Pediatric radiologists are self-taught and independently interpret artificial intelligence manuscripts. However, they might not identify the pitfalls and biases related to complex data from pediatric models. Pediatric imaging differs from adult imaging in several instances such as sample size, growth-related disorders, disease presentation, quality standards, image distortions and motioning, radiation safety adjustment, and other challenges that can impact the development of algorithms and models. The specific metrics to measure the performance of the model must be in accordance with the data and clinical task, as it can vary within the prevalence of the disease and with the balance of the dataset, and therefore can mislead the model performance results. Challenges related to the model development can arise from data management, data transformation, small datasets, imbalanced datasets, optimization, and type of validation. All those pieces of information are as important as the medical aspect, however, in artificial intelligence, manuscripts will explore computational engineering and data science more than clinical knowledge. The understanding of the learning architecture, classifiers, parameter, and algorithms will allow the reader to translate engineering details into clinical meaning. In this work, we will present pitfalls and biases during data curation, hyperparameters, weights, validation, and classification metrics; and discuss the best practices to mitigate those challenges. This work integrates our group experience with a thorough literature review of prior artificial intelligence manuscripts in pediatric radiology for the analysis and application of artificial intelligence to clinical practice. Our goal is to improve critical thinking when interpreting scientific research in artificial intelligence for pediatric radiologists. Read More

Meeting name: SPR 2024 Annual Meeting & Postgraduate Course , 2024

Authors: Rafful Patricia, Khalkhali Vahid, Alkhulaifat Dana, Welsh Michael, Wieczkowski Sydney, Sotardi Susan

Keywords: Pitfalls, Machine Learning, Best Practices

Artificial intelligence (AI) applications for radiology have undergone exponential growth in recent years, owing to the development of large datasets for use in machine learning algorithms and technological advancements in the field of imaging informatics. However, the advancement of AI algorithms in pediatric radiology has lagged behind adult applications. Currently, only seven commercially-available AI algorithms have received FDA approval for use in the pediatric population [1]. One of the major factors limiting the use of AI in pediatric radiology is the lack of the requisite large pediatric imaging datasets. In AI research and implementation, pediatric radiologists serve as stewards of imaging data. As such, pediatric radiologists should be trained in AI data management, including best practices for the selection, curation, de-identification, and storage of radiology data. Since a necessary first step in the development of AI algorithms requires the curation of large datasets, pediatric radiologists should have a basic understanding of how to archive imaging data for AI research and validation. However, few resources are currently available to provide targeted education for pediatric radiologists with respect to AI data curation.. The aim of this educational exhibit is to provide an educational resource specifically for pediatric radiologists which teaches best practices for data management in AI research, including the selection of patient cohorts, data anonymization techniques, image annotation and segmentation methods, and data storage tools. This exhibit integrates our professional experience, with a thorough literature review of prior AI research, into an educational resource to teach data science methodologies for the management of AI research and clinical implementation to the pediatric radiology community. References 1. AI Central. https://aicentral.acrdsi.org/. Accessed 18 Oct 2022 Read More

Meeting name: SPR 2023 Annual Meeting & Postgraduate Course , 2023

Authors: Alkhulaifat Dana, Rafful Patricia, Lopez Rippe Julian, Khalkhali Vahid, Welsh Michael, Wieczkowski Sydney, Reid Janet, Sotardi Susan

Keywords: Artificial Intelligence, Education, Data Management

In recent years, artificial intelligence (AI) applications for radiology have undergone exponential growth. AI has the potential to assist pediatric radiologists in several respects, including identification of various pathologies and prioritization of studies with urgent imaging findings. Although image analysis is an important application of AI, there are many additional use cases where AI can assist radiologists, including radiology operations and quality analysis. Therefore, it is critical that radiologists familiarize themselves with the wide range of AI methodologies and applications. Although efforts have been made to create formal education for radiologists, currently available resources typically rely upon didactic lectures, without real-life context for the technology. However, case-based education is a principal component of radiology education. Furthermore, prior research has demonstrated that trainees and radiologists are interested in case-based approaches for learning AI [1]. Thus, the purpose of this educational exhibit is to provide an informative resource specifically for pediatric radiologists which utilizes a case-based learning approach. This resource uses 5 cases intended to demonstrate the variety of uses of AI in pediatric radiology, while illustrating the underlying process of AI model selection and development. Specifically, these cases will be selected to encompass AI topics including: image classification, lesion segmentation, natural language processing for imaging reports, recommender systems to enhance radiologist practice and predictive modeling for radiology operations research. This approach allows us to explore decision-points in AI model building, while examining potential pitfalls that may arise. This educational exhibit was developed based on a thorough literature review of prior AI research combined with our professional experience, and aims to provide educational resource intended to teach AI model selection and development to pediatric radiologists. References 1. Velez-Florez MC, Ghosh A, Patton D, et al (2022) Artificial intelligence curriculum needs assessment for a pediatric radiology fellowship program: what, how, and why? Acad Radiol. https://doi.org/10.1016/j.acra.2022.04.026 Read More

Meeting name: SPR 2023 Annual Meeting & Postgraduate Course , 2023

Authors: Alkhulaifat Dana, Rafful Patricia, Lopez Rippe Julian, Khalkhali Vahid, Welsh Michael, Wieczkowski Sydney, Reid Janet, Sotardi Susan

Keywords: Artificial Intelligence, Case-Based Learning, Education

Artificial intelligence (AI) has the potential to improve many aspects of pediatric radiology. AI solutions have emerged to perform a wide range of tasks, including facilitating the workload of radiologists, improving image quality, performing motion correction, and reducing contrast dose. While a myriad of courses for learning AI are available, most resources lack the necessary radiology perspective required for clinical application. Few interactive resources are available to teach a systematic approach for pediatric radiologists when analyzing AI literature. Since AI methodologies in pediatric radiology research fundamentally differ from classical statistical analyses, an educational poster dedicated to interpreting pediatric radiology AI literature would assist in bridging this gap. The purpose of this education exhibit is to provide a resource for pediatric radiologists that teaches a systematic approach for the interpretation of AI research publications. Knowledge delivery will be made by eLearning slide-based presentation. The major topics regarding AI literature will focus on: problem identification; data curation; data annotation, artificial intelligence models; validation and performance. Next, we review prior literature on important criteria for AI publications, including Checklist for Artificial Intelligence in Medical Imaging (CLAIM), the AI version of the Standards for Reporting of Diagnostic Accuracy Studies (STARD-AI), Transparent Report of Multivariable Prediction Model of Individual Prognosis and Diagnosis for AI (TRIPOD-AI) and Prediction Model Risk of Bias Assessment Tool for AI (PROBAST-AI). Lastly, we present these criteria in the context of pediatric AI research applications. AI research methodology in pediatric radiology differs from classical research methodologies. Therefore, this education exhibit is intended to teach the systematic interpretation of the primary AI literature to pediatric radiologists in order to translate scientific knowledge into meaningful clinical information. Read More

Meeting name: SPR 2023 Annual Meeting & Postgraduate Course , 2023

Authors: Rafful Patricia, Alkhulaifat Dana, Lopez Rippe Julian, Khalkhali Vahid, Welsh Michael, Venkatakrishna Shyam Sunder, Wieczkowski Sydney, Reid Janet, Sotardi Susan

Keywords: Artificial Intelligence, Radiology, Research