Artificial Intelligence in Neuroradiology: Current Status and Future Directions.
AJNR. American journal of neuroradiology
Fueled by new techniques, computational tools, and broader availability of imaging data, artificial intelligence has the potential to transform the practice of neuroradiology. The recent exponential increase in publications related to artificial intelligence and the central focus on artificial intelligence at recent professional and scientific radiology meetings underscores the importance. There is growing momentum behind leveraging artificial intelligence techniques to improve workflow and diagnosis and treatment and to enhance the value of quantitative imaging techniques. This article explores the reasons why neuroradiologists should care about the investments in new artificial intelligence applications, highlights current activities and the roles neuroradiologists are playing, and renders a few predictions regarding the near future of artificial intelligence in neuroradiology.
10.3174/ajnr.A6681
Machine learning-based discrimination of panic disorder from other anxiety disorders.
Na Kyoung-Sae,Cho Seo-Eun,Cho Seong-Jin
Journal of affective disorders
BACKGROUNDS:Panic disorder is a highly prevalent psychiatric disorder that substantially impairs quality of life and psychosocial function. Panic disorder arises from neurobiological substrates and developmental factors that distinguish it from other anxiety disorders. Differential diagnosis between panic disorder and other anxiety disorders has only been conducted in terms of a phenomenological spectrum. METHODS:Through a machine learning-based approach with heart rate variability (HRV) as input, we aimed to build algorithms that can differentiate panic disorder from other anxiety disorders. Five algorithms were used: random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), artificial neural network (ANN), and regularized logistic regression (LR). 10-fold cross-validation with five repeats was used to build the final models. RESULTS:A total of 60 patients with panic disorder and 61 patients with other anxiety disorders (aged between 20 and 65 years) were recruited. The L1-regularized LR showed the best accuracy (0.784), followed by ANN (0.730), SVM (0.730), GBM (0.676), and finally RF (0.649). LR also had good performance in other measures, such as F-score (0.790), specificity (0.737), sensitivity (0.833), and Matthews correlation coefficient (0.572). LIMITATIONS:Cross-sectional design and limited sample size is limitations. CONCLUSION:This study demonstrated that HRV can be used to differentiate panic disorder from other anxiety disorders. Future studies with larger sample sizes and longitudinal design are required to replicate the diagnostic utility of HRV in a machine learning approach.
10.1016/j.jad.2020.09.027
Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension.
Nature medicine
The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.
10.1038/s41591-020-1034-x
Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension.
Nature medicine
The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.
10.1038/s41591-020-1037-7
Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital.
Romero-Brufau Santiago,Wyatt Kirk D,Boyum Patricia,Mickelson Mindy,Moore Matthew,Cognetta-Rieke Cheristi
Applied clinical informatics
BACKGROUND:Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictive model coupled with effective, patient-centered interventions. OBJECTIVE:The aim of this study was to reduce unplanned hospital readmissions through the use of artificial intelligence-based clinical decision support. METHODS:A commercially vended artificial intelligence tool was implemented at a regional hospital in La Crosse, Wisconsin between November 2018 and April 2019. The tool assessed all patients admitted to general care units for risk of readmission and generated recommendations for interventions intended to decrease readmission risk. Similar hospitals were used as controls. Change in readmission rate was assessed by comparing the 6-month intervention period to the same months of the previous calendar year in exposure and control hospitals. RESULTS:Among 2,460 hospitalizations assessed using the tool, 611 were designated by the tool as high risk. Sensitivity and specificity for risk assignment were 65% and 89%, respectively. Over 6 months following implementation, readmission rates decreased from 11.4% during the comparison period to 8.1% ( < 0.001). After accounting for the 0.5% decrease in readmission rates (from 9.3 to 8.8%) at control hospitals, the relative reduction in readmission rate was 25% ( < 0.001). Among patients designated as high risk, the number needed to treat to avoid one readmission was 11. CONCLUSION:We observed a decrease in hospital readmission after implementing artificial intelligence-based clinical decision support. Our experience suggests that use of artificial intelligence to identify patients at the highest risk for readmission can reduce quality gaps when coupled with patient-centered interventions.
10.1055/s-0040-1715827
Artificial intelligence in celiac disease.
Sana Muhammad Khawar,Hussain Zeshan M,Shah Pir Ahmad,Maqsood Muhammad Haisum
Computers in biology and medicine
Celiac disease (CD) has been on the rise in the world and a large part of it remains undiagnosed. Novel methods are required to address the gaps in prompt detection and management. Artificial intelligence (AI) has seen an exponential surge in the last decade worldwide. With the advent of big data and powerful computational ability, we now have self-driving cars and smart devices in our daily lives. Huge databases in the form of electronic medical records and images have rendered healthcare a lucrative sector where AI can prove revolutionary. It is being used extensively to overcome the barriers in clinical workflows. From the perspective of a disease, it can be deployed in multiple steps i.e. screening tools, diagnosis, developing novel therapeutic agents, proposing management plans, and defining prognostic indicators, etc. We review the areas where it may augment physicians in the delivery of better healthcare by summarizing current literature on the use of AI in healthcare using CD as a model. We further outline major barriers to its large-scale implementations and prospects from the healthcare point of view.
10.1016/j.compbiomed.2020.103996
Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians.
