Journal of medical artificial intelligence最新文献

{"title":"Web-Based Apps in the fight against COVID-19","authors":"J. P. Sosa, M. M. Caceres, Jennifer Ross-Comptis, D. Hathaway, Jayati Mehta, Krunal Pandav, R. Pakala, Maliha Butt, Zeryab Dogar, Marie-Pierre Belizaire, Nada El Mazboudi, M. K. Pormento, Madiha Zaidi, Harshitha Mergey Devender, Hanyou Loh, Radhika Garimella, Niran Brahmbhatt","doi":"10.21037/JMAI-20-61","DOIUrl":"https://doi.org/10.21037/JMAI-20-61","url":null,"abstract":"When and where the first case of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) appeared, remains controversial. However, it has proven to be highly infectious and capable of rapid mutation. Within months, it spread to over 213 countries infecting 21.7 million people and causing 770,000 deaths. SARS-CoV-2 belongs to a virus family known as Coronaviridae. It is transmitted through minute respiratory droplets produced by coughing, sneezing, or talking in close proximity to one another. Another mode of transmission is by droplets, touching surfaces contaminated with the virus, and touching the face, eyes, or mouth with the contaminated hands. Symptoms of the viral infection appear in 1–14 days and include fever, cough, fatigue, general weakness, sore throat, and muscular pains, while in severe cases it can lead to acute respiratory distress syndrome (ARDS), severe pneumonia, and sepsis (1). Coronavirus Disease 2019 (COVID-19) was declared a pandemic by the World Health Organization (WHO) on March 11, 2020. Not much is known about the virus, but research is still ongoing, and the search for treatment is underway. Strict standard operating measures (SOPs) are being used in order to limit the spread of the virus until a vaccine is developed. The rapid spread of SARS-CoV-2 has resulted in several difficulties regarding accurate and timely information dissemination, controlling the spread rate, and public health planning. This pandemic has proven to be a unique situation since it was recommended to limit physical interactions to prevent infection (2,3). Due to the social distancing measures enforced by many countries, it is more difficult for people to receive medical attention quickly and safely. To overcome this problem, be more efficient, and be able to save more lives, the use of artificial intelligence (AI) has been introduced. This has helped promote telehealth and allow patients to receive care in the comfort of their homes and decrease the patient load on the already overflowing hospitals. SARS-CoV-2 is a highly contagious virus, and as health professionals are closely dealing with the affected people, the use of AI has helped to decrease inpatient visits, thereby decreasing the workload and exposure. Using applications (henceforth referred to as apps) has helped remotely monitor patients while keeping in mind doctor-patient confidentiality and secure communication between them. Contact tracing through the apps has helped identify the ‘hotspots’ for the virus, track the spread, and contain it (4). These apps can be used in population screening and getting day-to-day updates of the areas where new cases are emerging. The use of apps improves productivity and efficiency in studies with large samples (5). It is for this reason that web and mobile-based apps are being used during this pandemic situation. Several apps deployed in different areas of the world are being used to accelerate and aid the process of geographical mapping of case","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43330511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in the diagnosis and management of COVID-19: a narrative review","authors":"S. Ellahham","doi":"10.21037/JMAI-20-48","DOIUrl":"https://doi.org/10.21037/JMAI-20-48","url":null,"abstract":"As per November 2020, there have been over 51.5 million cases of COVID-19 in the world with its mortality rate being close to 7%, causing a major burden on health care systems. Artificial intelligence (AI) is a promising tool, the use of which has been encouraged for the development of an automated diagnosis system for COVID-19 minimising the drawback of limited reverse transcription polymerase chain reaction (RT-PCR) tests. It is a time-saving, cost-effective approach, which is being promoted for reducing the physician burden during the pandemic crisis. For this narrative review, most recent data sources were collected from PubMed and Cochrane Library. Deep Learning is a promising technology for the automated diagnosis of COVID-19 through the use of advanced algorithms that identify hidden patterns on patient radiographs. Machine learning is useful in predicting patient