Artificial Intelligence in Predicting Cardiac Arrest: A Scoping Review (Preprint)

2021 ◽  
Author(s):  
Asma Alamgir ◽  
Osama Mousa 2nd ◽  
Zubair Shah 3rd
Keyword(s):  
Artificial Intelligence ◽  
At Risk ◽  
Cardiac Arrest ◽  
Scoping Review ◽  
Pediatric Population ◽  
Data Extraction ◽  
Warning System ◽  
Reference List ◽  
Evaluation Tool ◽  
Patients At Risk

BACKGROUND Cardiac arrest is a life-threatening cessation of heart activity. Early prediction of cardiac arrest is important as it provides an opportunity to take the necessary measures to prevent or intervene during the onset. Artificial intelligence technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. OBJECTIVE This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. METHODS Scoping review was conducted in line with guidelines of PRISMA Extension for Scoping Review (PRISMA-ScR). Scopus, Science Direct, Embase, IEEE, and Google Scholar were searched to identify relevant studies. Backward reference list checking of included studies was also conducted. The study selection and data extraction were conducted independently by two reviewers. Data extracted from the included studies were synthesized narratively. RESULTS Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. We were able to classify the approach taken by the studies in three different categories - 26 studies predicted cardiac arrest by analyzing specific parameters or variables of the patients while 16 studies developed an AI-based warning system. The rest of the 5 studies focused on distinguishing high-risk cardiac arrest patients from patients, not at risk. 2 studies focused on the pediatric population, and the rest focused on adults (n=45). The majority of the studies used datasets with a size of less than 10,000 (n=32). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (n=38) and the most used algorithm belonged to the neural network (n=23). K-Fold cross-validation was the most used algorithm evaluation tool reported in the studies (n=24). CONCLUSIONS : AI is extensively being used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in changing cardiac medicine for the better. There is a need for more reviews to learn the obstacles of implementing AI technologies in the clinical setting. Moreover, research focusing on how to best provide clinicians support to understand, adapt and implement the technology in their practice is also required.

Artificial Intelligence for Skin Cancer Detection: A Scoping Review (Preprint)

2020 ◽  
Author(s):  
Abdulrahman Takiddin ◽  
Jens Schneider ◽  
Yin Yang ◽  
Alaa Abd-Alrazaq ◽  
Mowafa Househ
Keyword(s):  
Artificial Intelligence ◽  
Skin Cancer ◽  
Scoping Review ◽  
Performance Metrics ◽  
Data Extraction ◽  
Characteristic Curve ◽  
Evaluation Metrics ◽  
Cancer Type ◽  
Computer Algorithms ◽  
Scoping Reviews

BACKGROUND Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, Artificial Intelligence (AI) tools are being used, including shallow and deep machine learning-based techniques that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. OBJECTIVE The aim of this study is to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examines the reliability of the selected papers by studying the correlation between the dataset size and number of diagnostic classes with the performance metrics used to evaluate the models. METHODS We conducted a systematic search for articles using IEEE Xplore, ACM DL, and Ovid MEDLINE databases following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. The study included in this scoping review had to fulfill several selection criteria; to be specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were conducted by two reviewers independently. Extracted data were synthesized narratively, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. RESULTS We retrieved 906 papers from the 3 databases, but 53 studies were eligible for this review. While shallow techniques were used in 14 studies, deep techniques were utilized in 39 studies. The studies used accuracy (n=43/53), the area under receiver operating characteristic curve (n=5/53), sensitivity (n=3/53), and F1-score (n=2/53) to assess the proposed models. Studies that use smaller datasets and fewer diagnostic classes tend to have higher reported accuracy scores. CONCLUSIONS The adaptation of AI in the medical field facilitates the diagnosis process of skin cancer. However, the reliability of most AI tools is questionable since small datasets or low numbers of diagnostic classes are used. In addition, a direct comparison between methods is hindered by a varied use of different evaluation metrics and image types.

Artificial Intelligence and Ethics in Dentistry: A Scoping Review

2021 ◽  
pp. 002203452110138
Author(s):  
C.M. Mörch ◽  
S. Atsu ◽  
W. Cai ◽  
X. Li ◽  
S.A. Madathil ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Scoping Review ◽  
Ethical Issues ◽  
Data Extraction ◽  
Cochrane Library ◽  
Data Sets ◽  
Ethical Challenges ◽  
Internal Validation ◽  
Neural Learning ◽  
Learning Techniques

