International Journal of Health Systems and Translational Medicine
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Medical image registration has important value in actual clinical applications. From the traditional time-consuming iterative similarity optimization method to the short time-consuming supervised deep learning to today's unsupervised learning, the continuous optimization of the registration strategy makes it more feasible in clinical applications. This survey mainly focuses on unsupervised learning methods and introduces the latest solutions for different registration relationships. The registration for inter-modality is a more challenging topic. The application of unsupervised learning in registration for inter-modality is the focus of this article. In addition, this survey also proposes ideas for future research methods to show directions of the future research.


The development of a telehealth technology in an academic setting is a complex project that faces several obstacles. The early assessment of the project risks plays a critical role in the translation of promising telehealth innovations into healthcare practice. This paper presents a decision support tool based on Failure Mode and Effects Analysis (FMEA) and Quality Function Deployment (QFD) techniques to associate the project risks to relevant success factors. Certain modifications in both techniques are applied to deploy them for project risk assessment. The project risks and success factors used in the tool are identified from the literature. The proposed decision support tool enables researchers to manage the risks in their telehealth development projects and identify action items to overcome such risks. The application of the proposed tool is illustrated with a telehealth development project for virtual physical therapy.


Ethics is critical in emergency response to public health and patient care in ways that create a variety of challenging dilemmas and decisions. Understanding ethical codes around medical care, especially during the emergence of COVID 19, has made leadership's role in perpetuating ethical organizational cultures in healthcare vital. Ethical leadership and ethical organizational cultures transform and unite social systems around everyday purposes of ethical decision-making, leveraging organizational connectedness. Leadership value systems mitigate subjectivity constituting ethical themes of moral character and virtues to advance organizational trust. Leadership value systems reduce subjectivity, forming ethical issues of moral character and virtues to promote organizational confidence and moral organizational decision-making. This paper employs the use of content analysis from the literature to take disjointed approaches and combine them into a cohesive understanding of leadership dynamics on organizational ethics in healthcare.


Medical image registration has important value in actual clinical applications. From the traditional time-consuming iterative similarity optimization method to the short time-consuming supervised deep learning to today's unsupervised learning, the continuous optimization of the registration strategy makes it more feasible in clinical applications. This survey mainly focuses on unsupervised learning methods and introduces the latest solutions for different registration relationships. The registration for inter-modality is a more challenging topic. The application of unsupervised learning in registration for inter-modality is the focus of this article. In addition, this survey also proposes ideas for future research methods to show directions of the future research.


Epilepsy is caused by the abnormal discharge of the patient's brain. Smart medical uses advanced technologies such as signal recognition and machine learning to identify and analyze the biological signals fed back from the subjects’ brain electrical signals and provide diagnostic results. In the past, doctors used their own experience and theoretical knowledge to judge whether there are characteristic signals by observing the subject’s EEG signal to realize the judgment of the condition. This method of diagnosis through observation often infuses the doctor's own subjective judgment, leading to misdiagnosis of the condition and low diagnosis and treatment efficiency. With the continuous development of advanced technologies such as artificial intelligence and signal recognition, this provides new ideas for the realization of EEG signal recognition and processing technology and opens up new development paths. This article is based on epilepsy EEG signal data, realizes EEG signal processing and uses machine learning methods to realize EEG signal identification and diagnosis.


Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a potentially fatal disease that prompted health disasters worldwide. The virus was reported first from Wuhan, China, in December 2019. SARS-CoV-2 majorly transmits through direct contactwith an infected person or inhalation.The spread rate of SARS-CoV-2 is much higher than the other virus of family. The virus is very harmful to the children, people with old age, low immunity, or suffering from other critical diseases. A total of 29.6 million infected cases and approximately 936000 death were reported worldwide. Whereas in India reached 5.02 Million cases are reported with 82000 deaths. In this paper, the authors had study the Origin of viruses, Symptoms, actions taken by the Indian government, and precautions suggested to healthcare workers. The biometric system's adverse effects in hospitals are highlighted, and authors emphasize IoT-based smart door-lock that works without direct contact. The proposed system helps in reducing contamination at healthcare centers.


Epilepsy is caused by the abnormal discharge of the patient's brain. Smart medical uses advanced technologies such as signal recognition and machine learning to identify and analyze the biological signals fed back from the subjects’ brain electrical signals and provide diagnostic results. In the past, doctors used their own experience and theoretical knowledge to judge whether there are characteristic signals by observing the subject’s EEG signal to realize the judgment of the condition. This method of diagnosis through observation often infuses the doctor's own subjective judgment, leading to misdiagnosis of the condition and low diagnosis and treatment efficiency. With the continuous development of advanced technologies such as artificial intelligence and signal recognition, this provides new ideas for the realization of EEG signal recognition and processing technology and opens up new development paths. This article is based on epilepsy EEG signal data, realizes EEG signal processing and uses machine learning methods to realize EEG signal identification and diagnosis.


Urine drug screens (UDSs) are often performed in the emergency department (ED) as part of a standard ED order set in patients with significant altered mental status, trauma, or seizures usually without the patient’s knowledge or specified informed consent. In the ED the UDS has been included in the standard consent to treatment for routine testing along with blood studies, EKG, urinalysis and radiology. Many technical factors are known to effect UDS results.There is a lack of education among physicians regarding the clinical pitfalls of UDS interpretation. This article discusses the current state and issues associated with the UDS, and presents three clinical vignettes that illustrate the impact of false-positive UDS results on patient care and the potential for a patient becoming unknowingly and unfairly stigmatized. The article also offers suggestions including a requirement for either formal informed consent or an “opt out” screening process, as recommended by the CDC in HIV testing, designed to protect patient autonomy and confidentiality.


Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a potentially fatal disease that prompted health disasters worldwide. The virus was reported first from Wuhan, China, in December 2019. SARS-CoV-2 majorly transmits through direct contactwith an infected person or inhalation.The spread rate of SARS-CoV-2 is much higher than the other virus of family. The virus is very harmful to the children, people with old age, low immunity, or suffering from other critical diseases. A total of 29.6 million infected cases and approximately 936000 death were reported worldwide. Whereas in India reached 5.02 Million cases are reported with 82000 deaths. In this paper, the authors had study the Origin of viruses, Symptoms, actions taken by the Indian government, and precautions suggested to healthcare workers. The biometric system's adverse effects in hospitals are highlighted, and authors emphasize IoT-based smart door-lock that works without direct contact. The proposed system helps in reducing contamination at healthcare centers.


Medical image registration has important value in actual clinical applications. From the traditional time-consuming iterative similarity optimization method to the short time-consuming supervised deep learning to today's unsupervised learning, the continuous optimization of the registration strategy makes it more feasible in clinical applications. This survey mainly focuses on unsupervised learning methods and introduces the latest solutions for different registration relationships. The registration for inter-modality is a more challenging topic. The application of unsupervised learning in registration for inter-modality is the focus of this article. In addition, this survey also proposes ideas for future research methods to show directions of the future research.


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