Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 1: Report of the MIDI Task Group - Best Practices and Recommendations, Tools for Conventional Approaches to De-identification, International Approaches to De-identification, and Industry Panel on Image De-identification.

De-identification of medical images intended for research is a core requirement for data-sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and Information Technology (CBIIT) of the US National Cancer Institute (NCI) convened a virtual workshop with the intent of summarizing the state of the art in de-identification technology and processes and exploring interesting aspects of the subject. This paper summarizes the highlights of the first day of the workshop, the recordings, and presentations of which are publicly available for review.

Accelerating genomic workflows using NVIDIA Parabricks.

Background: As genome sequencing becomes better integrated into scientific research, government policy, and personalized medicine, the primary challenge for researchers is shifting from generating raw data to analyzing these vast datasets. Although much work has been done to reduce compute times using various configurations of traditional CPU computing infrastructures, Graphics Processing Units (GPUs) offer opportunities to accelerate genomic workflows by orders of magnitude. Here we benchmark one GPU-accelerated software suite called NVIDIA Parabricks on Amazon Web Services (AWS), Google Cloud Platform (GCP), and an NVIDIA DGX cluster.

Are You Using the Right Electronic Health Record?

With the enactment of the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009, hospitals and physician practices across the country converted from a system of paper recordkeeping to fully integrated electronic health records (EHR)., With financial incentives in hand, there was a rush to market to acquire and implement these systems. Fast-forward 10 years, and it is apparent that the EHR space has significantly evolved in technology, processes, and policies.

Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery.

Background: Artificial intelligence (AI), including machine learning (ML) and deep learning, has the potential to revolutionize biomedical research. Defined as the ability to "mimic" human intelligence by machines executing trained algorithms, AI methods are deployed for biomarker discovery.

Objective: We detail the advancements and challenges in the use of AI for biomarker discovery in ovarian and pancreatic cancer.

FAIRshake: Toolkit to Evaluate the FAIRness of Research Digital Resources.

As more digital resources are produced by the research community, it is becoming increasingly important to harmonize and organize them for synergistic utilization. The findable, accessible, interoperable, and reusable (FAIR) guiding principles have prompted many stakeholders to consider strategies for tackling this challenge. The FAIRshake toolkit was developed to enable the establishment of community-driven FAIR metrics and rubrics paired with manual and automated FAIR assessments.

Effect of battery longevity on costs and health outcomes associated with cardiac implantable electronic devices: a Markov model-based Monte Carlo simulation.

Introduction: The effects of device and patient characteristics on health and economic outcomes in patients with cardiac implantable electronic devices (CIEDs) are unclear. Modeling can estimate costs and outcomes for patients with CIEDs under a variety of scenarios, varying battery longevity, comorbidities, and care settings. The objective of this analysis was to compare changes in patient outcomes and payer costs attributable to increases in battery life of implantable cardiac defibrillators (ICDs) and cardiac resynchronization therapy defibrillators (CRT-D).

Estimated hospital costs associated with preventable health care-associated infections if health care antiseptic products were unavailable.

Objectives: Health care-associated infections (HAIs) pose a significant health care and cost burden. This study estimates annual HAI hospital costs in the US avoided through use of health care antiseptics (health care personnel hand washes and rubs; surgical hand scrubs and rubs; patient preoperative and preinjection skin preparations).

Methods: A spreadsheet model was developed with base case inputs derived from the published literature, supplemented with assumptions when data were insufficient.

Multilevel modeling and value of information in clinical trial decision support.

Background: Clinical trials are the main method for evaluating safety and efficacy of medical interventions and have produced many advances in improving human health. The Women's Health Initiative overturned a half-century of harmful practice in hormone therapy, the National Lung Screening Trial identified the first successful lung cancer screening tool and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial overturned decades-long assumptions. While some trials identify unforeseen safety issues or harms, many fail to demonstrate efficacy.

Characteristics and value of machine learning for imaging in high content screening.

Requirements for a flexible image analysis package for high content screening (HCS) are discussed. An overview of tools and techniques for image analysis and machine learning is given. Machine learning for classification and segmentation, the two fundamental elements of image analysis, is discussed.