scholarly journals Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Nicola Maffulli ◽  
Hugo C. Rodriguez ◽  
Ian W. Stone ◽  
Andrew Nam ◽  
Albert Song ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Clinical Diagnosis ◽  
Systematic Reviews ◽  
Orthopedic Surgery ◽  
Case Reports ◽  
Clinical Care ◽  
Primary Objective ◽  
Operative Outcomes

Abstract Background Artificial intelligence (AI) and machine learning (ML) are interwoven into our everyday lives and have grown enormously in some major fields in medicine including cardiology and radiology. While these specialties have quickly embraced AI and ML, orthopedic surgery has been slower to do so. Fortunately, there has been a recent surge in new research emphasizing the need for a systematic review. The primary objective of this systematic review will be to provide an update on the advances of AI and ML in the field of orthopedic surgery. The secondary objectives will be to evaluate the applications of AI and ML in providing a clinical diagnosis and predicting post-operative outcomes and complications in orthopedic surgery. Methods A systematic search will be conducted in PubMed, ScienceDirect, and Google Scholar databases for articles written in English, Italian, French, Spanish, and Portuguese language articles published up to September 2020. References will be screened and assessed for eligibility by at least two independent reviewers as per PRISMA guidelines. Studies must apply to orthopedic interventions and acute and chronic orthopedic musculoskeletal injuries to be considered eligible. Studies will be excluded if they are animal studies and do not relate to orthopedic interventions or if no clinical data were produced. Gold standard processes and practices to obtain a clinical diagnosis and predict post-operative outcomes shall be compared with and without the use of ML algorithms. Any case reports and other primary studies assessing the prediction rate of post-operative outcomes or the ability to identify a diagnosis in orthopedic surgery will be included. Systematic reviews or literature reviews will be examined to identify further studies for inclusion, and the results of meta-analyses will not be included in the analysis. Discussion Our findings will evaluate the advances of AI and ML in the field of orthopedic surgery. We expect to find a large quantity of uncontrolled studies and a smaller subset of articles describing actual applications and outcomes for clinical care. Cohort studies and large randomized control trial will likely be needed. Trial registration The protocol will be registered on PROSPERO international prospective register of systematic reviews prior to commencement.

scholarly journals Artificial Intelligence and Machine Learning in Orthopedic Surgery: A Systematic Review Protocol

2020 ◽  
Author(s):  
Nicola Maffulli ◽  
Hugo C. Rodriguez ◽  
Ian W. Stone ◽  
Andrew Nam ◽  
Albert Song ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Clinical Diagnosis ◽  
Systematic Reviews ◽  
Orthopedic Surgery ◽  
Case Reports ◽  
Clinical Care ◽  
Primary Objective ◽  
Operative Outcomes

Abstract Background: Artificial Intelligence (AI) and Machine Learning (ML) is interwoven into our everyday lives and has grown enormously in some major fields in medicine including cardiology and radiology. While these specialties have quickly embraced AI and ML, orthopedic surgery has been slower to do so. Fortunately, there has been a recent surge in new research emphasizing the need for a systematic review. The primary objective of this systematic review will be to provide an update on the advances of AI and ML in the field of orthopedic surgery. The secondary objectives will be to evaluate the applications of AI and ML in providing a clinical diagnosis and predicting post-operative outcomes and complications in orthopedic surgery.Methods: A systematic search will be conducted in PubMed, ScienceDirect, and Google Scholar databases for articles written in English, Italian, French, Spanish and Portuguese language articles published up to September 2020. References will be screened and assessed for eligibility by at least two independent reviewers as per PRISMA guidelines. Studies must apply to orthopedic interventions, acute and chronic orthopedic musculoskeletal injuries to be considered eligible. Studies will be excluded if they are animal studies, do not relate to orthopedic interventions, or if no clinical data were produced. Gold standard processes and practices to obtain a clinical diagnosis and predict post-operative outcomes shall be compared with and without the use of ML algorithms. Any case reports and other primary studies assessing the prediction rate of post-operative outcomes or the ability to identify a diagnosis in orthopedic surgery will be included. Systematic reviews or literature reviews will be examined to identify further studies for inclusion, and results of meta-analyses will not be included in the analysis.Discussion: Our findings will evaluate the advances of AI and ML in the field of orthopedic surgery. We expect to find a large quantity of uncontrolled studies, and a smaller subset of articles describing actual applications and outcomes for clinical care. Cohort studies and large randomized control trial will likely be needed.Trial registration: The Protocol will be registered on PROSPERO international prospective register of systematic reviews prior to commencement.

scholarly journals Artificial Intelligence and Machine Learning in Orthopedic Surgery: A Systematic Review Protocol

2020 ◽  
Author(s):  
Nicola Maffulli ◽  
Hugo C. Rodriguez ◽  
Ian W. Stone ◽  
Andrew Nam ◽  
Albert Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Clinical Diagnosis ◽  
Systematic Reviews ◽  
Orthopedic Surgery ◽  
Animal Studies ◽  
Case Reports ◽  
Clinical Care ◽  
Primary Objective ◽  
Operative Outcomes

