Browsing by Author "Nawaz, Faisal A"

Now showing 1 - 11 of 11

Afghanistan's humanitarian crisis and its impacts on the mental health of healthcare workers during COVID-19
(2022-12) Nawaz, Faisal A
Abstract: Afghanistan’s humanitarian crisis has severely impacted the mental health of frontline workers. With the introduction of the Taliban government, ongoing civil unrest, and other forms of violent attacks, healthcare workers (HCWs) continue to provide patient care despite minimal resources. A severe contraction in the economy, poor supply of medications, political turmoil, and insufficient humanitarian aid have added to pre-existing problems. High levels of insecurity and instability as well as decades of traumatic experiences have contributed to increasing mental health challenges amongst frontline workers. Despite the scarcity of mental health services, HCWs continue to persevere with their service to the community. However, inadequate interventions may have serious implications for HCWs bearing the brunt of multiple traumas. Thus, governmental and international involvement is needed to address both the economic and psychological needs of HCWs in Afghanistan
AI-enhanced solutions during COVID-19: Current trends and future innovations
(2022-08) Nawaz, Faisal A; Boillat, Thomas; Khan, Abdul Rahman
Abstract: Artificial Intelligence (AI) is defined as a branch of Computer Science that is capable of simulating intelligent behavior through machine automation systems. There has been a significant rise in research and application of AI in addressing various aspects of Engineering and Medicine. This mutual overlap between the two fields has led to a new discipline, so-called “Artificial Intelligence in Medicine”, or AIM in short [1]. Not only is AIM being applied for image processing and analysis, but also for prognose [2–4], treatment [5–7], and patient monitoring [1,8] among others. In this matter, AIM has been instrumental during the Corona Virus Disease 2019 (COVID-19) pandemic. The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has been brought to global attention and was declared a pandemic by the World Health Organization (WHO) on March 11, 2020 [9]. The exponential increase in the number of cases worldwide has prompted for emergent innovations and collaborations in the fields of Medicine and Engineering. While there have been contrasting opinions on the scope of AI during this period, we are observing a continuum of interdisciplinary growth across this field [10]. The early impact of AI during COVID-19 has been observed in 1) Early warning system and predictive modeling 2) Contact Tracing 3) Diagnostics 4) Drug discovery and development and 5) Social Control. As depicted in Fig. 1, this article aims to explore these domains [10] in the context of AI-assisted applications and their impact on addressing COVID-19. This article describes the contributions of AI during COVID-19, along with trends and innovations related to these technologies in harnessing sustainable healthcare solutions.
Enhancing public trust in COVID-19 vaccination during the 2022 FIFA Men’s World Cup: a call for action
(2022) Nawaz, Faisal A
Abstract:‎ The COVID-19 continues to be a global threat with many countries currently battling the third wave of this ‎pandemic.1 This pandemic has caused long-term effects in the form of health, human and economic ‎loss along with psychological distress, particularly in low-income countries.‎ Although vaccination efforts are under way in many countries, vaccine hesitancy, listed as one of global ‎health’s most challenging issues, continues to be a major limitation to curbing the pandemic.2 Moreover, ‎vaccine distribution inequality has emerged as a matter of serious concern, leaving lower-income ‎countries with limited vaccine doses.3‎ The high visibility of sport provides an opportunity, if not a responsibility, to assist multi-faceted efforts ‎to help mitigate this distressing crisis.‎
Monkeypox Outbreaks in 2022: Battling Another "Pandemic" of Misinformation
(2022-07) Ennab, Farah; Nawaz, Faisal A
Abstract: Monkeypox is a virus that was formerly recognized as a rare zoonotic disease but has been noted as an emerging disease of concern where there are significant gaps in knowledge. In 2022, Monkeypox outbreaks are being concurrently reported in numerous non-endemic geographical areas, thus instigating a global wave of public health concern amid calls for urgent action from international authorities. As of the 8th of June 2022, about 1,285 laboratory-confirmed cases were detected in 28 regions across Africa, the Americas, and the European Region [1]. This virus is more commonly reported in West and Central Africa, where previous self-limiting outbreaks have had a mortality rate ranging between 1% and 15% [2]. However, the alarming surge of cases in 2022 with obvious multicountry community transmission is raising the alarm bells for monkeypox as a potential future global threat with unprecedented ramifications.
