Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey.

Digital pathology technologies, including whole slide imaging (WSI), have significantly improved modern clinical practices by facilitating storing, viewing, processing, and sharing digital scans of tissue glass slides. Researchers have proposed various artificial intelligence (AI) solutions for digital pathology applications, such as automated image analysis, to extract diagnostic information from WSI for improving pathology productivity, accuracy, and reproducibility. Feature extraction methods play a crucial role in transforming raw image data into meaningful representations for analysis, facilitating the characterization of tissue structures, cellular properties, and pathological patterns. These features have diverse applications in several digital pathology applications, such as cancer prognosis and diagnosis. Deep learning-based feature extraction methods have emerged as a promising approach to accurately represent WSI contents and have demonstrated superior performance in histology-related tasks. In this survey, we provide a comprehensive overview of feature extraction methods, including both manual and deep learning-based techniques, for the analysis of WSIs. We review relevant literature, analyze the discriminative and geometric features of WSIs (i.e., features suited to support the diagnostic process and extracted by "engineered" methods as opposed to AI), and explore predictive modeling techniques using AI and deep learning. This survey examines the advances, challenges, and opportunities in this rapidly evolving field, emphasizing the potential for accurate diagnosis, prognosis, and decision-making in digital pathology.

Assessing the Landscape of Telehealth Interventions for Women's Health: An Umbrella Review of Recent Advances Post-Pandemic.

This umbrella review aims to provide a comprehensive overview of the use of telehealth services for women after the COVID-19 pandemic. The review synthesizes findings from 21 reviews, covering diverse topics such as cancer care, pregnancy and postpartum care, general health, and specific populations. While some areas have shown promising results, others require further research to better understand the potential of digital health interventions. The review identifies gaps in knowledge and highlights the need for more rigorous and comprehensive research to address the limitations and gaps identified in the current evidence base. This includes prioritizing the use of standardized guidelines, quality assessment tools, and meta-analyses, as well as exploring the comparative effectiveness of different digital health interventions, the experiences of specific populations, and the cost-effectiveness of these technologies. By addressing these gaps, this umbrella review can inform future research and policy decisions, ultimately improving women's health outcomes in the post-pandemic era.

Deep Learning in Colorectal Cancer Classification: A Scoping Review.

Colorectal cancer (CRC) is one of the most common cancers worldwide, and its diagnosis and classification remain challenging for pathologists and imaging specialists. The use of artificial intelligence (AI) technology, specifically deep learning, has emerged as a potential solution to improve the accuracy and speed of classification while maintaining the quality of care. In this scoping review, we aimed to explore the utilization of deep learning for the classification of different types of colorectal cancer. We searched five databases and selected 45 studies that met our inclusion criteria. Our results show that deep learning models have been used to classify colorectal cancer using various types of data, with histopathology and endoscopy images being the most common. The majority of studies used CNN as their classification model. Our findings provide an overview of the current state of research on deep learning in the classification of colorectal cancer.

Predicting Overall Survival in METABRIC Cohort Using Machine Learning.

Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer that presents very high relapse and mortality. However, due to differences in the genetic architecture associated with TNBC, patients have different outcomes and respond differently to available treatments. In this study, we predicted the overall survival of TNBC patients in the METABRIC cohort employing supervised machine learning to identify important clinical and genetic features that are associated with better survival. We achieved a slightly higher Concordance index than the state of art and identified biological pathways related to the top genes considered important by our model.

Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review.

BACKGROUND: Non-alcoholic Fatty Liver Disease (NAFLD) is growing more prevalent worldwide. Although non-invasive diagnostic approaches such as conventional ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy, their efficacy has been called into doubt. Artificial Intelligence (AI) is now combined with traditional diagnostic processes to improve the performance of non-invasive approaches. OBJECTIVE: This study explores how well various AI methods function and perform on ultrasound (US) images to diagnose and quantify non-alcoholic fatty liver disease. METHODOLOGY: A systematic review was conducted to achieve this objective. Five science bibliographic databases were searched, including PubMed, Association for Computing Machinery ACM Digital Library, Institute of Electrical and Electronics Engineers IEEE Xplore, Scopus, and Google Scholar. Only peer-reviewed English articles, conferences, theses, and book chapters were included. Data from studies were synthesized using narrative methodologies per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. RESULTS: Forty-nine studies were included in the systematic review. According to the qualitative analysis, AI significantly enhanced the diagnosis of NAFLD, Non-Alcoholic Steatohepatitis (NASH), and liver fibrosis. In addition, modalities, image acquisition, feature extraction and selection, data management, and classifiers were assessed and compared in terms of performance measures (i.e., accuracy, sensitivity, and specificity). CONCLUSION: AI-supported systems show potential performance increases in detecting and quantifying steatosis, NASH, and liver fibrosis in NAFLD patients. Before real-world implementation, prospective studies with direct comparisons of AI-assisted modalities and conventional techniques are necessary.