prognosis and biomarker analysis is helpful for customised treatment planning. Infrared thermal scanners, chatbot applications, AI-based decision-making systems and image analysers are some generic contributions of AI assisting in the contactless diagnosis in suspected patients. Overall, deep neural network-based approaches have found to be superior to RT-PCR in diagnosing COVID-19 having a sensitivity of 85.35% and a specificity of 92.18% in the image-intensive diagnosis of pneumonia. In patients with comorbid conditions, telemedicine is a significant contribution of AI for monitoring and diagnosis positive cases through the use of applications such as My Day for Senior on Alexa Daily Check. Despite these advantages, the use of AI is only recommended under the guidance of the physician until sufficient clinical trials are not conducted supporting its independent use. Conclusively, the role of AI is prominent in the detection and diagnosis of COVID-19 through the use of technologies such as machine learning, deep learning and deep neural networks. However, its careful use is recommended until suitable clinical trials confirming safety are not conducted. © Journal of Medical Artificial Intelligence. All rights reserved.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47132154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artemisia: validation of a deep learning model for automatic breast density categorization","authors":"M. Tajerian, K. Pesce, J. Frangella, Ezequiel D. Quiroga, B. Boietti, M. Chico, M. Swiecicki, S. Benítez, M. Rabellino, D. Luna","doi":"10.21037/JMAI-20-43","DOIUrl":"https://doi.org/10.21037/JMAI-20-43","url":null,"abstract":"Breast density is the terminology used in mammography to describe the proportion between fibroglandular tissue and adipose tissue. It is estimated that 50% of women who undergo mammography examinations have dense breast patterns (1). There is evidence that mammographic density is as strong a predictor of risk for breast cancer in African-American and Asian-American women as for white women (2). High breast density is an independent risk factor for breast cancer (3-6). Furthermore, it may link to higher percentages of interval cancers (7). Dense breast tissue can mask lesions and has a negative impact on the sensitivity of the mammography with rates ranging from 85.7% for the adipose patterns to 61% for the extremely dense patterns. It can also generate an increase in false positives from 11.2% for the non-dense patterns to 23% for dense breasts (8). Breast density can be measured through qualitative or quantitative methods. The American College of Radiology (ACR) has established a structured system for the visual Original Article","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48183820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for the identification of pre- and post-capillary pulmonary hypertension on cine MRI","authors":"Kai Lin, Roberto Sarnari, Ashitha Pathrose, Daniel Z. Gordon, M. Markl, J. Carr","doi":"10.21037/jmai-21-27","DOIUrl":"https://doi.org/10.21037/jmai-21-27","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42119701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The COVID-19 repercussion on Google Trend data analyses","authors":"L. Bertolaccini, A. Cara, Gabriele Maffeis, E. Prisciandaro, A. Mazzella, N. Filippi, L. Spaggiari","doi":"10.21037/jmai-22-2","DOIUrl":"https://doi.org/10.21037/jmai-22-2","url":null,"abstract":"Background: In response to the coronavirus disease 2019 (COVID-19) pandemic, the use of Telemedicine has skyrocketed. This study aimed to assess the relationship between the changes in Google relative search volume (RSV) of telehealth and COVID-19 worldwide and in different Italian regions over 18 months during the pandemic. Methods: Data about the Google searches Telemedicine and COVID-19 were analysed (01/12/2019– 31/08/2021). The number of Google searches was measured in RSV (range, 0–100). Results: Mean worldwide RSV was 52.2±17.6 for the Telemedicine and 57.7±19.5 for COVID-19;mean Italian RSV was 17.5±21.6 for the Telemedicine and 42.0±20.0 for COVID-19. The maximum interest for Telemedicine was observed on 16/02/2020, while the maximum interest for COVID-19 was registered on 25/10/2020. The RSV curve of COVID-19 presented two nadirs during the summer periods. On the other hand, the RSV curve of Telemedicine presented a single peak in May 2020. After the peak, interest in Telemedicine continued declining (mean RSV =18). Conclusions: COVID-19 has expanded the use of all telemedicine modalities. Future research is required to improve the understanding of user needs and the effects of Telemedicine on providers at various levels of experience to guide efforts to encourage telemedicine adoption and usage after the COVID-19 pandemic. © Journal of Medical Artificial Intelligence. All rights reserved.