Artificial Intelligence in the healthcare of older people
Published on: 20th March, 2020
OCLC Number/Unique Identifier: 8559314473
Clinical applications of Artificial Intelligence (AI) in healthcare are relatively rare. The high expectations in relation to data analysis influencing general healthcare have not materialized, with few exceptions, and then predominantly in the field of rare diseases, oncology and pathology, and interpretation of laboratory results. While electronic health records, introduced over the last decade or so in the UK have increased access to medical and treatment histories of patients, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, laboratory and test results, these have potential for evidence-based tools that providers can use to make decisions about a patient’s care, as well as streamline workflow. In the following text, we review the advances achieved using machine learning and deep learning technology, as well as robot use and telemedicine in the healthcare of older people.
Key points:
1. Artificial Intelligence use is extensively explored in prevention, diagnosis, novel drug designs and after-care.
2. AI studies on older adults include a small number of patients and lack reproducibility needed for their wider clinical use in different clinical settings and larger populations.
3. Telemedicine and robot assisted technology are well received by older service users.
4. Ethical concerns need to be resolved prior to wider AI use in routine clinical setting.
WMW: A Secure, Web based Middleware for C4I Interoperable Applications
Published on: 19th January, 2017
OCLC Number/Unique Identifier: 7286357212
Modern-day enhancements in Enterprise Architectures (EA) has increased the interoperability issues in almost all domains; these issues are increasing day-by-day as organizations are spanning and information is being exchanged between different platforms. Command Control Computer Communication and Intelligence (C4I) complex systems are also facing the interoperability issues due to highly classified and sensitive information being exchanged. In this paper we have discussed the integration of different C4I applications running under heterogeneous platforms by allowing them to communicate using a secure and ciphered web based middleware named as Web Middleware (WMW). This middleware is a client-server based web adaptor to achieve clean, systematic, secure and reliable communication. The main feature among many is the simple HTTP browser based customization that do not require any specific or special add-ons and controls to be installed on the client machine. Architecture usage, and initialization of the WMW middleware is discussed with security and performance discussion.
What can Mathematics say about unsolved problems in Medicine?
Published on: 3rd January, 2018
OCLC Number/Unique Identifier: 7355939664
Nobody doubts that mathematics plays a crucial role in medical achievements. It is certain that is being mainly used in statistics and physics for biomedical problems [1]. For sure that we have already heard about how mathematics can improve the anticancer arsenal [2]. Quantitative genetics have triggered a giant potential in medical care [3,4]. And mathematical algorithms, provided by artificial intelligence, continuously boost new therapeutic paradigms [5,6]. Nonetheless, one cannot ignore the ability of mathematics for analyzing ideas.
Moving space-matter as the basis of the intelligence in the Physical World
Published on: 28th March, 2019
OCLC Number/Unique Identifier: 8188232631
The concept of space-matter motion in the new Cartesian physics, based on the identity of space and matter, creates the basis for the study of consciousness as the action of the brain in space inside and outside itself and offers a way of materialistic explanation of life on Earth. She claims that consciousness in living matter arises when the brain begins to create the surrounding space the image of themselves and the world. And since space according to Descartes is identical to matter, the images created by the brain of itself and the external world in the surrounding space have a material basis and therefore the displayed organs interact with each other and the external world.
Modeling of A.I based Inhalation for Advanced Life Support System Development
Published on: 27th November, 2019
OCLC Number/Unique Identifier: 8452139651
Present piece of idea exhibits to divert attention towards automated high precision Life Support System (LSS) instead of manual one using medical intelligence devices while treating and diagnosis to the patient, where Ventilator, inhaler and respiratory control is most important factor during operation, surgeries and in other likewise medical emergency situations to maintain proper saturation in patient lungs to sustain their lives. This work gives idea, how we can design A.I based Inhaler System for the same.
