Artificial Intelligence-Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development.

Artificial intelligence (AI) technologies can play a key role in preventing, detecting, and monitoring epidemics. In this paper, we provide an overview of the recently published literature on the COVID-19 pandemic in four strategic areas: (1) triage, diagnosis, and risk prediction; (2) drug repurposing and development; (3) pharmacogenomics and vaccines; and (4) mining of the medical literature. We highlight how AI-powered health care can enable public health systems to efficiently handle future outbreaks and improve patient outcomes.

VR and machine learning: novel pathways in surgical hands-on training.

PURPOSE OF REVIEW: Surgical training has dramatically changed over the last decade. It has become not only the way to prepare surgeons for their everyday work, but also a way to certify their skills thus increasing patient safety. This article reviews advances in the use of machine learning and artificial intelligence applied to virtual reality based surgical training over the last 5 years. RECENT FINDINGS: Eight articles have been published which met the inclusion criteria. This included six articles about the use of machine learning and artificial intelligence for assessment purposes and two articles about the possibility of teaching applications, including one review and one original research article. All the research articles pointed out the importance of machine learning and artificial intelligence for the stratification of trainees, based on their performance on basic tasks or procedures simulated in a virtual reality environment. SUMMARY: Machine learning and artificial intelligence are designed to analyse data and use them to take decisions that typically require human intelligence. Evidence in literature is still scarce about this technology applied to virtual reality and existing manuscripts are mainly focused on its potential to stratify surgical performance and provide synthetic feedbacks about it. In consideration of the exponential growth of computer calculation capabilities, it is possible to expect a parallel increase of research about this topic within the next few years.

Mobile Screening Units for the Early Detection of Breast Cancer and Cardiovascular Disease: A Pilot Telemedicine Study in Southern Italy.

Introduction: Telemedicine is the use of Information and Communication Technologies (ICT) to improve patient outcomes by increasing access to care, medical information and services. The aim of this pilot study was to evaluate and support the implementation of screening and early detection programs in the prevention of breast cancer and cardiovascular diseases with the establishment of a remote diagnosis through the use of ICT in mobile units. Materials and Methods: A total of 430 individuals were recruited in an area of Southern Italy. Particularly, 321 women were recruited to undergo breast cancer screening in accordance with Italian guidelines. Likewise, cardiovascular screening interested 109 subjects. A self-contained mobile unit with connectivity was provided to offer breast and cardiovascular screenings. To maximize the benefit, we have evaluated the return of investment. Results: The telemedicine screening program allowed the detection of early pathologies. In breast cancer screening, 40.8% of cases were negative to lesions, 34.9% were positive to benign lesions, and 3.1% presented suspicious malignant lesions; these lesions were further checked by histological analyses, which showed a positive response in 70% of cases. The cardiovascular screening concerned 109 participants based on age and other risk factors. We observed a significant difference among risk factors in patients with cardiac disease (p < 0.001); particularly, hypertension was significantly the most present risk factor (51.4%, p < 0.05), followed by smoking (28.4%, p < 0.05). A cardiovascular pathology was detected in 40.4% of enrolled subjects. A 3.3:1 return on investment was calculated. Conclusion: Our findings demonstrate that telemedicine may represent a promising approach to deliver several health services, such as screening programs, with users who cannot utilize services in their locations. The use of telemedicine on diagnostic campers greatly reduces the costs of screening for breast cancer and major cardiovascular diseases within the Southern Italian Health Service. We believe that public investment can have a further significant return on investment by implementing the principles of precision medicine.

Towards AI-driven longevity research: An overview.

While in the past technology has mostly been utilized to store information about the structural configuration of proteins and molecules for research and medical purposes, Artificial Intelligence is nowadays able to learn from the existing data how to predict and model properties and interactions, revealing important knowledge about complex biological processes, such as aging. Modern technologies, moreover, can rely on a broader set of information, including those derived from the next-generation sequencing (e.g., proteomics, lipidomics, and other omics), to understand the interactions between human body and the external environment. This is especially relevant as external factors have been shown to have a key role in aging. As the field of computational systems biology keeps improving and new biomarkers of aging are being developed, artificial intelligence promises to become a major ally of aging research.