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46436932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors influencing trust in medical artificial intelligence for healthcare professionals: a narrative review","authors":"Victoria Tucci, J. Saary, Thomas E. Doyle","doi":"10.21037/jmai-21-25","DOIUrl":"https://doi.org/10.21037/jmai-21-25","url":null,"abstract":"Objective: We performed a comprehensive review of the literature to better understand the trust dynamics between medical artificial intelligence (AI) and healthcare expert end-users. We explored the factors that influence trust in these technologies and how they compare to established concepts of trust in the engineering discipline. By identifying the qualitatively and quantitatively assessed factors that influence trust in medical AI, we gain insight into understanding how autonomous systems can be optimized during the development phase to improve decision-making support and clinician-machine teaming. This facilitates an enhanced understanding of the qualities that healthcare professional users seek in AI to consider it trustworthy. We also highlight key considerations for promoting on-going improvement of trust in autonomous medical systems to support the adoption of medical technologies into practice. Background: decision support systems introduces challenges and barriers to adoption and implementation into clinical practice. Methods: We searched databases including, Ovid MEDLINE, Ovid EMBASE, Clarivate Web of Science, and Google Scholar, as well as gray literature, for publications from 2000 to July 15, 2021, that reported features of AI-based diagnostic and clinical decision support systems that contribute to enhanced end-user trust. Papers discussing implications and applications of medical AI in clinical practice were also recorded. Results were based on the quantity of papers that discussed each trust concept, either quantitatively or qualitatively, using frequency of concept commentary as a proxy for importance of a respective concept. Conclusions: Explainability, transparency, interpretability, usability, and education are among the key identified factors thought to influence a healthcare professionals’ trust in medical AI and enhance clinician-machine teaming in critical decision-making healthcare environments. We also identified the need to better evaluate and incorporate other critical factors to promote trust by consulting medical professionals when developing AI systems for clinical decision-making and diagnostic support.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41440959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weibo users perception of the COVID-19 pandemic on Chinese social networking service (Weibo): sentiment analysis and fuzzy-c-means model","authors":"Feng Han, Ying-Dan Cao, Ziheng Zhang, Hongjian Zhang, Tomohiko Aoki, Katsuhiko Ogasawara","doi":"10.21037/jmai-21-36","DOIUrl":"https://doi.org/10.21037/jmai-21-36","url":null,"abstract":"Background: Over the last decade, social media analysis tools have been used to monitor public sentiment and communication methods for public health emergencies such as the Ebola and Zika epidemics. Research articles have indicated that many outbreaks and pandemics could have been promptly controlled if experts considered social media data. With the World Health Organization (WHO) pandemic statement and various governments government action on the disease, various sentiments regarding coronavirus disease 2019 (COVID-19) have spread across the world. Therefore, sentiment analyses in studying pandemics, such as COVID-19, are important based on recent events. Methods: The Term Frequency-Inverse Document Frequency (TF-IDF) method was used to extract keywords from the 850,083 content of Weibo from January 24, 2020, to March 31, 2020. Then the Latent Dirichlet Allocation (LDA) was used to perform topic analysis on the keywords. Finally, the fuzzy-c-means method was used to divide the content of Weibo into seven categories of emotions: fear, happiness, disgust, surprise, sadness, anger, and good. And the changes in emotion were tracked over time. Results: The results indicated that people showed “surprise” overall (55.89%);however, with time, the “surprise” decreased. As the knowledge regarding the COVID-19 increased, the “surprise” of the citizens decreased (from 59.95% to 46.58%). Citizens’ feelings of “fear” and “good” increased