Report from the RSNA COVID-19 Task Force: COVID-19 Impact on Academic Radiology Research-A Survey of Vice Chairs of Research.

OBJECTIVE: Survey vice chairs of research from academic radiology departments on the impact of coronavirus disease 2019 (COVID-19) on research activities. METHODS: The survey asked respondents to quantify changes in research performed during the shutdown and ramp-up, relative to pre-COVID-19 levels. Respondents estimated research activity changes by overall research type (wet, instrumentation, or core facilities: prospective non-COVID-19 clinical research and computational laboratories) and then by the research activity type (data analysis, grant or manuscript writing, clinician involvement, summer student participation, and international research fellow appointments).The χ2 test was used for comparison between shutdown and ramp-up, with Yates correction when necessary. RESULTS: Of 105 vice chairs contacted, 46 (43.8%) responded. For 95.5%, wet, instrumentation, or core facilities research decreased to ≤50% during shutdown and for 83.3% during ramp-up (P < .0001). In addition, 89.2% and 46.5% indicated reduction to ≤25% of non-COVID-19 clinical research during shutdown and ramp-up, respectively (P < .0001). Only computational research increased to 120% during shutdown (39.5%) or ramp-up (50%) (P = .8984). For data analysis from closed laboratories, 75% and 86% showed decreased activity during shutdown and ramp-up, respectively (P = .28). Increased grant writing during shutdown and ramp-up was reported by 45.5% and 23.3% (P = .093). For 52.3% and 23.3%, manuscript writing and submission increased during shutdown and ramp-up, respectively (P < .02). Clinician research involvement trended toward relative decreases during shutdown (84.1% versus 60.5%, P = .05). There was similar drop in summer student participation (shutdown: 86.4%, ramp-up: 83.7%, P = .95) and international researcher appointment (shutdown: 85.7%, ramp-up: 86.1%; P = .96). CONCLUSION: Many radiology research activities diminished during the COVID-19 shutdown and to a lesser extent during the ramp-up. Activities that could be done remotely, such as computational analysis and grant and manuscript writing and submission, increased.

A review of explainable and interpretable AI with applications in COVID-19 imaging.

The development of medical imaging artificial intelligence (AI) systems for evaluating COVID-19 patients has demonstrated potential for improving clinical decision making and assessing patient outcomes during the recent COVID-19 pandemic. These have been applied to many medical imaging tasks, including disease diagnosis and patient prognosis, as well as augmented other clinical measurements to better inform treatment decisions. Because these systems are used in life-or-death decisions, clinical implementation relies on user trust in the AI output. This has caused many developers to utilize explainability techniques in an attempt to help a user understand when an AI algorithm is likely to succeed as well as which cases may be problematic for automatic assessment, thus increasing the potential for rapid clinical translation. AI application to COVID-19 has been marred with controversy recently. This review discusses several aspects of explainable and interpretable AI as it pertains to the evaluation of COVID-19 disease and it can restore trust in AI application to this disease. This includes the identification of common tasks that are relevant to explainable medical imaging AI, an overview of several modern approaches for producing explainable output as appropriate for a given imaging scenario, a discussion of how to evaluate explainable AI, and recommendations for best practices in explainable/interpretable AI implementation. This review will allow developers of AI systems for COVID-19 to quickly understand the basics of several explainable AI techniques and assist in the selection of an approach that is both appropriate and effective for a given scenario.

Medical imaging of COVID-19.

Editor-in-Chief Maryellen Giger introduces the JMI Special Issue on COVID-19 Medical Imaging Research.