A review article on artificial intelligence
Published on: 19th May, 2021
OCLC Number/Unique Identifier: 9048248509
Artificial intelligence (AI) is the emulation of human intelligence in computers that have been trained to think and behave like humans. The word may also refer to any computer that exhibits human-like characteristics like learning and problem-solving. Artificial intelligence is intelligence demonstrated by machines, as opposed to natural intelligence, which involves consciousness and emotionality and is demonstrated by humans and animals [1].
Developmentally appropriate practices on knowledge skills for contributing child’s intelligences of receptive language skills in appropriate and inappropriate early childhoods
Published on: 10th September, 2021
OCLC Number/Unique Identifier: 9252226608
To investigate the variables correlation analysis research method for assessing the caregivers’ perceptions in two groups including dependent and independent variables to correlate the measuring of early childhoods. Typically, in correlated data, for jointly normally distributed data with relevant outliers that can use a correlation as a measure of a monotonic association. Designing the 65-paired samples for the Thai Model of early detection and intervention of children as the health care system guidelines from 26-CUPs have compared. Using the DSPM divided into 65-appropriate and 65-inappropriate development early childhoods for every 13 CUPS that depends on talented children. Selecting the Receptive Language (RL) skills identified in contributing growth relative factors with four research instruments: the EPRLS, PRLF, CNRLF, and CMRLF are valid and reliable significantly. Comparisons of the appropriate and inappropriate early childhoods are differences ( < .05), the intercorrelation circumflex nature analysis (p < .05), positively. The R2 values show that 26% and 55% of the variance in training caregivers’ factor skills on the PRLF, CNRLF, and CMRLF to the EPRLS in inappropriate and appropriate early childhoods, respectively. Developmentally Appropriate Practice is a perspective in a child’s development: social, emotional, physical, and cognitive-based on the child’s cultural background: community, family history, and family structure.
Awareness level on the relevance of forensics in criminal investigation in Nigeria
Published on: 15th October, 2021
OCLC Number/Unique Identifier: 9305467463
The main aim of forensic science is to gather intelligence to enable the judge to credible and logical decisions in the court by means of scientific approach through evaluation of evidence for the administration of justice, and country around the world now considers forensic methodology as the gold standard for criminal investigation. Therefore, the present study examined the level of awareness on the relevance of forensics in criminal investigation in Nigeria. The design used in this study is the survey research design and the sample size of this study was a total of one hundred personnel of law enforcement and the judiciary. The study adopted descriptive statistics which involves the use of frequency and percentage. The result of the present study revealed that the participants were distributed socio-demographically as follows; there was an observable higher number of male participants (68%) relative to the female participants (32%), As per age distribution, a larger population of the participants were found to be > 40 years of age with 55%, and it was observed that age between 35-39 years ranked the least with 15%. On educational level, the result of the present study revealed that majority of the participants possesses a bachelor’s degree as the highest level of educational qualification with 75% from a pool of 100% of participants. The present study further examined responses on the relevance of forensics in criminal investigation, and the result revealed an inadequate level of awareness on the relevance of forensics in criminal investigation. Therefore, the study recommends that the Nigerian Police Force and the Judiciary should collaborate with Universities running programs on forensics for trainings.
Applications of convolutional neural networks in chest X-ray analyses for the detection of COVID-19
Published on: 20th January, 2022
OCLC Number/Unique Identifier: 9391752257
Throughout global efforts to defend against the spread of COVID-19 from late 2019 up until now, one of the most crucial factors that has helped combat the pandemic is the development of various screening methods to detect the presence of COVID-19 as conveniently and accurately as possible. One of such methods is the utilization of chest X-Rays (CXRs) to detect anomalies that are concurrent with a patient infected with COVID-19. While yielding results much faster than the traditional RT-PCR test, CXRs tend to be less accurate. Realizing this issue, in our research, we investigated the applications of computer vision in order to better detect COVID-19 from CXRs. Coupled with an extensive image database of CXRs of healthy patients, patients with non-COVID-19 induced pneumonia, and patients positive with COVID-19, convolutional neural networks (CNNs) prove to possess the ability to easily and accurately identify whether or not a patient is infected with COVID-19 in a matter of seconds. Borrowing and adjusting the architectures of three well-tested CNNs: VGG-16, ResNet50, and MobileNetV2, we performed transfer learning and trained three of our own models, then compared and contrasted their differing precisions, accuracies, and efficiencies in correctly labeling patients with and without COVID-19. In the end, all of our models were able to accurately categorize at least 94% of the CXRs, with some performing better than the others; these differences in performance were largely due to the contrasting architectures each of our models borrowed from the three respective CNNs.