Abstract Background:Artificial Intelligence (AI) and Machine Learning (ML)is interwoven into our everyday lives and has grown enormously in some major fields in medicine including cardiology and radiology. While these specialties have quickly embraced AI and ML, orthopedic surgery has been slower to do so. Fortunately, there has been a recent surge in new research emphasizing the need for a systematic review. The primary objective of this review is to provide an update on the advances of AI and ML in the field of orthopedic surgery. The secondary objectives of this review are to evaluate the applications of AI and ML in providing a clinical diagnosis and predicting post-operative outcomes and complications in orthopedic surgery.Methods:A systematic search will be conducted in PubMed, Science Direct, and Google Scholar databases for articles written in English, Italian, French, Spanish and Portuguese language articles published up to July 2020. References will be screened and assessed for eligibility by at least two independent reviewers as per PRISMA guidelines. Studies must apply to orthopedic interventions, acute and chronic orthopedic musculoskeletal injuries to be considered eligible. Studies will be excluded if they are animal studies, do not relate to orthopedic interventions, or if no clinical data were used. Gold standard processes and practices to obtain a clinical diagnosis and predict post-operative outcomes shall be compared with and without the use of ML algorithms. Any case reports and other primary studies assessing the prediction rate of post-operative outcomes or the ability to identify a diagnosis in orthopedic surgery will be included. Systematic reviews or literature reviews will be examined to identify further studies for inclusion, and results of meta-analyses will not be included in the analysis.Discussion:Our findings will evaluate the advances of AI and ML in the field of orthopedic surgery. We expect to find a large quantity of uncontrolled studies, and a smaller subset of papers describing actual applications and outcomes for clinical care. Cohort studies and large randomized control trial will likely be needed.Trial registration: The Protocol will be registered on PROSPERO international prospective register of systematic reviews prior to commencement.

scholarly journals Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

2021 ◽  
Vol 18 (9) ◽  
pp. 4499
Author(s):  
Anil Babu Payedimarri ◽  
Diego Concina ◽  
Luigi Portinale ◽  
Massimo Canonico ◽  
Deborah Seys ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Public Transportation ◽  
Prediction Models ◽  
Positive Impact ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Commercial Activities

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

scholarly journals Systematic review protocol to assess artificial intelligence diagnostic accuracy performance in detecting acute ischaemic stroke and large-vessel occlusions on CT and MR medical imaging

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e043665
Author(s):  
Srinivasa Rao Kundeti ◽  
Manikanda Krishnan Vaidyanathan ◽  
Bharath Shivashankar ◽  
Sankar Prasad Gorthi
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Diagnostic Accuracy ◽  
Ischaemic Stroke ◽  
Study Protocol ◽  
Systematic Reviews ◽  
Acute Ischaemic Stroke ◽  
Meta Analysis ◽  
Large Vessel ◽  
Sufficient Data

IntroductionThe use of artificial intelligence (AI) to support the diagnosis of acute ischaemic stroke (AIS) could improve patient outcomes and facilitate accurate tissue and vessel assessment. However, the evidence in published AI studies is inadequate and difficult to interpret which reduces the accountability of the diagnostic results in clinical settings. This study protocol describes a rigorous systematic review of the accuracy of AI in the diagnosis of AIS and detection of large-vessel occlusions (LVOs).Methods and analysisWe will perform a systematic review and meta-analysis of the performance of AI models for diagnosing AIS and detecting LVOs. We will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols guidelines. Literature searches will be conducted in eight databases. For data screening and extraction, two reviewers will use a modified Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. We will assess the included studies using the Quality Assessment of Diagnostic Accuracy Studies guidelines. We will conduct a meta-analysis if sufficient data are available. We will use hierarchical summary receiver operating characteristic curves to estimate the summary operating points, including the pooled sensitivity and specificity, with 95% CIs, if pooling is appropriate. Furthermore, if sufficient data are available, we will use Grading of Recommendations, Assessment, Development and Evaluations profiler software to summarise the main findings of the systematic review, as a summary of results.Ethics and disseminationThere are no ethical considerations associated with this study protocol, as the systematic review focuses on the examination of secondary data. The systematic review results will be used to report on the accuracy, completeness and standard procedures of the included studies. We will disseminate our findings by publishing our analysis in a peer-reviewed journal and, if required, we will communicate with the stakeholders of the studies and bibliographic databases.PROSPERO registration numberCRD42020179652.

scholarly journals AI in global healthcare: Need for robust governance frameworks

2020 ◽  
Author(s):  
Sandeep Reddy ◽  
Sonia Allan ◽  
Simon Coghlan ◽  
Paul Cooper
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Developing Countries ◽  
Clinical Care ◽  
Ethical Guidelines ◽  
Popular Discourse ◽  
The World ◽  
Healthcare Need

The re-emergence of artificial intelligence (AI) in popular discourse and its application in medicine, especially via machine learning (ML) algorithms, has excited interest from policymakers and clinicians alike. The use of AI in clinical care in both developed and developing countries is no longer a question of ‘if?’ but ‘when?’. This creates a pressing need not only for sound ethical guidelines but also for robust governance frameworks to regulate AI in medicine around the world. In this article, we discuss what components need to be considered in developing these governance frameworks and who should lead this worldwide effort?

Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review

2021 ◽  
Vol 89 ◽  
pp. 177-198
Author(s):  
Quinlan D. Buchlak ◽  
Nazanin Esmaili ◽  
Jean-Christophe Leveque ◽  
Christine Bennett ◽  
Farrokh Farrokhi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Machine Learning Applications

Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant, Evidence from the Biomedical Literature: A Systematic Review (Preprint)

2021 ◽  
Author(s):  
Wael Abdelkader ◽  
Tamara Navarro ◽  
Rick Parrish ◽  
Chris Cotoi ◽  
Federico Germini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Strong Evidence ◽  
Clinical Care ◽  
Biomedical Literature ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
High Quality ◽  
Applied Machine Learning

BACKGROUND The rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy. OBJECTIVE To summarize the nature and comparative performance of machine learning approaches that have been applied to retrieve high-quality evidence for clinical consideration from the biomedical literature. METHODS We conducted a systematic review of studies that applied machine learning techniques to identify high-quality clinical articles in the biomedical literature. Multiple databases were searched to July 2020. Extracted data focused on the applied machine learning model, steps in the development of the models, and model performance. RESULTS From 3918 retrieved studies, 10 met our inclusion criteria. All followed a supervised machine learning approach and applied, from a limited range of options, a high-quality standard for the training of their model. The results show that machine learning can achieve a sensitivity of 95% while maintaining a high precision of 86%. CONCLUSIONS Applying machine learning to distinguish studies with strong evidence for clinical care has the potential to decrease the workload of manually identifying these. The evidence base is active and evolving. Reported methods were variable across the studies but focused on supervised machine learning approaches. Performance may improve by applying more sophisticated approaches such as active learning, auto-machine learning, and unsupervised machine learning approaches.

scholarly journals Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review

2020 ◽  
Vol 130 ◽  
pp. 109899 ◽  
Author(s):  
Ioannis Antonopoulos ◽  
Valentin Robu ◽  
Benoit Couraud ◽  
Desen Kirli ◽  
Sonam Norbu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Energy Demand ◽  
Learning Approaches ◽  
Demand Side ◽  
Demand Side Response

scholarly journals Association between financial links to indoor tanning industry and conclusions of published studies on indoor tanning: systematic review

BMJ ◽  
2020 ◽  
pp. m7 ◽  
Author(s):  
Lola Adekunle ◽  
Rebecca Chen ◽  
Lily Morrison ◽  
Meghan Halley ◽  
Victor Eng ◽  
...  
Keyword(s):  
Systematic Review ◽  
Systematic Reviews ◽  
Science Studies ◽  
Case Reports ◽  
Indoor Tanning ◽  
Original Research ◽  
Industry Funding ◽  
Study Selection ◽  
English Articles ◽  
Tanning Industry

Abstract Objective To assess whether an association exists between financial links to the indoor tanning industry and conclusions of indoor tanning literature. Design Systematic review. Data sources PubMed, Embase, and Web of Science, up to 15 February 2019. Study selection criteria Articles discussing indoor tanning and health were eligible for inclusion, with no article type restrictions (original research, systematic reviews, review articles, case reports, editorials, commentaries, and letters were all eligible). Basic science studies, articles describing only indoor tanning prevalence, non-English articles, and articles without full text available were excluded. Results 691 articles were included in analysis, including empiric articles (eg, original articles or systematic reviews) (357/691; 51.7%) and non-empiric articles letters (eg, commentaries, letters, or editorials) (334/691; 48.3%). Overall, 7.2% (50/691) of articles had financial links to the indoor tanning industry; 10.7% (74/691) articles favored indoor tanning, 3.9% (27/691) were neutral, and 85.4% (590/691) were critical of indoor tanning. Among the articles without industry funding, 4.4% (27/620) favored indoor tanning, 3.5% (22/620) were neutral, and 92.1% (571/620) were critical of indoor tanning. Among the articles with financial links to the indoor tanning industry, 78% (39/50) favored indoor tanning, 10% (5/50) were neutral, and 12% (6/50) were critical of indoor tanning. Support from the indoor tanning industry was significantly associated with favoring indoor tanning (risk ratio 14.3, 95% confidence interval 10.0 to 20.4). Conclusions Although most articles in the indoor tanning literature are independent of industry funding, articles with financial links to the indoor tanning industry are more likely to favor indoor tanning. Public health practitioners and researchers need to be aware of and account for industry funding when interpreting the evidence related to indoor tanning. Systematic review registration PROSPERO CRD42019123617.

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