Digital biomarkers and sex impacts in Alzheimer's disease management - potential utility for innovative 3P medicine approach.

Digital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected and measured by means of digital devices. Their use has revolutionized clinical research by enabling high-frequency, longitudinal, and sensitive measurements. In the field of neurodegenerative diseases, an example of a digital biomarker-based technology is instrumental activities of daily living (iADL) digital medical application, a predictive biomarker of conversion from mild cognitive impairment (MCI) due to Alzheimer's disease (AD) to dementia due to AD in individuals aged 55 + . Digital biomarkers show promise to transform clinical practice. Nevertheless, their use may be affected by variables such as demographics, genetics, and phenotype. Among these factors, sex is particularly important in Alzheimer's, where men and women present with different symptoms and progression patterns that impact diagnosis. In this study, we explore sex differences in Altoida's digital medical application in a sample of 568 subjects consisting of a clinical dataset (MCI and dementia due to AD) and a healthy population. We found that a biological sex-classifier, built on digital biomarker features captured using Altoida's application, achieved a 75% ROC-AUC (receiver operating characteristic - area under curve) performance in predicting biological sex in healthy individuals, indicating significant differences in neurocognitive performance signatures between males and females. The performance dropped when we applied this classifier to more advanced stages on the AD continuum, including MCI and dementia, suggesting that sex differences might be disease-stage dependent. Our results indicate that neurocognitive performance signatures built on data from digital biomarker features are different between men and women. These results stress the need to integrate traditional approaches to dementia research with digital biomarker technologies and personalized medicine perspectives to achieve more precise predictive diagnostics, targeted prevention, and customized treatment of cognitive decline. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-022-00284-3.

Simulating SARS-CoV-2 epidemics by region-specific variables and modeling contact tracing app containment.

Targeted contact-tracing through mobile phone apps has been proposed as an instrument to help contain the spread of COVID-19 and manage the lifting of nation-wide lock-downs currently in place in USA and Europe. However, there is an ongoing debate on its potential efficacy, especially in light of region-specific demographics. We built an expanded SIR model of COVID-19 epidemics that accounts for region-specific population densities, and we used it to test the impact of a contact-tracing app in a number of scenarios. Using demographic and mobility data from Italy and Spain, we used the model to simulate scenarios that vary in baseline contact rates, population densities, and fraction of app users in the population. Our results show that, in support of efficient isolation of symptomatic cases, app-mediated contact-tracing can successfully mitigate the epidemic even with a relatively small fraction of users, and even suppress altogether with a larger fraction of users. However, when regional differences in population density are taken into consideration, the epidemic can be significantly harder to contain in higher density areas, highlighting potential limitations of this intervention in specific contexts. This work corroborates previous results in favor of app-mediated contact-tracing as mitigation measure for COVID-19, and draws attention on the importance of region-specific demographic and mobility factors to achieve maximum efficacy in containment policies.

Training Simulators for Gastrointestinal Endoscopy: Current and Future Perspectives.

Gastrointestinal (GI) endoscopy is the gold standard in the detection and treatment of early and advanced GI cancers. However, conventional endoscopic techniques are technically demanding and require visual-spatial skills and significant hands-on experience. GI endoscopy simulators represent a valid solution to allow doctors to practice in a pre-clinical scenario. From the first endoscopy mannequin, developed in 1969, several simulation platforms have been developed, ranging from purely mechanical systems to more complex mechatronic devices and animal-based models. Considering the recent advancement of technologies (e.g., artificial intelligence, augmented reality, robotics), simulation platforms can now reach high levels of realism, representing a valid and smart alternative to standard trainee/mentor learning programs. This is particularly true nowadays, when the current demographic trend and the most recent pandemic demand, more than ever, the ability to cope with many patients. This review offers a broad view of the technology available for GI endoscopy training, including platforms currently in the market and the relevant advancements in this research and application field. Additionally, new training needs and new emerging technologies are discussed to understand where medical education is heading.