Radiation-induced salivary gland damage/dysfunction in head and neck cancer: Nano-bioengineering strategies and artificial intelligence for prevention, therapy and reparation
Published on: 20th December, 2022
Saliva is produced by and secreted from salivary glands. It is an extra-cellular fluid, 98% water, plus electrolytes, mucus, white blood cells, epithelial cells, enzymes, and anti-microbial agents. Saliva serves a critical role in the maintenance of oral, dental, and general health and well-being. Hence, alteration(s) in the amount/quantity and/or quality of secreted saliva may induce the development of several oro-dental variations, thereby the negatively-impacting overall quality of life. Diverse factors may affect the process of saliva production and quantity/quality of secretion, including medications, systemic or local pathologies and/or reversible/irreversible damage. Herein, chemo- and/or radio-therapy, particularly, in cases of head and neck cancer, for example, are well-documented to induce serious damage and dysfunction to the radio-sensitive salivary gland tissue, resulting in hypo-salivation, xerostomia (dry mouth) as well as numerous other adverse Intra-/extra-oral, medical and quality-of-life issues. Indeed, radio-therapy inevitably causes damage to the normal head and neck tissues including nerve structures (brain stem, spinal cord, and brachial plexus), mucous membranes, and swallowing muscles. Current commercially-available remedies as well as therapeutic interventions provide only temporary symptom relief, hence, do not address irreversible glandular damage. Further, despite salivary gland-sparing techniques and modified dosing strategies, long-term hypo-function remains a significant problem. Although a single governing mechanism of radiation-induced salivary gland tissue damage and dysfunction has not been yet elucidated, the potential for synergy in radio-protection (mainly, and possibly -reparation) via a combinatorial approach of mechanistically distinct strategies, has been suggested and explored over the years. This is, undoubtfully, in parallel to the ongoing efforts in improving the precision, safety, delivery, and efficacy of clinical radiotherapy protocols/outcomes, and in designing, developing, evaluating and optimizing (for translation) new artificial intelligence, technological and bio-pharmaceutical alternatives, topics covered in this review.
Artificial awareness, as an innovative learning method and its application in science and technology
Published on: 24th February, 2023
The creation of the information society is associated with the creation of new intellectual, cultural, spiritual and material values, as well as with new principles and methods of social and interpersonal communication. Achieving this goal is impossible without changes in teaching methodology, teaching technologies and teacher’s work.The article is an overview and focuses on the following issues. In the information society, the era of biocomputers and quantum computers is coming, which will use not only artificial intelligence, but also artificial consciousness for simulation. Artificial awareness builds the foundations for the development of robots that will be widely used in various fields of industry and science. - Artificial awareness combined with artificial intelligence can be an innovative method in education and communication; - Quantum computers and biocomputers will find wide application in human education and social life;
The effects of hemp seed consumption on depression, anxiety and cognitive improvement based on machine learning
Published on: 11th March, 2023
Background: Hemp seed (Cannabis sativa L.) is an annual herbaceous plant of the Cannabis genus that contains a large amount of protein, iron, and fatty acids, including linoleic, α-linolenic, and γ-linolenic acid. These compounds are involved in a number of biological activities, including immunity enhancement, hyperlipidemia, and inflammation reduction. Here, we investigated the antioxidant effects of hemp seed on human cognitive function.Methods: The test was administered to 34 healthy volunteers aged ≥ 20 years. Participants were selected according to age and sex and were administered 10 g of hemp seed three times daily (30 g/day) for 45 days. The outcome measurements were recorded using a survey, computerized neurocognitive tests, and artificial intelligence.Results: Survey analysis determined that both the Beck Anxiety Inventory and Beck Depression Inventory measurements decreased significantly after hemp seed consumption when compared to measurements taken before consumption (p < 0.05). Additionally, significant results were observed in the Stroop and Tower of London tasks (p < 0.05). The prediction performance for the antidepressant effect was 0.83 for the area under the curve in the random forest algorithm, which was superior to that of other machine learning methods. Conclusion: These results suggest that hemp seeds have a beneficial effect on cognitive impairment.
