The Emerging Role of Automation, Measurement Standardization, and Artificial Intelligence in Foot and Ankle Imaging: An Update.

In the past few years, advances in clinical imaging in the realm of foot and ankle have been consequential and game changing. Improvements in the hardware aspects, together with the development of computer-assisted interpretation and intervention tools, have led to a noticeable improvement in the quality of health care for foot and ankle patients. Focusing on the mainstay imaging tools, including radiographs, computed tomography scans, and ultrasound, in this review study, the authors explored the literature for reports on the new achievements in improving the quality, accuracy, accessibility, and affordability of clinical imaging in foot and ankle.

Transforming experimental radiology: Design and implementation of an innovative ePACS image storage system for AI imaging research environments.

INTRODUCTION AND PURPOSE: We present the needs, design, development, implementation, and accessibility of a crafted experimental PACS (ePACS) system to securely store images, ensuring efficiency and ease of use for AI processing, specifically tailored for research scenarios, including phantoms, animal and human studies and quality assurance (QA) exams. The ePACS system plays a crucial role in any medical imaging departments that handle non-care profile studies, such as protocol adjustments and dummy runs. By effectively segregating non-care profile studies from the healthcare assistance, the ePACS usefully prevents errors both in clinical practice and storage security. METHODS AND RESULTS: The developed ePACS system considers the best practices for management, maintenance, access, long-term storage and backups, regulatory audits, and economic aspects. Moreover, key aspects of the ePACS system include the design of data flows with a focus on incorporating data security and privacy, access control and levels based on user profiles, internal data management policies, standardized architecture, infrastructure and application monitorization and traceability, and periodic backup policies. A new tool called DicomStudiesQA has been developed to standardize the analysis of DICOM studies. The tool automatically identifies, extracts, and renames series using a consistent nomenclature. It also detects corrupted images and merges separated dynamic series that were initially split, allowing for streamlined post-processing. DISCUSSION AND CONCLUSIONS: The developed ePACS system encompasses a successful implementation, both in hospital and research environments, showcasing its transformative nature and the challenging yet crucial transfer of knowledge to industry. This underscores the practicality and real-world applicability of our innovative approach, highlighting the significant impact it has on the field of experimental radiology.

Optimizing Advanced Imaging of the Pediatric Patient in the Emergency Department: Policy Statement.

Advanced imaging, including ultrasonography, computed tomography, and magnetic resonance imaging (MRI), is an integral component to the evaluation and management of ill and injured children in the emergency department. As with any test or intervention, the benefits and potential impacts on management must be weighed against the risks to ensure that high-value care is being delivered. There are important considerations specific to the pediatric patient related to the ordering and interpretation of advanced imaging. This policy statement provides guidelines for institutions and those who care for children to optimize the use of advanced imaging in the emergency department setting and was coauthored by experts in pediatric and general emergency medicine, pediatric radiology, and pediatric surgery. The intent is to guide decision-making where children may access care.

Optimizing Advanced Imaging of the Pediatric Patient in the Emergency Department: Policy Statement.

Advanced imaging, including ultrasonography, computed tomography, and magnetic resonance imaging, is an integral component to the evaluation and management of ill and injured children in the emergency department. As with any test or intervention, the benefits and potential impacts on management must be weighed against the risks to ensure that high-value care is being delivered. There are important considerations specific to the pediatric patient related to the ordering and interpretation of advanced imaging. This policy statement provides guidelines for institutions and those who care for children to optimize the use of advanced imaging in the emergency department setting and was coauthored by experts in pediatric and general emergency medicine, pediatric radiology, and pediatric surgery. The intent is to guide decision-making where children may access care.

The Role of Imaging for Gastrointestinal Bleeding: Consensus Recommendations From the American College of Gastroenterology and Society of Abdominal Radiology.

