Since the onset of the COVID-19 pandemic in 2019, many clinical prognostic scoring tools have been proposed or developed to aid clinicians in the disposition and severity assessment of pneumonia. However, there is limited work that focuses on explaining techniques that are best suited for clinicians in their decision making. In this article, we present a new image explainability method named ensemble AI explainability (XAI), which is based on the SHAP and Grad-CAM++ methods. It provides a visual explanation for a deep learning prognostic model that predicts the mortality risk of community-acquired pneumonia and COVID-19 respiratory infected patients. In addition, we surveyed the existing literature and compiled prevailing quantitative and qualitative metrics to systematically review the efficacy of ensemble XAI, and to make comparisons with several state-of-the-art explainability methods (LIME, SHAP, saliency map, Grad-CAM, Grad-CAM++). Our quantitative experimental results have shown that ensemble XAI has a comparable absence impact (decision impact: 0.72, confident impact: 0.24). Our qualitative experiment, in which a panel of three radiologists were involved to evaluate the degree of concordance and trust in the algorithms, has showed that ensemble XAI has localization effectiveness (mean set accordance precision: 0.52, mean set accordance recall: 0.57, mean set ${{\bm{F}}_1}$ : 0.50, mean set IOU: 0.36) and is the most trusted method by the panel of radiologists (mean vote: 70.2%). Finally, the deep learning interpretation dashboard used for the radiologist panel voting will be made available to the community. Our code is available at https://github.com/IHIS-HealthInsights/Interpretation-Methods-Voting-dashboard .

This paper describes the development of a deep learning model for prediction of hip fractures on pelvic radiographs (X-rays). Developed using over 40,000 pelvic radiographs from a single institution, the model demonstrated high sensitivity and specificity when applied to a test set of emergency department radiographs. This study approximates the real-world application of a deep learning fracture detection model by including radiographs with sub-optimal image quality, other non-hip fractures, and metallic implants, which were excluded from prior published work. The study also explores the effect of ethnicity on model performance, as well as the accuracy of visualization algorithm for fracture localization.

An increasing number of patients are being treated with cardiovascular implantable electronic devices (CIEDs), many of which are MR conditional. There is a lack of literature on the safe scanning of MR conditional CIEDs. This review article discusses MR imaging safety in patients with implanted CIEDs. Guidelines on safe use and indications of imaging patients with MR conditional CIEDs are described, followed by a pictorial essay of the radiographic features of these devices. We also discuss the challenges of monitoring the patient in the MR environment, advances in MRI conditional imaging of devices, availability, limitations and workflow including vendor-specific and other collaborative efforts to simplify the scanning process. Radiologists must be able to facilitate the safe utilization of MR imaging in patients who have CIEDs. A thorough knowledge of the hazards of imaging non-MR compatible devices is required as well as knowing how to correctly identify and manage the imaging of patients with MR conditional CIEDs. Finally, we propose steps required to facilitate the safe scanning of patients with MR conditional CIEDs adopted in our institution and a contingency plan in the event that an inadvertent MR scan of a patient with a MRI unsafe CIED should occur.

Amyloidosis is a rare disorder where abnormal protein aggregates are deposited in tissues forming amyloid fibrils, leading to progressive organ failure. Although any organ can be affected, cardiac involvement is the main cause of morbidity and mortality associated with amyloidosis as diagnosis is often delayed due to the indolent nature of the disease in some forms. An early diagnosis of disease and knowledge of the type/subtype of cardiac amyloidosis (CA) are essential for appropriate management and better outcome. Echocardiography is often the first line of investigation for patients suspected of CA and offers superior hemodynamic assessment. Although cardiovascular magnetic resonance (CMR) imaging is not diagnostic of CA, it provides vital clues to diagnosis and has a role in disease quantification and prognostication. Radiolabeled bone seeking tracers are the mainstay of diagnosis of CA and when combined with screening of monoclonal light chains, bone scintigraphy offers high sensitivity in diagnosing transthyretin type of CA. This review aims to describe the noninvasive imaging assessment and approach to diagnosis of patients with suspected CA. Imaging features of echocardiography, nuclear scintigraphy, and CMR are described with a brief mention on computed tomography. Article published online: 16 August 2022 © 2022. World Association of Radiopharmaceutical and Molecular Therapy (WARMTH). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/) Thieme Medical and Scientific Publishers Pvt. Ltd. A-12, 2nd Floor, Sector 2, Noida-201301 UP, India

Radiology is increasingly adopting AI systems, and to ensure their safety after the systems go live, postdeployment monitoring is essential as part of the total product life cycle framework.