Current and emerging trends in oro-dental healthcare and cranio-maxillo-facial surgery
Published on: 14th April, 2023
Dentistry is an ever-evolving field that has seen significant advances in recent years. This article sheds light on some of the current and emerging trends in oral health care, including digital dentistry, regenerative medicine, and the use of lasers. For example, digital dentistry involves the use of computer-aided design and manufacturing technology, which enables more accurate and efficient production of dental devices. On the other hand, regenerative medicine and nanoDentistry can be considered promising area that combines the use of stem cells, growth factors, biomaterials, and nanotechnology to regenerate damaged tissue and improve treatment outcomes. Lasers are increasingly being used in dentistry for a range of applications, including the treatment of gum disease and teeth whitening. Other developing technologies such as 3D printing and artificial intelligence are also being increasingly incorporated into dentistry, providing improved treatment options for our patients. Last yet definitely would/will not least, controlled drug delivery systems are being developed to deliver drugs to specific target sites in a localized and sustained manner, reducing the risk of adverse effects. Currently, these emerging trends are transforming the landscape of odontology and beyond. Hence, in this mini-Review, we explore such trends in oro-dental and cranio-maxillo-facial indications to highlight the potential benefits, advancements, and opportunities of applications for improved patient care.
COVID-19 detection and classification: key AI challenges and recommendations for the way forward
Published on: 25th May, 2023
Coronavirus disease (COVID-19) is a viral pneumonia that is found in China and has spread globally. Early diagnosis is important for effective and timely treatment. Thus, many ongoing studies attempt to solve key COVID-19 problems such as workload classification, detection, and differentiation from other pneumonia and healthy lungs using different imaging modalities. Researchers have identified some limitations in the deployment of deep learning methods to detect COVID-19, but there are still unmet challenges to be addressed. The use of binary classifiers or building classifiers based on only a few classes is some of the limitations that most of the existing research on the COVID-19 classification problem suffers from. Additionally, most prior studies have focused on model or ensemble models that depend on a flat single-feature imaging modality without using any clinical information or benefiting from the hierarchical structure of pneumonia, which leads to clinical challenges, and evaluated their systems using a small public dataset. Additionally, reliance on diagnostic processes based on CT as the main imaging modality, ignoring chest X-rays. Radiologists, computer scientists, and physicians all need to come to an understanding of these interdisciplinary issues. This article first highlights the challenges of deep learning deployment for COVID-19 detection using a literature review and document analysis. Second, it provides six key recommendations that could assist future researchers in this field in improving the diagnostic process for COVID-19. However, there is a need for a collective effort from all of them to consider the provided recommendations to effectively solve these issues.
Neurointerventional Radiology: History, Present and Future
Published on: 20th June, 2023
Neurointerventional Radiology (NIR), encompassing neuroendovascular surgery, endovascular neurosurgery, and interventional neurology, is an innovative and rapidly evolving multidisciplinary specialty focused on minimally invasive therapies for a wide range of neurological disorders. This review provides a comprehensive overview of NIR, discussing the three routes into the field, highlighting their distinct training paradigms, and emphasizing the importance of unified approaches through organizations like the Society of Neurointerventional Surgery (SNIS).The paper explores the benefits of co-managed care and its potential to improve patient outcomes, as well as the role of interdisciplinary collaboration and cross-disciplinary integration in advancing the field. We discuss the various contributions of neurosurgery, radiology, and neurology to cerebrovascular surgery, aiming to inform and educate those interested in pursuing a career in neurointervention. Additionally, the review examines the adoption of innovative technologies such as robotic-assisted techniques and artificial intelligence in NIR, and their implications for patient care and the future of the specialty. By presenting a comprehensive analysis of the field of neurointervention, we hope to inspire those considering a career in this exciting and rapidly advancing specialty, and underscore the importance of interdisciplinary collaboration in shaping its future.