as the number of deaths associated with COVID-19 increased (“fear”: from 15.42% to 20.95% “good”: 10.31% to 18.89%). As the number of infections was suppressed, the feelings of “fear” and “good” diminished (“fear”: from 20.95% to 15.79% “good”: from 18.89% to 8.46%). Conclusions: The findings of this study indicate that people’s feelings were analyzed regarding the COVID-19 pandemic in three stages over time. In the beginning, people’s emotions were primarily “surprised”;however after the outbreak, people’s “surprise” decreased with increasing knowledge. At the end of the phase, I of the COVID-19 pandemic, people’s “fear” and “good” feelings were diminished as the epidemic was suppressed. People’s interest shifted from China to other countries and their concern about the situation in other countries. © Journal of Medical Artificial Intelligence. All rights reserved.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41971607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MONITOR: a multi-domain machine learning approach to predicting in-hospital mortality","authors":"Christina Guerrier, S. D'Acunto, Guillaume Labilloy, Rhemar Esma, H. Kendall, Daniel A. Norez, J. Fishe","doi":"10.21037/jmai-21-28","DOIUrl":"https://doi.org/10.21037/jmai-21-28","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45580879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of machine learning in predicting medication adherence of patients with cardiovascular diseases: a systematic review of the literature","authors":"M. Zakeri, S. Sansgiry, S. Abughosh","doi":"10.21037/jmai-21-26","DOIUrl":"https://doi.org/10.21037/jmai-21-26","url":null,"abstract":"Background: Cardiovascular disease (CVD) is among the most common chronic diseases in the US. Adequate controlling CVD risk factors with medications can have a significant impact on patients’ long-term outcome. Early identification of patients with low adherence to medications using predictive models through machine learning (ML) may enhance outcomes of patients with CVD. The objective of the current review was to systematically identify and summarize published predictive models that used ML to assess medication adherence (MA) among patients with chronic CVD [heart failure (HF) and coronary artery disease (CAD)] or their main risk factors [hypertension (HTN) and hypercholesterolemia (HCL)]. Methods: A targeted review of English literature was undertaken in PubMed and Google Scholar from January 1, 2000 to August 9, 2021. All studies that used ML to predict MA among patients with chronic CVD or their main risk factors were eligible for this review. Risk of bias was assessed based on the studies’ sample size, data analysis methods, variables in each model, and validation methods used. Characteristics and outcomes of each study were summarized in tables. Results: A total of 12 studies met the eligibility criteria. Selected studies evaluated MA among patients with HF (n=2), CAD (n=3), HTN (n=5), and HCL (n=2). The most used ML algorithms used were random forest (RF) (n=5), support vector machine (SVM) (n=4), and neural network (NN) (n=4). The accuracy of the models ranged from 0.53 to 0.97. Discussion: Most studies used cross-validation to evaluate the internal validation of the model. However, none of the models were externally validated using a different dataset. Using ML to predict MA among patients with CVD and their risk factors is relatively new with only a few studies identified. Compared to conventional statistical methods, fewer restrictions for inclusion of variables in ML models, may enhance the model performance. More research is required to predict MA with higher accuracy and external validity.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42016485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural network-based prediction of consented organs utilization","authors":"H. Ghali, Sarah S. Lam, K. D. Carpini, Chad Ezzell, A. Friedman, S. Yoon, Daehan Won","doi":"10.21037/jmai-21-9","DOIUrl":"https://doi.org/10.21037/jmai-21-9","url":null,"abstract":"Organ scarcity is a pressing matter that requires serious attention. According to the US Department of Health and Human Services, a patient is added to the transplant waiting list every 10 minutes (1). As of December 2019, 73,934 people were waiting for a lifesaving organ (2). Although many people are registered on the organ waiting lists, available organs do not meet the need. In 2019, there was a national daily average of 95 transplants, meaning about Original Article","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43826781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}