Severe hypoxemia after radiofrequency ablation for atrial fibrillation in palliatively repaired tetralogy of Fallot: A case report

BACKGROUND Patients with tetralogy of Fallot(TOF)often have arrhythmias,commonly being atrial fibrillation(AF).Radiofrequency ablation is an effective treatment for AF and does not usually cause severe postoperative hypoxemia,but the risk of complications may increase in patients with conditions such as TOF.CASE SUMMARY We report a young male patient with a history of TOF repair who developed severe hypoxemia after radiofrequency ablation for AF and was ultimately confirmed to have a new right-to-left shunt.The patient subsequently underwent atrial septal occlusion and eventually recovered.CONCLUSION Radiofrequency ablation may cause iatrogenic atrial septal injury;thus possible complications should be predicted in order to ensure successful treatment and patient safety.

A machine learning approach for predictings stroke

Background:Stroke is one of the most dangerous and life-threatening disease as it can cause lasting brain damage,long-term disability,or even death.The early detection of warning signs of a stroke can help save the life of a patient.In this paper,we adopted machine learning approaches to predict strokes and identify the three most important factors that are associated with strokes.Methods:This study used an open-access stroke prediction dataset.We developed 11 machine learning models and compare the results to those found in prior studies.Results:The accuracy,recall and area under the curve for the random forest model in our study is significantly higher than those of other studies.Machine learning models,particularly the random forest algorithm,can accurately predict the risk of stroke and support medical decision making.Conclusion:Our findings can be applied to design clinical prediction systems at the point of care.

BMRMIA:A Platform for Radiologists to Systematically Learn Automated Medical Image Analysis by Three Dimensional Medical Decision Support System

Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis technology.The platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system,and strengthen the teaching practice of deep learning related courses in hospitals,so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary diagnosis.Background:In recent years,deep learning has been widely used in academia,industry,andmedicine.An increasing number of companies are starting to recruit a large number of professionals in the field of deep learning.Increasing numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis techniques.For now,however,there is no practical training platform that can help radiologists learn automated medical image analysis systematically.ApplicationDesign:The platform proposes the basic learning,model combat,business application(BMR)concept,including the learning guidance system and the assessment training system,which constitutes a closed-loop learning guidance mode of“learning-assessment-training-learning”.Findings:The survey results show that most of radiologists met their learning expectations by using this platform.The platform can help radiologists master deep learning techniques quickly,comprehensively and firmly.

Analysis of the interactive meaning of journalistic images of the human papillomavirus vaccine and the perceptions of female undergraduate students

Objectives:This paper focuses on the underlying mechanisms of women's perceptions of persuasive visual health information.Methods:In the image viewing process,a separation between the image producer and the image viewer occurs,and the connection between the two is fractured.This mixed method research included modal discourse analysis(coding based on visual grammar theory),an eye tracking experiment,a questionnaire survey,and in-depth semi-structured interviews.The interactive meanings of journalistic images related to the human papillomavirus(HPV)vaccine were identified through four sets of codes.In addition,the perceptions of female viewers were analyzed.Results:In the first set of stimuli,i.e.,the infographic,the female participants focused most of their attention on information about the nine-valent HPV vaccine.An analysis of the interactive meaning of two sets of journalistic pictures,i.e.,fictional pictures and nonfictional pictures,indicated that the image producers did not implement useful viewer involvement strategies to persuade viewers.Furthermore,female viewers focused their attention on the"similar other"during the viewing process,gazing at the patient the longest as the primary area of interest(AOI).Conclusions:The study indicates that the current persuasive visual information about the HPV vaccine needs further improvement due to the high demand for information about HPV from the Chinese female audience.