Search

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site’s data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare. Federated learning, a method for training artificial intelligence algorithms that protects data privacy, was used to predict future oxygen requirements of symptomatic patients with COVID-19 using data from 20 different institutes across the globe.

Natural language processing (NLP) is a methodology designed to extract concepts and meaning from human-generated unstructured (free-form) text. It is intended to be implemented by using computer algorithms so that it can be run on a corpus of documents quickly and reliably. To enable machine learning (ML) techniques in NLP, free-form text must be converted to a numerical representation. After several stages of preprocessing including tokenization, removal of stop words, token normalization, and creation of a master dictionary, the bag-of-words (BOW) technique can be used to represent each remaining word as a feature of the document. The preprocessing steps simplify the documents but also potentially degrade meaning. The values of the features in BOW can be modified by using techniques such as term count, term frequency, and term frequency–inverse document frequency. Experience and experimentation will guide decisions on which specific techniques will optimize ML performance. These and other NLP techniques are being applied in radiology. Radiologists’ understanding of the strengths and limitations of these techniques will help in communication with data scientists and in implementation for specific tasks. Online supplemental material is available for this article. (©)RSNA, 2021

Integration of artificial intelligence (AI) applications within clinical workflows is an important step for leveraging developed AI algorithms. In this report, generalizable components for deploying AI systems into clinical practice are described that were implemented in a clinical pilot study using lymphoscintigraphy examinations as a prospective use case (July 1, 2019–October 31, 2020). Deployment of the AI algorithm consisted of seven software components, as follows: (a) image delivery, (b) quality control, (c) a results database, (d) results processing, (e) results presentation and delivery, (f) error correction, and (g) a dashboard for performance monitoring. A total of 14 users used the system (faculty radiologists and trainees) to assess the degree of satisfaction with the components and overall workflow. Analyses included the assessment of the number of examinations processed, error rates, and corrections. The AI system processed 1748 lymphoscintigraphy examinations. The system enabled radiologists to correct 146 AI results, generating real-time corrections to the radiology report. All AI results and corrections were successfully stored in a database for downstream use by the various integration components. A dashboard allowed monitoring of the AI system performance in real time. All 14 survey respondents “somewhat agreed” or “strongly agreed” that the AI system was well integrated into the clinical workflow. In all, a framework of processes and components for integrating AI algorithms into clinical workflows was developed. The implementation described could be helpful for assessing and monitoring AI performance in clinical practice. Keywords: PACS, Computer Applications-General (Informatics), Diagnosis © RSNA, 2021

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC)0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

PurposeStarting from a broad-based needs assessment and utilizing an image analysis algorithm (IAA) developed at our institution, the purpose of this study was to define generalizable building blocks necessary for the integration of any IAA into a clinical practice.MethodsAn IAA was developed in our institution to process lymphoscintigraphy exams. A team of radiologists defined a set of building blocks for integration of this IAA into clinical workflow. The building blocks served the following roles: (1) Timely delivery of images to the IAA, (2) quality control, (3) IAA results processing, (4) results presentation & delivery, (5) IAA error correction, (6) system performance monitoring, and (7) active learning. Utilizing these modules, the lymphoscintigraphy IAA was integrated into the clinical workflow at our institution. System performance was tested over a 1 month period, including assessment of number of exams processed and delivered, and error rates and corrections.ResultsFrom June 26-July 27, 2019, the building blocks were used to integrate IAA results from 132 lymphoscintigraphy exams into the clinical workflow, representing 100% of the exams performed during the time period. The system enabled radiologists to correct 21 of the IAA results. All results and corrections were successfully stored in a database. A dashboard allowed the development team to monitor system performance in real-time.ConclusionsWe describe seven building blocks that optimize the integration of IAAs into clinical workflow. The implementation of these building blocks in this study can be used to inform development of more robust, standards-based solutions.