Nonalcoholic Fatty Liver Disease in Children and Adolescents Taking Atypical Antipsychotic Medications: Protocol for a Systematic Review and Meta-analysis
(2022) Hatem, Reem; Nawaz, Faisal A; Almoosa, Mohammad; Albanna, Ammar; Al-Sharif, Ghadah A
Background: Atypical antipsychotics (AAP) are commonly prescribed to children and adolescents and are associated with important adverse effects including weight gain and metabolic syndrome. Nonalcoholic fatty liver disease (NAFLD) is not only the most common pediatric liver disease but is also associated with serious complications including liver cirrhosis. Objective: Given that NAFLD and AAP are associated with metabolic syndrome, we aim to comprehensively examine the association between AAP and NAFLD in children and adolescents. Methods: We will conduct a systematic review of studies exploring NAFLD in subjects younger than 18 years on AAP published in English between 1950 and 2020 following the PRISMA (Preferred Reporting items for Systematic Reviews and Meta-Analysis) guidelines. Results: A PRISMA flowchart will be used present the study results after comprehensively reviewing studies on NAFLD in children and adolescents taking AAP. The first and second systematic searches will be conducted during December 2021. The results are expected to be published in June 2022. Conclusions: This research project will serve as a foundation for future studies and assist in devising interventions and reforming clinical guidelines for using AAP to ensure improved patient safety.
Promoting Research, Awareness, and Discussion on AI in Medicine Using #MedTwitterAI: A Longitudinal Twitter Hashtag Analysis
(2022-07) Nawaz, Faisal A
Abstract: Background: Artificial intelligence (AI) has the potential to reshape medical practice and the delivery of healthcare. Online discussions surrounding AI’s utility in these domains are increasingly emerging, likely due to considerable interest from healthcare practitioners, medical technology developers, and other relevant stakeholders. However, many practitioners and medical students report limited understanding and familiarity with AI. Objective: To promote research, events, and resources at the intersection of AI and medicine for the online medical community, we created a Twitter-based campaign using the hashtag #MedTwitterAI. Methods: In the present study, we analyze the use of #MedTwitterAI by tracking tweets containing this hashtag posted from 26th March, 2019 to 26th March, 2021, using the Symplur Signals hashtag analytics tool. The full text of all #MedTwitterAI tweets was also extracted and subjected to a natural language processing analysis. Results: Over this time period, we identified 7,441 tweets containing #MedTwitterAI, posted by 1,519 unique Twitter users which generated 59,455,569 impressions. The most common identifiable locations for users including this hashtag in tweets were the United States (378/1,519), the United Kingdom (80/1,519), Canada (65/1,519), India (46/1,519), Spain (29/1,519), France (24/1,519), Italy (16/1,519), Australia (16/1,519), Germany (16/1,519), and Brazil (15/1,519). Tweets were frequently enhanced with links (80.2%), mentions of other accounts (93.9%), and photos (56.6%). The five most abundant single words were AI (artificial intelligence), patients, medicine, data, and learning. Sentiment analysis revealed an overall majority of positive single word sentiments (e.g., intelligence, improve) with 230 positive and 172 negative sentiments with a total of 658 and 342 mentions of all positive and negative sentiments, respectively. Most frequently mentioned negative sentiments were cancer, risk, and bias. Most common bigrams identified by Markov chain depiction were related to analytical methods (e.g., label-free detection) and medical conditions/biological processes (e.g., rare circulating tumor cells). Conclusion: These results demonstrate the generated considerable interest of using #MedTwitterAI for promoting relevant content and engaging a broad and geographically diverse audience. The use of hashtags in Twitter-based campaigns can be an effective tool to raise awareness of interdisciplinary fields and enable knowledge-sharing on a global scale.
