motivation to engage in various cognitive exercises. Additionally, continuous monitoring by specialists ensures effective patient management. The game-based system offers different game stages to strengthen and rehabilitate attention in patients with brain injury. In the next step, the clinical effects of this system will be evaluated.