ALLD: Acute Lymphoblastic Leukemia Detector.

Acute Lymphoblastic Leukemia (ALL) is a life-threatening type of cancer wherein mortality rate is unquestionably high. Early detection of ALL can reduce both the rate of fatality as well as improve the diagnosis plan for patients. In this study, we developed the ALL Detector (ALLD), which is a deep learning-based network to distinguish ALL patients from healthy individuals based on blast cell microscopic images. We evaluated multiple DL-based models and the ResNet-based model performed the best with 98% accuracy in the classification task. We also compared the performance of ALLD against state-of-the-art tools utilized for the same purpose, and ALLD outperformed them all. We believe that ALLD will support pathologists to explicitly diagnose ALL in the early stages and reduce the burden on clinical practice overall.

Recent Developments in Artificial Intelligence-Based Techniques for Prostate Cancer Detection: A Scoping Review.

Artificial intelligence (AI) techniques can contribute to the early diagnosis of prostate cancer. Recently, there has been a sharp increase in the literature on AI techniques for prostate cancer diagnosis. This review article presents a summary of the AI methods that detect and diagnose prostate cancer using different medical imaging modalities. Following the PRISMA-ScR principle, this review covers 69 studies selected from 1441 searched papers published in the last three years. The application of AI methods reported in these articles can be divided into three broad categories: diagnosis, grading, and segmentation of tissues that have prostate cancer. Most of the AI methods leveraged convolutional neural networks (CNNs) due to their ability to extract complex features. Some studies also reported traditional machine learning methods, such as support vector machines (SVM), decision trees for classification, LASSO, and Ridge regression methods for features extraction. We believe that the implementation of AI-based tools will support clinicians to provide better diagnosis plans for prostate cancer.

Artificial Intelligence for Skin Cancer Detection: Scoping Review.

BACKGROUND: Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, artificial intelligence (AI) tools are being used, including shallow and deep machine learning-based methodologies that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. OBJECTIVE: The aim of this study was to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examined the reliability of the selected papers by studying the correlation between the data set size and the number of diagnostic classes with the performance metrics used to evaluate the models. METHODS: We conducted a systematic search for papers using Institute of Electrical and Electronics Engineers (IEEE) Xplore, Association for Computing Machinery Digital Library (ACM DL), and Ovid MEDLINE databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. The studies included in this scoping review had to fulfill several selection criteria: being specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were independently conducted by two reviewers. Extracted data were narratively synthesized, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. RESULTS: We retrieved 906 papers from the 3 databases, of which 53 were eligible for this review. Shallow AI-based techniques were used in 14 studies, and deep AI-based techniques were used in 39 studies. The studies used up to 11 evaluation metrics to assess the proposed models, where 39 studies used accuracy as the primary evaluation metric. Overall, studies that used smaller data sets reported higher accuracy. CONCLUSIONS: This paper examined multiple AI-based skin cancer detection models. However, a direct comparison between methods was hindered by the varied use of different evaluation metrics and image types. Performance scores were affected by factors such as data set size, number of diagnostic classes, and techniques. Hence, the reliability of shallow and deep models with higher accuracy scores was questionable since they were trained and tested on relatively small data sets of a few diagnostic classes.

Cardiovascular Diseases in Qatar: Smoking, Food Habits and Physical Activities Perspectives.

Cardiovascular diseases (CVDs) trigger a high number of deaths across the world. In this study, we investigate the food, drinking, smoking, and lifestyle-related habits for a Qatari CVD cohort to understand the implication of these factors on CVD. Statistical analysis shows that the CVD group is consuming a lower amount of fast foods, soft drinks, snacks, and meats compared to the control group. Alarmingly, the level of smoking is still higher in the CVD group, and the consumption level of healthy items (e.g., cereal, cornflakes) in breakfast is relatively lower compared to the control group. Interestingly, the CVD cohort is spending more time walking and avoiding heavy sports, compared to the control group, but their involvement in moderate physical activities is lower than the control group. Overall, we conclude that the Qatari CVD cohort is following most of the standard guidelines related to food items and heavy sports; however, the cohort should reduce smoking habits, and may modify the moderate level of physical activity based on physician guidelines.

Use of mobile applications to improve nutrition behaviour: A systematic review.

