Journal of medical artificial intelligence最新文献

{"title":"A narrative review on radiotherapy practice in the era of artificial intelligence: how relevant is the medical physicist?","authors":"Eric Naab Manson, A. N. Mumuni, E. Fiagbedzi, I. Shirazu, H. Sulemana","doi":"10.21037/jmai-22-27","DOIUrl":"https://doi.org/10.21037/jmai-22-27","url":null,"abstract":"Background and Objective: Artificial intelligence (AI) uses computers and machines to simulate how the human mind makes decisions and solves problems. In radiotherapy practice, AI technologies continue to be promising in image registration, synthetic computed tomography (CT), image segmentation, motion management, treatment planning, and delivery procedures, patient follow-up and quality assurance (QA). This, therefore, provides a new window of opportunity to improve upon the accuracy and output times of the manual implementation of these procedures. The goal of this review was to explore how machine learning AI technologies in radiotherapy could affect the clinical practice of medical physicists. Methods: A narrative literature review was conducted from PubMed, Science Direct and Scopus using the search terms: in the English language within 6 months. Key Content and Findings: The roles of AI and the clinical medical physicist are complementary in radiotherapy practice. Both the medical physicists and AI technology are highly needed to support the full implementation and optimization of radiotherapy procedures. Conclusions: To achieve successful implementation of AI in radiotherapy and optimize radiotherapy procedures, clinical medical physicist should receive some compulsory training in AI technologies during their education and training. They should ultimately be involved in the incorporation of machine learning technologies in radiotherapy equipment. patient-specific dosimetric of patient treatment Dosimetric measurements in phantoms one of following detectors: portal imaging The third type of looks for delivery errors in log files generated during during delivery time-series linear quality","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47113877","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":"Machine learning in atrial fibrillation—racial bias and a call for caution","authors":"Hiten Doshi, J. Chudow, K. Ferrick, A. Krumerman","doi":"10.21037/jmai-21-12","DOIUrl":"https://doi.org/10.21037/jmai-21-12","url":null,"abstract":"© Journal of Medical Artificial Intelligence. All rights reserved. J Med Artif Intell 2021;4:6 | https://dx.doi.org/10.21037/jmai-21-12 Early diagnosis of atrial fibrillation (AF), a common arrhythmia that can cause adverse events such as stroke, is a major clinical challenge. Due to its often asymptomatic and paroxysmal nature, AF is easily missed on single electrocardiograms (ECGs), making outpatient screening challenging. As a result, patients may not receive a timely diagnosis, with up to 5% of all AF cases being diagnosed at the time of stroke (1). Various machine learning (ML) models, primarily involving supervised ML methods, have been developed with the hopes of bringing an effective population screening tool to the forefront. While these models show strong performance in their respective studies, data regarding their effectiveness across racial groups is lacking. Therefore, using ML for AF screening requires two important considerations: (I) any biases in the training set data will be perpetuated in the predictions that the models offer; (II) AF has a known racial paradox, where traditional risk factors that were derived from a largely Caucasian population have a weaker correlation with AF incidence in Black patients. Below, we elaborate on these points and argue that while ML presents a unique opportunity to increase the detection of AF, it also deserves special caution to avoid reinforcing existing healthcare disparities. ML AF screening tools are commonly developed using ECG data about p-waves, R-R intervals, heart rate, and other parameters. While this has shown the ability to produce strong predictive models, the actual data sources deserve scrutiny (2). A recently published systematic review identified that while more than 100 publications exist using ECG data to develop ML models, more than half of them used the same four open-access ECG databases (3). In theory, this is not necessarily problematic, and it is understandable that so many studies reuse well known and freely available datasets. Ideally, however, the datasets would report a sufficient level of patient diversity to well represent the entire US population. Instead, many of the most commonly used ECG datasets only report limited demographic data, including the patient’s age, gender, and/or baseline clinical characteristics, without reporting racial or ethnic background. Considering the known racial differences that exist in several baseline ECG parameters, including left ventricular hypertrophy, right axis deviation, bundle branch blocks, and others, transparency about racial demographic information in these datasets is critical (4). Table 1 summarizes the most commonly used ECG databases, as well as the readily available demographic information provided by each. The reuse of these datasets carries particular concern in the diagnosis of AF, a disease with a known “racial paradox”. This paradox refers to the fact that while Black patients have a higher burden of AF risk f","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42700356","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":"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}