Gastrointestinal (GI) bleeding is the most common GI diagnosis leading to hospitalization within the United States. Prompt diagnosis and treatment of GI bleeding is critical to improving patient outcomes and reducing high healthcare utilization and costs. Radiologic techniques including computed tomography angiography, catheter angiography, computed tomography enterography, magnetic resonance enterography, nuclear medicine red blood cell scan, and technetium-99m pertechnetate scintigraphy (Meckel scan) are frequently used to evaluate patients with GI bleeding and are complementary to GI endoscopy. However, multiple management guidelines exist which differ in the recommended utilization of these radiologic examinations. This variability can lead to confusion as to how these tests should be used in the evaluation of GI bleeding. In this document, a panel of experts from the American College of Gastroenterology and Society of Abdominal Radiology provide a review of the radiologic examinations used to evaluate for GI bleeding including nomenclature, technique, performance, advantages, and limitations. A comparison of advantages and limitations relative to endoscopic examinations is also included. Finally, consensus statements and recommendations on technical parameters and utilization of radiologic techniques for GI bleeding are provided.

Types of Evidence Needed to Assess the Clinical Value of Diagnostic Imaging.

AbstractThe evidence underlying the use of advanced diagnostic imaging is based mainly on diagnostic accuracy studies and not on well-designed trials demonstrating improved patient outcomes. This has led to an expansion of low-value and potentially harmful patient care and raises ethical issues around the widespread implementation of tests with incompletely known benefits and harms. Randomized clinical trials are needed to support the safety and effectiveness of imaging tests and should be required for clearance of most new technologies. Large, diverse cohort studies are needed to quantify disease risk associated with many imaging findings, especially incidental findings, to enable evidence-based management. The responsibility to minimize the use of tests with unknown or low value requires engagement of clinicians, medical societies, and the public.

ACR Appropriateness Criteria® Sepsis.

Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection. A search for the underlying cause of infection typically includes radiological imaging as part of this investigation. This document focuses on thoracic and abdominopelvic causes of sepsis. In 2017, the global incidence of sepsis was estimated to be 48.9 million cases, with 11 million sepsis-related deaths (accounting for nearly 20% of all global deaths); therefore, understanding which imaging modalities and types of studies are acceptable or not acceptable is imperative. The 5 variants provided include the most commonly encountered scenarios in the setting of sepsis along with recommendations and data for each imaging study. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

ACR Appropriateness Criteria® Workup of Pleural Effusion or Pleural Disease.

Pleural effusions are categorized as transudative or exudative, with transudative effusions usually reflecting the sequala of a systemic etiology and exudative effusions usually resulting from a process localized to the pleura. Common causes of transudative pleural effusions include congestive heart failure, cirrhosis, and renal failure, whereas exudative effusions are typically due to infection, malignancy, or autoimmune disorders. This document summarizes appropriateness guidelines for imaging in four common clinical scenarios in patients with known or suspected pleural effusion or pleural disease. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Optimizing Advanced Imaging of the Pediatric Patient in the Emergency Department: Policy Statement.

Advanced imaging, including ultrasonography, computed tomography, and magnetic resonance imaging (MRI), is an integral component to the evaluation and management of ill and injured children in the emergency department. As with any test or intervention, the benefits and potential impacts on management must be weighed against the risks to ensure that high-value care is being delivered. There are important considerations specific to the pediatric patient related to the ordering and interpretation of advanced imaging. This policy statement provides guidelines for institutions and those who care for children to optimize the use of advanced imaging in the emergency department setting and was coauthored by experts in pediatric and general emergency medicine, pediatric radiology, and pediatric surgery. The intent is to guide decision-making where children may access care.

Radiomics Beyond the Hype: A Critical Evaluation Toward Oncologic Clinical Use.

