Challenges, Barriers, and Successes of Standardized Report Templates: Results of a Society of Interventional Radiology Survey.

Registry data are being increasingly used to establish treatment guidelines, set benchmarks, allocate resources, and make payment decisions. Although many registries rely on manual data entry, the Society of Interventional Radiology (SIR) is using automated data extraction for its VIRTEX registry. This process relies on participants using consistent terminology with highly structured data in physician-developed standardized reports (SR). To better understand barriers to adoption, a survey was sent to 3,178 SIR members. Responses were obtained from 451 interventional radiology practitioners (14.2%) from 92 unique academic and 151 unique private practices. Of these, 75% used structured reports and 32% used the SIR SR. The most common barriers to the use of these reports include SR length (35% of respondents), lack of awareness about the SR (31%), and lack of agreement on adoption within practices (27%). The results demonstrated insights regarding barriers in the use and/or adoption of SR and potential solutions.

A History and Modern Framework for Quality Improvement in Interventional Radiology.

Quality improvement (QI) initiatives have benefited patients as well as the broader practice of medicine. Large-scale QI has been facilitated by multi-institutional data registries, many of which were formed out of national or international medical society initiatives. With broad participation, QI registries have provided benefits that include but are not limited to establishing treatment guidelines, facilitating research related to uncommon procedures and conditions, and demonstrating the fiscal and clinical value of procedures for both medical providers and health systems. Because of the benefits offered by these databases, Society of Interventional Radiology (SIR) and SIR Foundation have committed to the development of an interventional radiology (IR) clinical data registry known as VIRTEX. A large IR database with participation from a multitude of practice environments has the potential to have a significant positive impact on the specialty through data-driven advances in patient safety and outcomes, clinical research, and reimbursement. This article reviews the current landscape of societal QI programs, presents a vision for a large-scale IR clinical data registry supported by SIR, and discusses the anticipated results that such a framework can produce.

Erratum: Author Correction: Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.

[This corrects the article DOI: 10.1038/s42256-021-00421-z.].

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence.

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.

Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses; however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health.

A collaborative online AI engine for CT-based COVID-19 diagnosis.

Artificial intelligence can potentially provide a substantial role in streamlining chest computed tomography (CT) diagnosis of COVID-19 patients. However, several critical hurdles have impeded the development of robust AI model, which include deficiency, isolation, and heterogeneity of CT data generated from diverse institutions. These bring about lack of generalization of AI model and therefore prevent it from applications in clinical practices. To overcome this, we proposed a federated learning-based Unified CT-COVID AI Diagnostic Initiative (UCADI, http://www.ai-ct-covid.team/), a decentralized architecture where the AI model is distributed to and executed at each host institution with the data sources or client ends for training and inferencing without sharing individual patient data. Specifically, we firstly developed an initial AI CT model based on data collected from three Tongji hospitals in Wuhan. After model evaluation, we found that the initial model can identify COVID from Tongji CT test data at near radiologist-level (97.5% sensitivity) but performed worse when it was tested on COVID cases from Wuhan Union Hospital (72% sensitivity), indicating a lack of model generalization. Next, we used the publicly available UCADI framework to build a federated model which integrated COVID CT cases from the Tongji hospitals and Wuhan Union hospital (WU) without transferring the WU data. The federated model not only performed similarly on Tongji test data but improved the detection sensitivity (98%) on WU test cases. The UCADI framework will allow participants worldwide to use and contribute to the model, to deliver a real-world, globally built and validated clinic CT-COVID AI tool. This effort directly supports the United Nations Sustainable Development Goals' number 3, Good Health and Well-Being, and allows sharing and transferring of knowledge to fight this devastating disease around the world.

Late Clinical Presentation of Prosthesis-Patient Mismatch Following Transcatheter Aortic Valve Replacement.

Prosthesis-patient mismatch (PPM) is relatively common after aortic valve replacement (AVR) and generally is associated with reduced regression of left ventricular (LV) mass. PPM after valve-in-valve transcatheter aortic valve replacement (TAVR) was reported to be 38%. PPM generally is manifested clinically by dyspnea and echocardiographically by high transvalvular gradients. In this E-Challenge, the authors will review a case of a late clinical presentation of PPM 1-year following a valve-in-valve TAVR.