Ocular Imaging Challenges, Current State, and a Path to Interoperability: A HIMSS-SIIM Enterprise Imaging Community Whitepaper.

Office-based testing, enhanced by advances in imaging technology, is routinely used in eye care to non-invasively assess ocular structure and function. This type of imaging coupled with autonomous artificial intelligence holds immense opportunity to diagnose eye diseases quickly. Despite the wide availability and use of ocular imaging, there are several factors that hinder optimization of clinical practice and patient care.

Imaging Informatics Education in Clinical Informatics Programs: Perspective from Imaging and Clinical Informatics Professionals.

Background:  Imaging and Clinical Informatics are domains of biomedical informatics. Imaging Informatics topics are often not covered in depth in most Clinical Informatics fellowships. While dedicated Imaging Informatics fellowships exist, they may not have the same rigor as ACGME (Accreditation Council for Graduate Medical Education) accredited Clinical Informatics fellowships and they do not provide a direct path toward subspecialty board certification.

A pilot study of volumetric and density tumor analysis of ACC patients treated with vorinostat in a phase II clinical trial.

Rationale And Objectives: Adenoid cystic carcinoma (ACC) is a rare salivary gland cancer. The vast majority of clinical trials evaluating systemic therapy efficacy in solid tumors use the Response Evaluation Criteria in Solid Tumors (RECIST) to measure response that is limited to 2 dimensional only evaluations, not taking volume or density into account. The indolent behavior ACC represents a challenge toward an appropriate evaluation of therapy response.

Annotations of Lung Abnormalities in Shenzhen Chest X-ray Dataset for Computer-Aided Screening of Pulmonary Diseases.

Developments in deep learning techniques have led to significant advances in automated abnormality detection in radiological images and paved the way for their potential use in computer-aided diagnosis (CAD) systems. However, the development of CAD systems for pulmonary tuberculosis (TB) diagnosis is hampered by the lack of training data that is of good visual and diagnostic quality, of sufficient size, variety, and, where relevant, containing fine region annotations. This study presents a collection of annotations/segmentations of pulmonary radiological manifestations that are consistent with TB in the publicly available and widely used Shenzhen chest X-ray (CXR) dataset made available by the U.

Interactive Multimedia Reporting Technical Considerations: HIMSS-SIIM Collaborative White Paper.

Despite technological advances in the analysis of digital images for medical consultations, many health information systems lack the ability to correlate textual descriptions of image findings linked to the actual images. Images and reports often reside in separate silos in the medical record throughout the process of image viewing, report authoring, and report consumption. Forward-thinking centers and early adopters have created interactive reports with multimedia elements and embedded hyperlinks in reports that connect the narrative text with the related source images and measurements.

GIMAP6 regulates autophagy, immune competence, and inflammation in mice and humans.

Inborn errors of immunity (IEIs) unveil regulatory pathways of human immunity. We describe a new IEI caused by mutations in the GTPase of the immune-associated protein 6 (GIMAP6) gene in patients with infections, lymphoproliferation, autoimmunity, and multiorgan vasculitis. Patients and Gimap6-/- mice show defects in autophagy, redox regulation, and polyunsaturated fatty acid (PUFA)-containing lipids.

Detecting Tuberculosis-Consistent Findings in Lateral Chest X-Rays Using an Ensemble of CNNs and Vision Transformers.

Research on detecting Tuberculosis (TB) findings on chest radiographs (or Chest X-rays: CXR) using convolutional neural networks (CNNs) has demonstrated superior performance due to the emergence of publicly available, large-scale datasets with expert annotations and availability of scalable computational resources. However, these studies use only the frontal CXR projections, i.e.

Rethinking Clinical Trial Radiology Workflows and Student Training: Integrated Virtual Student Shadowing Experience, Education, and Evaluation.

There is consistent demand for clinical exposure from students interested in radiology; however, the COVID-19 pandemic resulted in fewer available options and limited student access to radiology departments. Additionally, there is increased demand for radiologists to manage more complex quantification in reports on patients enrolled in clinical trials. We present an online educational curriculum that addresses both of these gaps by virtually immersing students (radiology preprocessors, or RPs) into radiologists' workflows where they identify and measure target lesions in advance of radiologists, streamlining report quantification.

Gamification in Radiology Training Module Developed During the Society for Imaging Informatics in Medicine Annual Meeting Hackathon.

The purpose of this manuscript is to report our experience in the 2021 SIIM Virtual Hackathon, where we developed a proof-of-concept of a radiology training module with elements of gamification. In the 50 h allotted in the hackathon, we proposed an idea, connected with colleagues from five different countries, and completed an operational proof-of-concept, which was demonstrated live at the hackathon showcase, competing with eight other teams. Our prototype involved participants annotating publicly available chest radiographs of patients with tuberculosis.

Training Strategies for Radiology Deep Learning Models in Data-limited Scenarios.

Data-driven approaches have great potential to shape future practices in radiology. The most straightforward strategy to obtain clinically accurate models is to use large, well-curated and annotated datasets. However, patient privacy constraints, tedious annotation processes, and the limited availability of radiologists pose challenges to building such datasets.

Fatal autoimmune pneumonitis requiring bilobectomy and omental flap repair in a patient with autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy (APECED).