Radiomics is a promising and fast-developing field within oncology that involves the mining of quantitative high-dimensional data from medical images. Radiomics has the potential to transform cancer management, whereby radiomics data can be used to aid early tumor characterization, prognosis, risk stratification, treatment planning, treatment response assessment, and surveillance. Nevertheless, certain challenges have delayed the clinical adoption and acceptability of radiomics in routine clinical practice. The objectives of this report are to (a) provide a perspective on the translational potential and potential impact of radiomics in oncology; (b) explore frequent challenges and mistakes in its derivation, encompassing study design, technical requirements, standardization, model reproducibility, transparency, data sharing, privacy concerns, quality control, as well as the complexity of multistep processes resulting in less radiologist-friendly interfaces; (c) discuss strategies to overcome these challenges and mistakes; and (d) propose measures to increase the clinical use and acceptability of radiomics, taking into account the different perspectives of patients, health care workers, and health care systems. Keywords: Radiomics, Oncology, Cancer Management, Artificial Intelligence © RSNA, 2024.

Explanation and Elaboration with Examples for CLEAR (CLEAR-E3): an EuSoMII Radiomics Auditing Group Initiative.

Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, which can be accomplished with systematic use of reporting guidelines. The CheckList for EvaluAtion of Radiomics research (CLEAR) was previously developed to assist authors in reporting their radiomic research and to assist reviewers in their evaluation. To take full advantage of CLEAR, further explanation and elaboration of each item, as well as literature examples, may be useful. The main goal of this work, Explanation and Elaboration with Examples for CLEAR (CLEAR-E3), is to improve CLEAR's usability and dissemination. In this international collaborative effort, members of the European Society of Medical Imaging Informatics-Radiomics Auditing Group searched radiomics literature to identify representative reporting examples for each CLEAR item. At least two examples, demonstrating optimal reporting, were presented for each item. All examples were selected from open-access articles, allowing users to easily consult the corresponding full-text articles. In addition to these, each CLEAR item's explanation was further expanded and elaborated. For easier access, the resulting document is available at https://radiomic.github.io/CLEAR-E3/ . As a complementary effort to CLEAR, we anticipate that this initiative will assist authors in reporting their radiomics research with greater ease and transparency, as well as editors and reviewers in reviewing manuscripts.Relevance statement Along with the original CLEAR checklist, CLEAR-E3 is expected to provide a more in-depth understanding of the CLEAR items, as well as concrete examples for reporting and evaluating radiomic research.Key points• As a complementary effort to CLEAR, this international collaborative effort aims to assist authors in reporting their radiomics research, as well as editors and reviewers in reviewing radiomics manuscripts.• Based on positive examples from the literature selected by the EuSoMII Radiomics Auditing Group, each CLEAR item explanation was further elaborated in CLEAR-E3.• The resulting explanation and elaboration document with examples can be accessed at  https://radiomic.github.io/CLEAR-E3/ .

Reliability of a Musculoskeletal Imaging Competency Examination for Physical Therapists.

INTRODUCTION: The Burley Readiness Examination (BRE) for Musculoskeletal (MSK) Imaging Competency assesses physical therapists' baseline MSK imaging competency. Establishing its reliability is essential to its value in determining MSK imaging competency. The purpose of this study was to test the reliability of the BRE for MSK Imaging Competency among physical therapists (PTs) with varying levels of training and education. REVIEW OF LITERATURE: Previous literature supports PTs' utility concerning diagnostic imaging; however, no studies directly measure their competency. With PTs expanding their practice scope and professional PT education programs, increasing their MSK imaging instruction, assessing competency becomes strategic in determining the future of MSK education and training. SUBJECTS: One hundred twenty-three United States licensed PTs completed the BRE. METHODS: Physical therapists completed the BRE through an online survey platform. Point biserial correlation (rpb) was calculated for each examination question. Final analyses were based on 140 examination questions. Examination scores were compared using independent sample t-test and one-way analysis of variance. Chi-square tests and odds ratios (ORs) assessed the relationship of a passing examination score (≥75%) and the type of training. Reliability of the BRE was assessed using Cronbach's alpha (α). RESULTS: Mean overall examination score was 75.89 ± 8.56%. Seventy PTs (56.9%) obtained a passing score. Physical therapists with additional MSK imaging training, board certification, and residency or fellowship training scored significantly higher (P < .001) compared with those with only entry-level PT program education. Physical therapists with additional MSK imaging training scored significantly higher (x̄ = 81.07% ± 8.93%) and were almost 5 times (OR = 4.74, 95% CI [1.95-11.50]) as likely to achieve a passing score than those without. The BRE demonstrated strong internal consistency (Cronbach's α = 0.874). DISCUSSION AND CONCLUSIONS: The BRE was reliable, consistently identifying higher examination scores among those with increased MSK imaging training. Training in MSK imaging influenced competency more than other factors. The BRE may be of analytical value to PT professional and postprofessional programs.