Harnessing Artificial Intelligence for Early and Evolution of Alzheimer’s Disease Detections and Enhancing Senior Mental Health through Innovative Art-Singing Therapies: A Multidisciplinary Approach
Published on: 28th June, 2023
The well-documented therapeutic potential of group singing for patients living with Alzheimer’s disease (PLAD) has been hindered by COVID-19 restrictions, exacerbating loneliness and cognitive decline among seniors in residential and long-term care centers (CHSLDs). Addressing this challenge, the multidisciplinary study aims to develop a patient-oriented virtual reality (XR) interaction system facilitating group singing for mental health support during confinement and enhancing the understanding of the links between Alzheimer’s disease, social interaction, and singing. The researchers also propose to establish an early AD detection system using voice, facial, and non-invasive biometric measurements and validate the efficacy of selected intervention practices. The methodology involves co-designing an intelligent environment with caregivers to support PLAD mental health through online group singing, addressing existing constraints in CHSLDs. The researchers will engage volunteers in remote singing interactions and validate the impact of voice stimulation for PLADs using a control group. The primary expected outcome is the development of an “Intelligent Learning Health Environment,” fostering interactions while adapting to individual PLAD situations and incrementally accumulating knowledge on AD signs. This environment will facilitate the transfer of knowledge and technologies to promote non-verbal interactions via singing, enabling intervention at the first symptoms. Additionally, the research will contribute to transforming CHSLDs’ living environments, informed by neuroscience insights, and potentially extend the “collaborative self-care” approach to support seniors in aging safely and healthily at home.
Comparative Study of Cerebral Volumetric Variations in Patients with Schizophrenia with their Unaffected First-degree Relatives, using Magnetic Resonance Imaging Technique, a Case-control Study
Published on: 3rd January, 2024
Background and purpose: Schizophrenia (SZH) is a chronic mental disorder affecting the individuals` thoughts, perceptions, emotions, and behaviors. People with SZH may experience a wide range of positive, negative, and cognitive symptoms. Since there are no laboratory assays for definite SZH diagnosis, the authors aimed to identify the cerebral volumetric variations in SZH patients with the most prevalent positive symptoms as a diagnostic tool. This study selected 15 SZH patients displaying the most prevalent positive symptoms based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria. Assessment tools included the Mini-Mental State Examination (MMSE) for cognitive impairment, the Positive and Negative Syndrome Scale (PANSS) for symptom evaluation, and the Wechsler Intelligence Scale (WIS) for intelligence assessment. Additionally, 15 Healthy Controls (HC) without cerebral pathologies were recruited. T1w MRI images underwent analysis using Freesurfer software. Data analysis employed Mann-Whitney U and χ² tests, considering p < 0.05 as significant.Results: SZH and HC groups showed no significant differences in age and gender. However, significant (p < 0.05) alterations in Gray Matter (GM) volume were observed in SZH patients compared to HC. In the right hemisphere, several regions exhibited volume reduction, including the Fusiform sulcus, Rostral middle frontal gyrus, isthmus cingulate, Frontal pole, Middle temporal gyrus, Lateral occipital gyrus, and Inferior Parietal gyrus. Notably, the Precentral sulcus and Postcentral gyrus demonstrated volume acceleration. Similarly, in the left hemisphere, various regions showed volume reduction while the Paracentral gyrus indicated volume acceleration, all significant (p < 0.05).Conclusion: SZH patients display significant volumetric brain changes, indicating potential for future diagnostic procedures in SZH.