BACKGROUND AND OBJECTIVE: Mobile applications could be effectively used for dietary intake assessment, physical activity monitoring, behavior improvement, and nutrition education. The aim of this review is to determine the effectiveness of mobile applications in improving nutrition behaviors through a systematic review of literature. METHODS: The review protocol was registered with PROSPERO: registration number CRD42018118809, and followed PRISMA guidelines. We involved original articles including mobile electronic devices for improving dietary intake, physical activity, and weight management in adult populations in this review. Data were retrieved from January 2010 to December 2018 with PubMed, Web of Science, Excerpta Medica Database (Embase), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) as data sources. Authors individually screened the titles and abstracts, then full articles in order to obtain papers that met inclusion criteria. RESULTS: The database search yielded 2962 records. After removing the duplicates and analyzing the full text papers a total of 8 original articles were reviewed. Two articles showed obvious bias and were not included in our results or discussion. The remaining six articles with low to moderate bias risk were included in this systematic review. Three selected studies were randomized control trials (RCTs) with over 180 participants each. The other three studies were a nested trial, a case-control trial, and a pilot RCT with 36, 162, and 24 participants respectively. All larger RCTs and the small case control trail showed significant improvements in some nutritional-health objectives measured. The other two trials showed insignificant improvements in outcomes measured between groups. CONCLUSION: This study highlights the potential significant health benefits acquirable through mobile health application-assisted nutrition interventions. Some of these studies required significant financial and time input from providers for the application's utilization. Further studies, perhaps with multiple intervention arms, are required to compare across programs the elements that are essential for health benefits observed.

The prevalence of chronic pain and its associated factors among Saudi Al-Kharj population; a cross sectional study.

BACKGROUND: Chronic pain (CP) can be a symptom of many underlying health issues. The consequences of CP may vary from slight discomfort to disruption of quality of life and normal functioning. In this study, we aim to investigate the prevalence of CP and its associated factors in Al Kharj, Saudi Arabia. METHODS: This was a cross-sectional study conducted in Al Kharj, Saudi Arabia. We recruited 1031 participants for our study. Data was collected on socio-demographic, health predictors and anthropometric measurements (such as weight, height and waist circumference). The data analysis was performed on JMP®, Version 12. SAS Institute Inc., Cary, NC, 1989-2007. RESULTS: The prevalence of self-reported chronic pain in Al Kharj population was 19% with a mean age of 26.4 (SD = 8.6) years. The most common locations of pain included; back pain (30%), abdominal pain (26%), headache (13%), and any musculoskeletal pain (56%). Multiple logistic regression revealed that presence of a chronic disease (OR = 3.8; 95% CI = 2.3-6.2), psychological disease (OR = 2.3; 95% CI = 1.2-4.3), high General Health Questionnaire (GHQ)-12 score (OR = 1.06; 95% CI = 1.03-1.1), and pack-years of smoking (OR = 1.05; 95% CI = 1.01-1.08) were significantly related to chronic pain in Al Kharj population. CONCLUSIONS: Our study results found a high burden of chronic pain in this selected Saudi population. The most prevalent pain was low back pain. The presence of chronic and psychological diseases were strongly related to chronic pain. Future prospective studies are needed to establish the temporal relationship of chronic pain with these factors.

The Use of Telemedicine in Surgical Care: a Systematic Review.

INTRODUCTION: Telemedicine describes a healthcare service where physicians communicate with patients remotely using telecommunication technologies. Telemedicine is being used to provide pre-/postoperative surgical consultation and monitoring as well as surgical education. AIM: Our purpose was to investigate the broad range of telemedicine technologies used in surgical care. MATERIAL AND METHODS: MEDLINE, EMBASE, CINAHL, and Science Direct were searched for available literature from inception to March 30, 2018 with no language restrictions. The search terms included: cell phones, telemedicine, telecommunications, video, online, videoconferencing, remote consultation, surgery, preoperative, perioperative, postoperative, and surgical procedures. Studies were included if they used telemedicine in surgery for pre-, peri-, or post-surgery periods, and if they compared traditional surgical care with surgical telemedicine. We excluded case series, case reports, and conference abstracts from our review. RESULTS: A total of 24 studies were included in our review. The study found that the use of telemedicine in preoperative assessment and diagnosis, evaluation after surgery and follow-up visits to be beneficial. Patients reported benefits to using telemedicine such as avoiding unnecessary trips to hospitals, saving time and reducing the number of working days missed. CONCLUSION: Telemedicine in surgical care can provide benefits to both patients and.

The impact of social media-based support groups on smoking relapse prevention in Saudi Arabia.