Readiness to Embrace Artificial Intelligence Among Medical Doctors and Students: Questionnaire-Based Study
(2022) Boillat, Thomas; Nawaz, Faisal A; Rivas, Homero
Background: Similar to understanding how blood pressure is measured by a sphygmomanometer, physicians will soon have to understand how an artificial intelligence–based application has come to the conclusion that a patient has hypertension, diabetes, or cancer. Although there are an increasing number of use cases where artificial intelligence is or can be applied to improve medical outcomes, the extent to which medical doctors and students are ready to work and leverage this paradigm is unclear. Objective: This research aims to capture medical students’ and doctors’ level of familiarity toward artificial intelligence in medicine as well as their challenges, barriers, and potential risks linked to the democratization of this new paradigm. Methods: A web-based questionnaire comprising five dimensions—demographics, concepts and definitions, training and education, implementation, and risks—was systematically designed from a literature search. It was completed by 207 participants in total, of which 105 (50.7%) medical doctors and 102 (49.3%) medical students trained in all continents, with most of them in Europe, the Middle East, Asia, and North America. Results: The results revealed no significant difference in the familiarity of artificial intelligence between medical doctors and students (P=.91), except that medical students perceived artificial intelligence in medicine to lead to higher risks for patients and the field of medicine in general (P<.001). We also identified a rather low level of familiarity with artificial intelligence (medical students=2.11/5; medical doctors=2.06/5) as well as a low attendance to education or training. Only 2.9% (3/105) of medical doctors attended a course on artificial intelligence within the previous year, compared with 9.8% (10/102) of medical students. The complexity of the field of medicine was considered one of the biggest challenges (medical doctors=3.5/5; medical students=3.8/5), whereas the reduction of physicians’skills was the most important risk (medical doctors=3.3; medical students=3.6; P=.03). Conclusions: The question is not whether artificial intelligence will be used in medicine, but when it will become a standard practice for optimizing health care. The low level of familiarity with artificial intelligence identified in this study calls for the implementation of specific education and training in medical schools and hospitals to ensure that medical professionals can leverage this new paradigm and improve health outcomes.
Relevance of Anthropometric Measurements in a Multiethnic Obesity Cohort: Observational Study
(2021) ElSaban, Mariam; Nawaz, Faisal A; Hassan Khamis, Amar
Background: The prevalence of obesity is increasing worldwide, and the Middle East is not an exception to this increasing trend. Obesity increases the risk of multiple metabolic complications, such as diabetes mellitus. Measurement of obesity has primarily relied on the BMI to identify risk; however, both bedside and office-based anthropometric measures of obesity can provide more detailed information on risk. Objective: This study aimed to investigate the prevalence of obesity-related diseases in a multidisciplinary weight management population and to determine its relationship with obesity anthropometric indices. Methods: This cross-sectional study was conducted at Mediclinic Parkview Hospital (Dubai, the United Arab Emirates). In total, 308 patients have been evaluated from January to September 2019 as part of a multidisciplinary weight management program. Key demographics, anthropometrics, and clinical data were analyzed using SPSS (version 25, SPSS Inc). Results: Our cohort of 308 patients included 103 (33%) males and 205 (67%) females of 38 nationalities. The mean age of the cohort was 41 (SD 9.6) years, with a median BMI of 34.5 (IQR 6.7) and 33.7 (IQR 7.8) for males and females, respectively. The mean waist circumference (WC) was 113.4 (SD 23.3) cm and 103.5 (SD 16.2) cm, fat percentage was 33.7% (SD 11.6%) and 45% (SD 6.8%), fat mass was 41 (SD 15.2) kg and 41.1 (SD 14.1) kg, and visceral fat mass was 6.5 (SD 3.2) kg and 3.1 (SD 1.8) kg for males and females, respectively. There was a strong correlation between BMI and WC (r=0.65 and r=0.69 in males and females, respectively; P=.01) and visceral fat (r=0.78 and r=0.90 in males and females, respectively). Furthermore, visceral fat was significantly associated with WC in both sexes (r=0.73 and r=0.68 in females and males respectively; P=.01). Furthermore, WC was significantly associated with a risk of diabetes, hypertension, and nonalcoholic fatty liver disease. Conclusions: BMI and WC are the most representative measures of obesity in our population and correlate with abdominal adiposity– and obesity-related diseases. Further studies are required to assess the benefits of these measures during weight reduction interventions.