All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems.

BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) models trained on multi-centric and multi-device studies can provide more robust insights and research findings compared to single-center studies. However, variability in acquisition protocols and equipment can introduce inconsistencies that hamper the effective pooling of multi-source datasets. This systematic review evaluates strategies for image harmonization, which standardizes appearances to enable reliable AI analysis of multi-source medical imaging. METHODS: A literature search using PRISMA guidelines was conducted to identify relevant papers published between 2013 and 2023 analyzing multi-centric and multi-device medical imaging studies that utilized image harmonization approaches. RESULTS: Common image harmonization techniques included grayscale normalization (improving classification accuracy by up to 24.42 %), resampling (increasing the percentage of robust radiomics features from 59.5 % to 89.25 %), and color normalization (enhancing AUC by up to 0.25 in external test sets). Initially, mathematical and statistical methods dominated, but machine and deep learning adoption has risen recently. Color imaging modalities like digital pathology and dermatology have remained prominent application areas, though harmonization efforts have expanded to diverse fields including radiology, nuclear medicine, and ultrasound imaging. In all the modalities covered by this review, image harmonization improved AI performance, with increasing of up to 24.42 % in classification accuracy and 47 % in segmentation Dice scores. CONCLUSIONS: Continued progress in image harmonization represents a promising strategy for advancing healthcare by enabling large-scale, reliable analysis of integrated multi-source datasets using AI. Standardizing imaging data across clinical settings can help realize personalized, evidence-based care supported by data-driven technologies while mitigating biases associated with specific populations or acquisition protocols.

Checklist for Reproducibility of Deep Learning in Medical Imaging.

The application of deep learning (DL) in medicine introduces transformative tools with the potential to enhance prognosis, diagnosis, and treatment planning. However, ensuring transparent documentation is essential for researchers to enhance reproducibility and refine techniques. Our study addresses the unique challenges presented by DL in medical imaging by developing a comprehensive checklist using the Delphi method to enhance reproducibility and reliability in this dynamic field. We compiled a preliminary checklist based on a comprehensive review of existing checklists and relevant literature. A panel of 11 experts in medical imaging and DL assessed these items using Likert scales, with two survey rounds to refine responses and gauge consensus. We also employed the content validity ratio with a cutoff of 0.59 to determine item face and content validity. Round 1 included a 27-item questionnaire, with 12 items demonstrating high consensus for face and content validity that were then left out of round 2. Round 2 involved refining the checklist, resulting in an additional 17 items. In the last round, 3 items were deemed non-essential or infeasible, while 2 newly suggested items received unanimous agreement for inclusion, resulting in a final 26-item DL model reporting checklist derived from the Delphi process. The 26-item checklist facilitates the reproducible reporting of DL tools and enables scientists to replicate the study's results.

Heterogeneity in Systematic Reviews of Medical Imaging Diagnostic Test Accuracy Studies: A Systematic Review.