BACKGROUND AND OBJECTIVE: Tobacco smoking remains a major preventable cause of mortality and morbidity across the globe. People who attempt to quit smoking often experience episodes of relapse before finally quitting. Understanding the part that social networking sites and social media can play in smoking cessation and prevention of relapse is important to aid the development of novel techniques to curb the smoking epidemic. This study investigated the use of extra-treatment provided outside of the formal healthcare setting, bolstered by online social support in order to prevent smoking relapse in Saudi Arabia. METHODS: This cross-sectional study included 473 smokers taking part in smoking cessation intervention programs run by the Riyadh branch of King Abdul-Aziz Medical City and PURITY, a Saudi anti-smoking association. Only subjects who expressed an interest in quitting smoking, and those attempting to quit, were considered for inclusion. The sample was divided into three groups: subjects who subscribed to support groups on Twitter (n = 150), and WhatsApp (n = 150), and a control group of subjects who had not subscribed to any social media support groups (n = 173). RESULTS: A significant difference was found between the mean average numbers of people who quit smoking among the three groups, with social media support proving to be more effective than other traditional methods. Our findings imply that Twitter and WhatsApp users found it easier to quit smoking than those who did not take part in these social media groups. CONCLUSION: Social media provides a good platform to discuss smoking cessation treatment, and thus reduce smoking relapses. Our findings support the suggestion that more social media support groups should be developed to help people to effectively cease smoking after abstinence. Individuals who struggle to quit smoking should be encouraged to join support groups on their social media platform of choice to increase their likelihood of quitting. Future studies should assess the effectiveness of social media to help people quit smoking by including a greater diversity of social media platforms, including Facebook, Snapchat, and Instagram.

Knowledge of Palliative Care Among Nursing Students.

The aim of this study was to evaluate nursing undergraduate students' knowledge with regard to palliative care in Saudi Arabia. A quantitative descriptive research study was conducted by the use of validated tool. A total of 204 students were included in the study. There is little evidence in Saudi Arabia to demonstrate if nursing undergraduates receives education on palliative care. The results indicate that 57.9% of the nursing undergraduates had received educational sessions and 42.1% of nursing undergraduates did not. In conclusion, palliative care nursing education is crucial to improve quality of patient care in nursing practices. It is recommended that a palliative care education should be integrated within the nursing programme courses. Hence, in order to improve students' knowledge of palliative care, course content should cover the principles of palliative care as a part of any nursing bachelor programme.

Sharing sensitive health information through social media in the Arab world.

BACKGROUND: Sharing daily activities on social media has become a part of our lifestyle, but little is known about sharing sensitive health information in the Arab world. OBJECTIVE: The objective of this study is to explore how social media users in the Arab world share sensitive health information through Facebook. DESIGN: A retrospective qualitative analysis was used in the study. SETTINGS AND PARTICIPANTS: A total of 110 Facebook groups, related to HIV, sickle cell and depression were screened between 5 June and 1 December 2014. RESULTS: Forty four Facebook groups met the inclusion criteria. 28 471 posts were extracted, of which 649 met inclusion criteria. Forty two percent of health information exchanged were related to HIV, 34% to depression and 24% to sickle cell diseases. The majority of postings were from Egypt 21.1%, Saudi Arabia 20%, Algeria 10% and Libya 9.2%. Male posts were 54.2% while 45.8% were posted by females. Individuals utilized Facebook groups to share personal experiences of their disease 31%, in addition to being used for seeking queries 13.6%, offering explicit advice 8.3%, reporting signs and symptoms of the disease 7.3% and posting their communication with the health-care provider 6.6%. CONCLUSIONS: Users in the Arab world use social media to exchange sensitive health information, which could have serious implications regarding the privacy of the information shared with other members of the group. On the other hand, sharing health information could have positive effects for patients, such as sharing disease experiences and peer support. However, more work is needed to ensure that Facebook users in the Arab world are aware of the potential consequences of sharing sensitive health information through social media.

The Impact of Telenursing on Nursing Practice and Education: A Systematic Literature Review.

This systematic review aims to answer the following question: What is the impact of telenursing on nursing practice and education? Any article that was written in English and published in PubMed and Computers Informatics Nursing (CIN) journal from January 2012 to February 2016 discussing the impact of telenursing on nursing practice and education were included, while any opinion and review literature was excluded. The results show that there are four themes covered by the literature: 1. Impact of telenursing intervention using telephone and/ or videoconferencing on satisfaction and health outcomes; 2. Association of the patients' comorbidity characteristics with nursing utilization of telenursing and/or withdrawal from telehealth service during a telenursing care episode; 3. Tele-intensive care unit (tele-ICU) nursing and developing its competencies; and finally 4. Training on telenursing. Articles on Intensive Care Unit telenursing were found to be the most covered specialty/area of nursing in using telenursing. More research is still needed to show the impact of telenursing on nursing education as well as other specialties of nursing practice.