Strategies for malaria vaccination during the COVID-19 pandemic in African countries
(2022) Nawaz, Faisal A; Du Plessis, Stefan
Abstract: Since October 2021, the World Health Organization (WHO) recommends the use of RTS,S/AS01 (RTS,S) malaria vaccine for children in areas of moderate to high transmission of Plasmodium falciparum in Africa. The vaccine can reduce the 241 million cases of malaria and 627 000 malaria deaths worldwide; it is much needed in the WHO African Region, which accounts for 228 million cases of malaria (95% of global cases) and about 96% of global malaria deaths. However, an effective vaccine roll-out in Africa can only be achieved when region-specific challenges can be overcome; intraregional inequality, health-care systems strengthening and lessons from community engagement in previous public health crises.
A successful management of left-sided posterior congenital diaphragmatic hernia of the jejunum, ileum, colon and left kidney: a case report
(2022-10) Al-Abdullah, Zainab; Duvuru, Ruthwik; Nawaz, Faisal A; Ennab, Farah
Abstract: Congenital diaphragmatic hernia (CDH) is a rare developmental anomaly in which abdominal contents herniate into the thoracic cavity due to underdevelopment of the diaphragm, possibly leading to pulmonary hypoplasia. Whereas surgery is not the first priority in treatment, it must be performed within a window of 2 weeks and after hemodynamic stability has been achieved. The patient described in this case report had a CDH of the jejunum, ileum, colon and left kidney diagnosed in a boy of South Asian origin who presented with tachypnea in the third hour of life. Imaging studies conducted included chest X-ray, chest ultrasound including echocardiogram, and abdominal and pelvic ultrasound. Treatment and management were successful despite complications. Future research on CDH is warranted in the populations in the Middle East, and local guidelines must be generated in order to improve diagnosis, treatment and prognosis.
Twitter-based crowdsourcing: What kind of measures can help to end the COVID-19 pandemic faster?
(2022) Nawaz, Faisal A
Background: Crowdsourcing is a low-cost, adaptable, and innovative method to collect ideas from numerous contributors with diverse backgrounds. Crowdsourcing from social media like Twitter can be used for generating ideas in a noticeably brief time based on contributions from globally distributed users. The world has been challenged by the COVID-19 pandemic in the last several years. Measures to combat the pandemic continue to evolve worldwide, and ideas and opinions on optimal counteraction strategies are of high interest. Objective: This study aimed to validate the use of Twitter as a crowdsourcing platform in order to gain an understanding of public opinion on what measures can help to end the COVID-19 pandemic faster. Methods: This cross-sectional study was conducted during the period from December 22, 2021, to February 4, 2022. Tweets were posted by accounts operated by the authors, asking “How to faster end the COVID-19 pandemic?” and encouraging the viewers to comment on measures that they perceive would be effective to achieve this goal. The ideas from the users’ comments were collected and categorized into two major themes – personal and institutional measures. In the final stage of the campaign, a Twitter poll was conducted to get additional comments and to estimate which of the two groups of measures were perceived to be important amongst Twitter users. Results: The crowdsourcing campaign generated seventeen suggested measures categorized into two major themes (personal and institutional) that received a total of 1,727 endorsements (supporting comments, retweets, and likes). The poll received a total of 325 votes with 58% of votes underscoring the importance of both personal and institutional measures, 20% favoring personal measures, 11% favoring institutional measures, and 11% of the votes given just out of curiosity to see the vote results. Conclusions: Twitter was utilized successfully for crowdsourcing ideas on strategies how to end the COVID-19 pandemic faster. The results indicate that the Twitter community highly values the significance of both personal responsibility and institutional measures to counteract the pandemic. This study validates the use of Twitter as a primary tool that could be used for crowdsourcing ideas with healthcare significance.