Dentistry increasingly integrates artificial intelligence (AI) to help improve the current state of clinical dental practice. However, this revolutionary technological field raises various complex ethical challenges. The objective of this systematic scoping review is to document the current uses of AI in dentistry and the ethical concerns or challenges they imply. Three health care databases (MEDLINE [PubMed], SciVerse Scopus, and Cochrane Library) and 2 computer science databases (ArXiv, IEEE Xplore) were searched. After identifying 1,553 records, the documents were filtered, and a full-text screening was performed. In total, 178 studies were retained and analyzed by 8 researchers specialized in dentistry, AI, and ethics. The team used Covidence for data extraction and Dedoose for the identification of ethics-related information. PRISMA guidelines were followed. Among the included studies, 130 (73.0%) studies were published after 2016, and 93 (52.2%) were published in journals specialized in computer sciences. The technologies used were neural learning techniques for 75 (42.1%), traditional learning techniques for 76 (42.7%), or a combination of several technologies for 20 (11.2%). Overall, 7 countries contributed to 109 (61.2%) studies. A total of 53 different applications of AI in dentistry were identified, involving most dental specialties. The use of initial data sets for internal validation was reported in 152 (85.4%) studies. Forty-five ethical issues (related to the use AI in dentistry) were reported in 22 (12.4%) studies around 6 principles: prudence (10 times), equity (8), privacy (8), responsibility (6), democratic participation (4), and solidarity (4). The ratio of studies mentioning AI-related ethical issues has remained similar in the past years, showing that there is no increasing interest in the field of dentistry on this topic. This study confirms the growing presence of AI in dentistry and highlights a current lack of information on the ethical challenges surrounding its use. In addition, the scarcity of studies sharing their code could prevent future replications. The authors formulate recommendations to contribute to a more responsible use of AI technologies in dentistry.

Artificial Intelligence in Kidney Transplantation: A Scoping Review (Preprint)

2021 ◽  
Author(s):  
Mowafa Househ ◽  
Asma Alamgir ◽  
Yasmin Abdelaal ◽  
Hagar Hussein
Keyword(s):  
Artificial Intelligence ◽  
Kidney Transplantation ◽  
Kidney Transplant ◽  
Data Extraction ◽  
Care Plan ◽  
Reference List ◽  
Potential Candidate ◽  
Transplant Patients ◽  
Study Selection ◽  
Standardized Practice

BACKGROUND Artificial Intelligence technologies and big data have been increasingly used to enhance kidney transplant experts’ ability to make critical decisions and manage the care plan for their patients. OBJECTIVE To explore the use of AI technologies in the field of kidney transplantation as reported in the literature. METHODS Embase, CINAHL, PubMed and Google Scholar were used in the search. Backward reference list checking of included studies was also conducted. Study selection and data extraction was done independently by three reviewers. Data extracted was synthesized in a narrative approach. RESULTS Of 505 citations retrieved from the databases, 33 unique studies are included in this review. Artificial intelligence (AI) technologies in the included studies were used to help with diagnosis (n= 16), used as a prediction tool (n=15) and, also for supporting appropriate prescription for kidney transplant patients (n = 2). The population who benefited from the technique included patients who underwent kidney transplantation procedure (n = 24) and those who are potential candidate (n=6). The most prominent AI branch used in kidney transplantation care was machine learning with Random Forest (n=11) being the most used AI model, followed by Linear Regression (n=6). CONCLUSIONS Conclusion: AI is extensively being used in the field of kidney transplant. However, there is a gap in research on the limitation and obstacles associated with implementing AI technologies in kidney transplant. There is a need for more research to identify educational needs and standardized practice for clinicians who wish to apply AI technologies in critical transplantation-related decisions.

scholarly journals Perfusion parameters and target values during extracorporeal cardiopulmonary resuscitation: a scoping review protocol

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e030562
Author(s):  
Lars Saemann ◽  
Christine Schmucker ◽  
Lisa Rösner ◽  
Friedhelm Beyersdorf ◽  
Christoph Benk
Keyword(s):  
Cardiac Arrest ◽  
Cardiopulmonary Resuscitation ◽  
Scoping Review ◽  
Citation Index ◽  
Data Extraction ◽  
Survival Rates ◽  
Social Science Citation Index ◽  
Extracorporeal Cardiopulmonary Resuscitation ◽  
Target Values ◽  
Hospital Cardiac Arrest

IntroductionExtracorporeal cardiopulmonary resuscitation (eCPR) is increasingly applied in out-of-hospital cardiac arrest (OHCA) and in-hospital cardiac arrest (IHCA) patients. Treatment results are promising, but the efficacy and safety of the procedure are still unclear. Currently, there are no recommended target perfusion parameters during eCPR, the lack of which could result in inadequate (re)perfusion. We aim to perform a scoping review to explore the current literature addressing target perfusion parameters, target values, corresponding survival rates and neurologic outcomes in OHCA and IHCA patients treated with eCPR.Methods and analysisTo identify relevant research, we will conduct searches in the electronic databases MEDLINE, EMBASE, Social Science Citation Index, Social Science Citation Index Expanded and the Cochrane library. We will also check references of relevant articles and perform a cited reference research (forward citation tracking).Two independent reviewers will screen titles and abstracts, check full texts for eligibility and perform data extraction. We will resolve dissent by consensus, moderated by a third reviewer. We will include observational and controlled studies addressing target perfusion parameters and outcomes such as survival rates and neurologic findings in OHCA and IHCA patients treated with eCPR. Data extraction tables will be set up, including study and patients’ characteristics, aim of study, details on eCPR including target perfusion parameters and reported outcomes. We will summarise the data using tables and figures (ie, bubble plot) to present the research landscape and to describe potential clusters and/or gaps.Ethics and disseminationAn ethical approval is not needed. We intend to publish the scoping review in a peer-reviewed journal and present results on a scientific meeting.