The Impact of Twitter and Facebook on Nursing Practice and Education: A Systematic Review of the Literature.

This systematic review aims to answer the following question: What is the impact of Twitter and Facebook on nursing practice and education? Any article that was written in English and was published in PubMed and Computers Informatics Nursing (CIN) journal from 2011 up to 2016 and discussed the impact of Twitter and Facebook on nursing practice and education was included, while any opinion and review articles were excluded. The results show three themes that are covered by the literature: (1) using social media to enhance students' confidence and /or self-efficacy, (2) characteristics of nurses who use social media, and (3) preferred modes of communication. Further research is still needed to reveal the impact of Twitter and Facebook on nursing education as well as other specialties of nursing practice.

Exploring the Impact of Information Seeking Behaviors of Online Health Consumers in the Arab World.

In the Arab world, increasing numbers of people are seeking online health related information for diagnoses, medicine, fitness, pharmaceutical drugs, and smoking cessation programs, among others. Studies exploring the impact of social media channels on health seeking behavior among Arabic users are limited. This study has two goals: (1) describe the prevalence of online health information-seeking behavior in the Arab world, and (2) study the impacts of social media based platforms in helping promote healthy living in the Arab world. In order to gather primary data, a web-based cross-sectional survey with a total of 7013 self-administered questionnaires was sent via SMS messages (n=1278), to Twitter followers of an Arab women's health social media account (n=3630 followers), and WhatsApp messages (n=2105) to participants above 16 years of age representing different socioeconomic groups and within the Arabic speaking world. The findings of this study show high interest among the participants (84.9%) in seeking online health information. Furthermore, reporting online information had an impact on participant health behaviors. Social media can play an important role in strengthening the health care system to provide valuable information, educational programs and interventions to promote healthy life styles among the Arabic people.

A Systematic Review of Mobile Health Technology Use in Developing Countries.

In developing countries, patients are now more informed about their healthcare options as a result of their use of mobile health (mHealth) technologies. The purpose of this paper is to describe the opportunities and challenges in using mHealth technologies for developing countries. In April 2015, Google Scholar and PubMed were searched to identify articles discussing the types, advantages and disadvantages, effectiveness, evaluation of mHealth technologies, and examples of mHealth implementation in developing countries. A total number of 3,803 articles were retrieved from both databases. Articles reporting the benefits and risks, effectiveness, and evaluation of mHealth were included. Articles that were written in English and from developing countries were also included. We excluded papers that were published before 2005, not written in English, and that were technical in nature. After screening the articles using the inclusion and exclusion criteria, 27 articles were selected for inclusion in the study. Of the 27 papers included in the review, eight described opportunities and challenges relating to mHealth, four focused on smoking cessation, three focused on weight loss, and four papers focused on chronic diseases. We also identified four articles discussing mHealth evaluation and four discussing the use of mHealth as a health promotion tool. We conclude that mHealth can improve healthcare delivery for developing countries. Some of the advantages of mHealth include: patient education, health promotion, disease self-management, decrease in healthcare costs, and remote monitoring of patients. However, there are several limitations in using mHealth technologies for developing countries, which include: interoperability, lack of evaluation standards, and lack of a technology infrastructure.

The Role of Social Media in Promoting Women's Health Education in Saudi Arabia.

Social media has the potential to improve women's health in developing countries through health education and promotion. In the Arab world, women's health interventions are lacking. However, with a high penetration rate of social media in the Arab world, there is good opportunity to utilize social media platforms such as Twitter to promote women's health. In this paper, we analyze the Tweet feeds of Saudi-based Twitter account to promote women's health. A total of 5167 Tweets were extracted and analyzed retrospectively, using NVivo Ncapture between June 2014 and March 2015. There were a total number of 3449 followers by March 20, 2015. Results showed that a majority of the Twitter followers (61%, n=2104) were seeking gynecological information, followed by pregnancy related information (27%, n=931), breast-feeding advice (9%, n=310), and other health related information (3%, n=103). Results also showed an increased level of health awareness and comprehension among Twitter followers. Further research is needed to promote women's health in Saudi Arabia and the Arab world through social media platforms such as Twitter and similar platforms including Instagram, Facebook, and YouTube which are also popular in the Arab world.