We present a severe case of progressive autoimmune pneumonitis requiring surgical intervention in a patient with the monogenic syndrome, autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy (APECED). APECED is caused by loss-of-function mutations in the autoimmune regulator () gene, which lead to impaired central immune tolerance and autoimmune organ destruction including pneumonitis, an underrecognized, life-threatening complication. When clinicians evaluate patients with pneumonitis, recurrent mucosal candidiasis, and autoimmunity, APECED should be considered in the differential.

Collaborative Writing Projects: Set Yourself up for Success.

This article will review critical components for the successful completion of a multi-institution, multiauthor collaborative paper. Best practices for the creation and publication of a collaborative paper will be addressed.

Multispecialty Enterprise Imaging Workgroup Consensus on Interactive Multimedia Reporting Current State and Road to the Future: HIMSS-SIIM Collaborative White Paper.

Diagnostic and evidential static image, video clip, and sound multimedia are captured during routine clinical care in cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, endoscopic procedural specialties, and other medical disciplines. Providers typically describe the multimedia findings in contemporaneous electronic health record clinical notes or associate a textual interpretative report. Visual communication aids commonly used to connect, synthesize, and supplement multimedia and descriptive text outside medicine remain technically challenging to integrate into patient care.

Pulmonary Manifestations of GATA2 Deficiency.

Background: GATA2 deficiency is a genetic disorder of hematopoiesis, lymphatics, and immunity caused by autosomal dominant or sporadic mutations in GATA2. The disease has a broad phenotype encompassing immunodeficiency, myelodysplasia, leukemia, and vascular or lymphatic dysfunction as well as prominent pulmonary manifestations.

Research Question: What are the pulmonary manifestations of GATA2 deficiency?

Study Design And Methods: A retrospective review was conducted of clinical medical records, diagnostic imaging, pulmonary pathologic specimens, and tests of pulmonary function.

CT Evaluation of Lymph Nodes That Merge or Split during the Course of a Clinical Trial: Limitations of RECIST 1.1.

Purpose To compare Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 with volumetric measurement in the setting of target lymph nodes that split into two or more nodes or merge into one conglomerate node. Materials and Methods In this retrospective study, target lymph nodes were evaluated on CT scans from 166 patients with different types of cancer; 158 of the scans came from The Cancer Imaging Archive.

Improved Semantic Segmentation of Tuberculosis-Consistent Findings in Chest X-rays Using Augmented Training of Modality-Specific U-Net Models with Weak Localizations.

Deep learning (DL) has drawn tremendous attention for object localization and recognition in both natural and medical images. U-Net segmentation models have demonstrated superior performance compared to conventional hand-crafted feature-based methods. Medical image modality-specific DL models are better at transferring domain knowledge to a relevant target task than those pretrained on stock photography images.

Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs.

Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, their use in medical computer vision tasks faces several limitations, viz., (i) adapting to visual characteristics that are unlike natural images; (ii) modeling random noise during training due to stochastic optimization and backpropagation-based learning strategy; (iii) challenges in explaining DL black-box behavior to support clinical decision-making; and (iv) inter-reader variability in the ground truth (GT) annotations affecting learning and evaluation.

Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays.

We demonstrate use of iteratively pruned deep learning model ensembles for detecting pulmonary manifestation of COVID-19 with chest X-rays. This disease is caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, also known as the novel Coronavirus (2019-nCoV). A custom convolutional neural network and a selection of ImageNet pretrained models are trained and evaluated at patient-level on publicly available CXR collections to learn modality-specific feature representations.

Web-based Conferencing: Tips, Tricks, and Scenario-based Best Practices for Clinicians During a Pandemic Crisis.

This article will familiarize the reader with useful tools and trouble-shooting tips for web-based conferencing. Radiology-based scenarios for web conferencing are also provided.

Cabozantinib in patients with platinum-refractory metastatic urothelial carcinoma: an open-label, single-centre, phase 2 trial.

Background: Cabozantinib is a multikinase inhibitor of MET, VEGFR, AXL, and RET, which also has an effect on the tumour immune microenvironment by decreasing regulatory T cells and myeloid-derived suppressor cells. In this study, we examined the activity of cabozantinib in patients with metastatic platinum-refractory urothelial carcinoma.

Methods: This study was an open-label, single-arm, three-cohort phase 2 trial done at the National Cancer Institute (Bethesda, MD, USA).

INVESTIGATION OF THE INFLUENCE OF THYROID LOCATION ON IODINE-131 S VALUES.

The use of iodine-131 S values based on reference computational phantoms with fixed thyroid model may lead to significant dosimetric errors in patients who may have different thyroid location from the reference phantoms. In the present study, we investigated individual thyroid location variation by examining the computed tomography image sets of 40 adult male and female patients. Subsequently, the thyroid location of the adult male and female mesh-type reference phantoms of the International Commission on Radiological Protection (ICRP) was adjusted to match each the highest, mean and the lowest locations of the thyroid observed in this dataset.

Preparing Medical Imaging Data for Machine Learning.

Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations).

Augmented Radiologist Workflow Improves Report Value and Saves Time: A Potential Model for Implementation of Artificial Intelligence.

Rationale And Objectives: Our primary aim was to improve radiology reports by increasing concordance of target lesion measurements with oncology records using radiology preprocessors (RP). Faster notification of incidental actionable findings to referring clinicians and clinical radiologist exam interpretation time savings with RPs quantifying tumor burden were also assessed.

Materials And Methods: In this prospective quality improvement initiative, RPs annotated lesions before radiologist interpretation of CT exams.