Lessons learned in transitioning to AI in the medical imaging of COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc across the world. It also created a need for the urgent development of efficacious predictive diagnostics, specifically, artificial intelligence (AI) methods applied to medical imaging. This has led to the convergence of experts from multiple disciplines to solve this global pandemic including clinicians, medical physicists, imaging scientists, computer scientists, and informatics experts to bring to bear the best of these fields for solving the challenges of the COVID-19 pandemic. However, such a convergence over a very brief period of time has had unintended consequences and created its own challenges. As part of Medical Imaging Data and Resource Center initiative, we discuss the lessons learned from career transitions across the three involved disciplines (radiology, medical imaging physics, and computer science) and draw recommendations based on these experiences by analyzing the challenges associated with each of the three associated transition types: (1) AI of non-imaging data to AI of medical imaging data, (2) medical imaging clinician to AI of medical imaging, and (3) AI of medical imaging to AI of COVID-19 imaging. The lessons learned from these career transitions and the diffusion of knowledge among them could be accomplished more effectively by recognizing their associated intricacies. These lessons learned in the transitioning to AI in the medical imaging of COVID-19 can inform and enhance future AI applications, making the whole of the transitions more than the sum of each discipline, for confronting an emergency like the COVID-19 pandemic or solving emerging problems in biomedicine.

Role of standard and soft tissue chest radiography images in deep-learning-based early diagnosis of COVID-19.

Purpose: We propose a deep learning method for the automatic diagnosis of COVID-19 at patient presentation on chest radiography (CXR) images and investigates the role of standard and soft tissue CXR in this task. Approach: The dataset consisted of the first CXR exams of 9860 patients acquired within 2 days after their initial reverse transcription polymerase chain reaction tests for the SARS-CoV-2 virus, 1523 (15.5%) of whom tested positive and 8337 (84.5%) of whom tested negative for COVID-19. A sequential transfer learning strategy was employed to fine-tune a convolutional neural network in phases on increasingly specific and complex tasks. The COVID-19 positive/negative classification was performed on standard images, soft tissue images, and both combined via feature fusion. A U-Net variant was used to segment and crop the lung region from each image prior to performing classification. Classification performances were evaluated and compared on a held-out test set of 1972 patients using the area under the receiver operating characteristic curve (AUC) and the DeLong test. Results: Using full standard, cropped standard, cropped, soft tissue, and both types of cropped CXR yielded AUC values of 0.74 [0.70, 0.77], 0.76 [0.73, 0.79], 0.73 [0.70, 0.76], and 0.78 [0.74, 0.81], respectively. Using soft tissue images significantly underperformed standard images, and using both types of CXR failed to significantly outperform using standard images alone. Conclusions: The proposed method was able to automatically diagnose COVID-19 at patient presentation with promising performance, and the inclusion of soft tissue images did not result in a significant performance improvement.

Cascaded deep transfer learning on thoracic CT in COVID-19 patients treated with steroids.

Purpose: Given the recent COVID-19 pandemic and its stress on global medical resources, presented here is the development of a machine intelligent method for thoracic computed tomography (CT) to inform management of patients on steroid treatment. Approach: Transfer learning has demonstrated strong performance when applied to medical imaging, particularly when only limited data are available. A cascaded transfer learning approach extracted quantitative features from thoracic CT sections using a fine-tuned VGG19 network. The extracted slice features were axially pooled to provide a CT-scan-level representation of thoracic characteristics and a support vector machine was trained to distinguish between patients who required steroid administration and those who did not, with performance evaluated through receiver operating characteristic (ROC) curve analysis. Least-squares fitting was used to assess temporal trends using the transfer learning approach, providing a preliminary method for monitoring disease progression. Results: In the task of identifying patients who should receive steroid treatments, this approach yielded an area under the ROC curve of 0.85 ± 0.10 and demonstrated significant separation between patients who received steroids and those who did not. Furthermore, temporal trend analysis of the prediction score matched expected progression during hospitalization for both groups, with separation at early timepoints prior to convergence near the end of the duration of hospitalization. Conclusions: The proposed cascade deep learning method has strong clinical potential for informing clinical decision-making and monitoring patient treatment.

Tailoring steroids in the treatment of COVID-19 pneumonia assisted by CT scans: three case reports.

In this article, we analyze and report cases of three patients who were admitted to Renmin Hospital, Wuhan University, China, for treating COVID-19 pneumonia in February 2020 and were unresponsive to initial treatment of steroids. They were then received titrated steroids treatment based on the assessment of computed tomography (CT) images augmented and analyzed with the artificial intelligence (AI) tool and output. Three patients were finally recovered and discharged. The result indicated that sufficient steroids may be effective in treating the COVID-19 patients after frequent evaluation and timely adjustment according to the disease severity assessed based on the quantitative analysis of the images of serial CT scans.