Asan Onur,Bayrak Alparslan Emrah,Choudhury Avishek
Journal of medical Internet research
Artificial intelligence (AI) can transform health care practices with its increasing ability to translate the uncertainty and complexity in data into actionable-though imperfect-clinical decisions or suggestions. In the evolving relationship between humans and AI, trust is the one mechanism that shapes clinicians' use and adoption of AI. Trust is a psychological mechanism to deal with the uncertainty between what is known and unknown. Several research studies have highlighted the need for improving AI-based systems and enhancing their capabilities to help clinicians. However, assessing the magnitude and impact of human trust on AI technology demands substantial attention. Will a clinician trust an AI-based system? What are the factors that influence human trust in AI? Can trust in AI be optimized to improve decision-making processes? In this paper, we focus on clinicians as the primary users of AI systems in health care and present factors shaping trust between clinicians and AI. We highlight critical challenges related to trust that should be considered during the development of any AI system for clinical use.
10.2196/15154
Artificial intelligence and machine learning for protein toxicity prediction using proteomics data.
Vishnoi Shubham,Matre Himani,Garg Prabha,Pandey Shubham Kumar
Chemical biology & drug design
Instead of only focusing on the targeted drug delivery system, researchers have a great interest in developing peptide-based therapies for the procurement of numerous class of diseases. The main idea behind this is to anchor the properties of the receptor to design peptide-based therapeutics. As these macromolecules have distinct physicochemical properties over small molecules, it becomes an obligatory field for the treatment of diseases. For this, various in silico models have been developed to speculate the proteins by virtue of the application of machine learning and artificial intelligence. By analysing the properties and structural alert of toxic proteins, researchers aim to dissert some of the mechanisms of protein toxicity from which therapeutic insights may be drawn. Numerous models already exist worldwide emphasizing themselves as leading paramount for toxicity prediction in protein macromolecules. Few of them comparatively compete with the other predictive protein toxicity models and convincingly give a high-performance result in terms of accuracy. But their foundation is quite ambiguous, and varying approaches are found at the level of toxicoproteomic data utilization while building a machine learning model. In this review work, we present the contribution of artificial intelligence and machine learning approaches in prediction of protein toxicity using proteomics data.
10.1111/cbdd.13701
Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives.
Shi Z,Hu B,Schoepf U J,Savage R H,Dargis D M,Pan C W,Li X L,Ni Q Q,Lu G M,Zhang L J
AJNR. American journal of neuroradiology
Intracranial aneurysms with subarachnoid hemorrhage lead to high morbidity and mortality. It is of critical importance to detect aneurysms, identify risk factors of rupture, and predict treatment response of aneurysms to guide clinical interventions. Artificial intelligence has received worldwide attention for its impressive performance in image-based tasks. Artificial intelligence serves as an adjunct to physicians in a series of clinical settings, which substantially improves diagnostic accuracy while reducing physicians' workload. Computer-assisted diagnosis systems of aneurysms based on MRA and CTA using deep learning have been evaluated, and excellent performances have been reported. Artificial intelligence has also been used in automated morphologic calculation, rupture risk stratification, and outcomes prediction with the implementation of machine learning methods, which have exhibited incremental value. This review summarizes current advances of artificial intelligence in the management of aneurysms, including detection and prediction. The challenges and future directions of clinical implementations of artificial intelligence are briefly discussed.
10.3174/ajnr.A6468
Artificial Intelligence in Intracoronary Imaging.
Fedewa Russell,Puri Rishi,Fleischman Eitan,Lee Juhwan,Prabhu David,Wilson David L,Vince D Geoffrey,Fleischman Aaron
Current cardiology reports
PURPOSE OF REVIEW:This paper investigates present uses and future potential of artificial intelligence (AI) applied to intracoronary imaging technologies. RECENT FINDINGS:Advances in data analytics and digitized medical imaging have enabled clinical application of AI to improve patient outcomes and reduce costs through better diagnosis and enhanced workflow. Applications of AI to IVUS and IVOCT have produced improvements in image segmentation, plaque analysis, and stent evaluation. Machine learning algorithms are able to predict future coronary events through the use of imaging results, clinical evaluations, laboratory tests, and demographics. The application of AI to intracoronary imaging holds significant promise for improved understanding and treatment of coronary heart disease. Even in these early stages, AI has demonstrated the ability to improve the prediction of cardiac events. Large curated data sets and databases are needed to speed the development of AI and enable testing and comparison among algorithms.
10.1007/s11886-020-01299-w
Artificial intelligence in cardiac radiology.
van Assen Marly,Muscogiuri Giuseppe,Caruso Damiano,Lee Scott J,Laghi Andrea,De Cecco Carlo N
La Radiologia medica
Artificial intelligence (AI) is entering the clinical arena, and in the early stage, its implementation will be focused on the automatization tasks, improving diagnostic accuracy and reducing reading time. Many studies investigate the potential role of AI to support cardiac radiologist in their day-to-day tasks, assisting in segmentation, quantification, and reporting tasks. In addition, AI algorithms can be also utilized to optimize image reconstruction and image quality. Since these algorithms will play an important role in the field of cardiac radiology, it is increasingly important for radiologists to be familiar with the potential applications of AI. The main focus of this article is to provide an overview of cardiac-related AI applications for CT and MRI studies, as well as non-imaging-based applications for reporting and image optimization.
10.1007/s11547-020-01277-w
Artificial intelligence for the management of pancreatic diseases.
Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
Novel artificial intelligence techniques are emerging in all fields of healthcare, including gastroenterology. The aim of this review is to give an overview of artificial intelligence applications in the management of pancreatic diseases. We performed a systematic literature search in PubMed and Medline up to May 2020 to identify relevant articles. Our results showed that the development of machine-learning based applications is rapidly evolving in the management of pancreatic diseases, guiding precision medicine in clinical, endoscopic and radiologic settings. Before implementation into clinical practice, further research should focus on the external validation of novel techniques, clarifying the accuracy and robustness of these models.
10.1111/den.13875