Importance: Systematic reviews of medical imaging diagnostic test accuracy (DTA) studies are affected by between-study heterogeneity due to a range of factors. Failure to appropriately assess the extent and causes of heterogeneity compromises the interpretability of systematic review findings. Objective: To assess how heterogeneity has been examined in medical imaging DTA studies. Evidence Review: The PubMed database was searched for systematic reviews of medical imaging DTA studies that performed a meta-analysis. The search was limited to the 40 journals with highest impact factor in the radiology, nuclear medicine, and medical imaging category in the InCites Journal Citation Reports of 2021 to reach a sample size of 200 to 300 included studies. Descriptive analysis was performed to characterize the imaging modality, target condition, type of meta-analysis model used, strategies for evaluating heterogeneity, and sources of heterogeneity identified. Multivariable logistic regression was performed to assess whether any factors were associated with at least 1 source of heterogeneity being identified in the included meta-analyses. Methodological quality evaluation was not performed. Data analysis occurred from October to December 2022. Findings: A total of 242 meta-analyses involving a median (range) of 987 (119-441 510) patients across a diverse range of disease categories and imaging modalities were included. The extent of heterogeneity was adequately described (ie, whether it was absent, low, moderate, or high) in 220 studies (91%) and was most commonly assessed using the I2 statistic (185 studies [76%]) and forest plots (181 studies [75%]). Heterogeneity was rated as moderate to high in 191 studies (79%). Of all included meta-analyses, 122 (50%) performed subgroup analysis and 87 (36%) performed meta-regression. Of the 242 studies assessed, 189 (78%) included 10 or more primary studies. Of these 189 studies, 60 (32%) did not perform meta-regression or subgroup analysis. Reasons for being unable to investigate sources of heterogeneity included inadequate reporting of primary study characteristics and a low number of included primary studies. Use of meta-regression was associated with identification of at least 1 source of variability (odds ratio, 1.90; 95% CI, 1.11-3.23; P = .02). Conclusions and Relevance: In this systematic review of assessment of heterogeneity in medical imaging DTA meta-analyses, most meta-analyses were impacted by a moderate to high level of heterogeneity, presenting interpretive challenges. These findings suggest that, despite the development and availability of more rigorous statistical models, heterogeneity appeared to be incomplete, inconsistently evaluated, or methodologically questionable in many cases, which lessened the interpretability of the analyses performed; comprehensive heterogeneity assessment should be addressed at the author level by improving personal familiarity with appropriate statistical methodology for assessing heterogeneity and involving biostatisticians and epidemiologists in study design, as well as at the editorial level, by mandating adherence to methodologic standards in primary DTA studies and DTA meta-analyses.

Meta-research on reporting guidelines for artificial intelligence: are authors and reviewers encouraged enough in radiology, nuclear medicine, and medical imaging journals?

PURPOSE: To determine how radiology, nuclear medicine, and medical imaging journals encourage and mandate the use of reporting guidelines for artificial intelligence (AI) in their author and reviewer instructions. METHODS: The primary source of journal information and associated citation data used was the Journal Citation Reports (June 2023 release for 2022 citation data; Clarivate Analytics, UK). The first- and second-quartile journals indexed in the Science Citation Index Expanded and the Emerging Sources Citation Index were included. The author and reviewer instructions were evaluated by two independent readers, followed by an additional reader for consensus, with the assistance of automatic annotation. Encouragement and submission requirements were systematically analyzed. The reporting guidelines were grouped as AI-specific, related to modeling, and unrelated to modeling. RESULTS: Out of 102 journals, 98 were included in this study, and all of them had author instructions. Only five journals (5%) encouraged the authors to follow AI-specific reporting guidelines. Among these, three required a filled-out checklist. Reviewer instructions were found in 16 journals (16%), among which one journal (6%) encouraged the reviewers to follow AI-specific reporting guidelines without submission requirements. The proportions of author and reviewer encouragement for AI-specific reporting guidelines were statistically significantly lower compared with those for other types of guidelines (P < 0.05 for all). CONCLUSION: The findings indicate that AI-specific guidelines are not commonly encouraged and mandated (i.e., requiring a filled-out checklist) by these journals, compared with guidelines related to modeling and unrelated to modeling, leaving vast space for improvement. This meta-research study hopes to contribute to the awareness of the imaging community for AI reporting guidelines and ignite large-scale group efforts by all stakeholders, making AI research less wasteful. CLINICAL SIGNIFICANCE: This meta-research highlights the need for improved encouragement of AI-specific guidelines in radiology, nuclear medicine, and medical imaging journals. This can potentially foster greater awareness among the AI community and motivate various stakeholders to collaborate to promote more efficient and responsible AI research reporting practices.