Exploring the Prognostic Efficacy of Machine Learning Models in Predicting Adenocarcinoma of the Esophagogastric Junction
Published on: 7th March, 2024
Objective: To investigate the value of machine learning and traditional Cox regression models in predicting postoperative survivorship in patients with adenocarcinoma of the esophagogastric junction (AEG).Methods: This study analyzed clinicopathological data from 203 patients. The Cox proportional risk model and four machine learning models were constructed and internally validated. ROC curves, calibration curves, and clinical decision curves (DCA) were generated. Model performance was assessed using the area under the curve (AUC), while calibration curves determined the fit and clinical significance of the model.Results: The AUC values of the 3-year survival in the validation set for the Cox regression model, extreme gradient boosting, random forest, support vector machine, and multilayer perceptron were 0.870, 0.901, 0.791, 0.832, and 0.725, respectively. The AUC values of 5-year survival in the validation set for each model were 0.915, 0.916, 0.758, 0.905, and 0.737, respectively. The internal validation AUC values for the four machine learning models, extreme gradient boosting, random forest, support vector machine, and multilayer perceptron, were 0.818, 0.772, 0.804, and 0.745, respectively.Conclusion: Compared with Cox regression models, machine learning models do not need to satisfy the assumption of equal proportionality or linear regression models, can include more influencing variables, and have good prediction performance for 3-year and 5-year survival rates of AEG patients, among which, XGBoost models are the most stable and have significantly better prediction performance than other machine learning methods and are practical and reliable.
Methodology for Studying Combustion of Solid Rocket Propellants using Artificial Neural Networks
Published on: 11th March, 2024
The combustion properties of energetic materials have been extensively studied in the scientific literature. With the rapid advancement of data science and artificial intelligence techniques, predicting the performance of solid rocket propellants (SRPs) has become a key focus for researchers globally. Understanding and forecasting the characteristics of SRPs are crucial for analyzing and modeling combustion mechanisms, leading to the development of cutting-edge energetic materials. This study presents a methodology utilizing artificial neural networks (ANN) to create multifactor computational models (MCM) for predicting the burning rate of solid propellants. These models, based on existing burning rate data, can solve direct and inverse tasks, as well as conduct virtual experiments. The objective functions of the models focus on burning rate (direct tasks) and pressure (inverse tasks). This research lays the foundation for developing generalized combustion models to forecast the effects of various catalysts on a range of SRPs. Furthermore, this work represents a new direction in combustion science, contributing to the creation of a High-Energetic Materials Genome that accelerates the development of advanced propellants.
Harmonizing Artificial Intelligence Governance; A Model for Regulating a High-risk Categories and Applications in Clinical Pathology: The Evidence and some Concerns
Published on: 18th March, 2024
The Canadian healthcare system, grappling with issues like systemic and intelligently established structural anti-black racism, including indigenous nations; even within Pathology and Laboratory Medicine Communities: and deteriorating outcomes, sees potential in AI to address challenges, though concerns exist regarding exacerbating discriminatory practices. In clinical pathology, AI demonstrates superior diagnostic accuracy compared to pathologists in a study, emphasizing its potential to improve healthcare. However, AI governance is crucial to navigating ethical, legal, and societal concerns. The Royal College of Physicians of Canada acknowledges the transformative impact of AI in healthcare but stresses the need for responsible AI tools co-developed by diverse teams. Despite positive attitudes towards AI in healthcare, concerns about patient safety, privacy, and autonomy highlight the necessity for comprehensive education, engagement, and collaboration. Legal concerns, including liability and regulation, pose challenges, emphasizing the need for a robust regulatory framework. AI application in healthcare is categorized as high-risk, demanding stringent regulation to ensure safety, efficacy, and fairness. A parallel is drawn to drug regulation processes, suggesting a similar approach for AI. The lack of transparency in AI-based decision-making raises ethical questions, necessitating measures to address biases and ensure patient privacy. Social accountability is crucial to prevent AI from exacerbating health disparities and harming marginalized communities. In conclusion, while AI offers potential benefits in clinical pathology, a cautious approach with comprehensive governance measures is essential to mitigate risks and ensure ethical AI integration into healthcare.
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