scholarly journals P071: Artificial intelligence in emergency medicine: A scoping review

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S90-S90
Author(s):  
A. Kirubarajan ◽  
A. Taher ◽  
S. Khan ◽  
S. Masood
Keyword(s):  
Artificial Intelligence ◽  
Emergency Medicine ◽  
Language Processing ◽  
Scoping Review ◽  
Clinical Decision Making ◽  
Data Extraction ◽  
A Priori ◽  
Clinical Decision ◽  
Hospital Environment ◽  
Clinical Implementation

Introduction: The study of artificial intelligence (AI) in medicine has become increasingly popular over the last decade. The emergency department (ED) is uniquely situated to benefit from AI due to its power of diagnostic prediction, and its ability to continuously improve with time. However, there is a lack of understanding of the breadth and scope of AI applications in emergency medicine, and evidence supporting its use. Methods: Our scoping review was completed according to PRISMA-ScR guidelines and was published a priori on Open Science Forum. We systematically searched databases (Medline-OVID, EMBASE, CINAHL, and IEEE) for AI interventions relevant to the ED. Study selection and data extraction was performed independently by two investigators. We categorized studies based on type of AI model used, location of intervention, clinical focus, intervention sub-type, and type of comparator. Results: Of the 1483 original database citations, a total of 181 studies were included in the scoping review. Inter-rater reliability for study screening for titles and abstracts was 89.1%, and for full-text review was 77.8%. Overall, we found that 44 (24.3%) studies utilized supervised learning, 63 (34.8%) studies evaluated unsupervised learning, and 13 (7.2%) studies utilized natural language processing. 17 (9.4%) studies were conducted in the pre-hospital environment, with the remainder occurring either in the ED or the trauma bay. The majority of interventions centered around prediction (n = 73, 40.3%). 48 studies (25.5%) analyzed AI interventions for diagnosis. 23 (12.7%) interventions focused on diagnostic imaging. 89 (49.2%) studies did not have a comparator to their AI intervention. 63 (34.8%) studies used statistical models as a comparator, 19 (10.5%) of which were clinical decision making tools. 15 (8.3%) studies used humans as comparators, with 12 of the 15 (80%) studies showing superiority in favour of the AI intervention when compared to a human. Conclusion: AI-related research is rapidly increasing in emergency medicine. AI interventions are heterogeneous in both purpose and design, but primarily focus on predictive modeling. Most studies do not involve a human comparator and lack information on patient-oriented outcomes. While some studies show promising results for AI-based interventions, there remains uncertainty regarding their superiority over standard practice, and further research is needed prior to clinical implementation.

Identifying patients at risk of futile resuscitation: palliative care indicators in out-of-hospital cardiac arrest

2019 ◽  
pp. bmjspcare-2019-001828
Author(s):  
Mia Cokljat ◽  
Adam Lloyd ◽  
Scott Clarke ◽  
Anna Crawford ◽  
Gareth Clegg
Keyword(s):  
At Risk ◽  
Palliative Care ◽  
Cardiac Arrest ◽  
High Risk ◽  
Low Risk ◽  
Intermediate Risk ◽  
Patients At Risk ◽  
Risk Patients ◽  
Record Review ◽  
Hospital Cardiac Arrest

ObjectivesPatients with indicators for palliative care, such as those with advanced life-limiting conditions, are at risk of futile cardiopulmonary resuscitation (CPR) if they suffer out-of-hospital cardiac arrest (OHCA). Patients at risk of futile CPR could benefit from anticipatory care planning (ACP); however, the proportion of OHCA patients with indicators for palliative care is unknown. This study quantifies the extent of palliative care indicators and risk of CPR futility in OHCA patients.MethodsA retrospective medical record review was performed on all OHCA patients presenting to an emergency department (ED) in Edinburgh, Scotland in 2015. The risk of CPR futility was stratified using the Supportive and Palliative Care Indicators Tool. Patients with 0–2 indicators had a ‘low risk’ of futile CPR; 3–4 indicators had an ‘intermediate risk’; 5+ indicators had a ‘high risk’.ResultsOf the 283 OHCA patients, 12.4% (35) had a high risk of futile CPR, while 16.3% (46) had an intermediate risk and 71.4% (202) had a low risk. 84.0% (68) of intermediate-to-high risk patients were pronounced dead in the ED or ED step-down ward; only 2.5% (2) of these patients survived to discharge.ConclusionsUp to 30% of OHCA patients are being subjected to advanced resuscitation despite having at least three indicators for palliative care. More than 80% of patients with an intermediate-to-high risk of CPR futility are dying soon after conveyance to hospital, suggesting that ACP can benefit some OHCA patients. This study recommends optimising emergency treatment planning to help reduce inappropriate CPR attempts.

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