Operational relevance of the Sydney Declaration: The example of the Australian Federal Police (AFP) Forensics Command.

In 2022, a group of eminent forensic scientists published The Sydney Declaration - Revisiting the essence of forensic science through its fundamental principles in Forensic Science International. The Sydney Declaration was delivered to revisit "the essence of forensic science, its purpose, and fundamental principles". At its heart, revisiting these foundational principles is hoped to "benefit forensic science as a whole to be more relevant, effective and reliable". But can these principles be translated operationally by a forensic services provider to achieve the benefits prescribed? How do we make the leap from a theoretical concept and begin to put it into practice to bring about the real and meaningful change that the declaration hopes to achieve? In this paper we will attempt to discuss how the Australian Federal Police (AFP) Forensics Command has reflected on the Sydney Declaration by relating reforms developed and implemented to our operating model with some selected principles. We hope to show that while the Sydney Declaration could be perceived as academic and disconnected from operations, it has the potential to impact and positively influence reforms and changes for forensic science providers. The AFP Forensics Command experience shows the operational relevance of The Sydney Declaration.

Progressive Pulmonary Fibrosis and Interstitial Lung Abnormalities: AJR Expert Panel Narrative Review.

Progressive pulmonary fibrosis (PPF) and interstitial lung abnormalities (ILA) are relatively new concepts in interstitial lung disease (ILD) imaging and clinical management. Recognition of signs of PPF, as well as identification and classification of ILA, are important tasks during chest high-resolution CT interpretation, to optimize management of patients with ILD and those at risk of developing ILD. However, following professional society guidance, the role of imaging surveillance remains unclear in stable patients with ILD, asymptomatic patients with ILA who are at risk of progression, and asymptomatic patients at risk of developing ILD without imaging abnormalities. In this AJR Expert Panel Narrative Review, we summarize the current knowledge regarding PPF and ILA and describe the range of clinical practice with respect to imaging patients with ILD, those with ILA, and those at risk of developing ILD. In addition, we offer suggestions to help guide surveillance imaging in areas with an absence of published guidelines, where such decisions are currently driven primarily by local pulmonologists' preference.

Towards the adoption of quantitative computed tomography in the management of interstitial lung disease.

The shortcomings of qualitative visual assessment have led to the development of computer-based tools to characterise and quantify disease on high-resolution computed tomography (HRCT) in patients with interstitial lung diseases (ILDs). Quantitative CT (QCT) software enables quantification of patterns on HRCT with results that are objective, reproducible, sensitive to change and predictive of disease progression. Applications developed to provide a diagnosis or pattern classification are mainly based on artificial intelligence. Deep learning, which identifies patterns in high-dimensional data and maps them to segmentations or outcomes, can be used to identify the imaging patterns that most accurately predict disease progression. Optimisation of QCT software will require the implementation of protocol standards to generate data of sufficient quality for use in computerised applications and the identification of diagnostic, imaging and physiological features that are robustly associated with mortality for use as anchors in the development of algorithms. Consortia such as the Open Source Imaging Consortium have a key role to play in the collation of imaging and clinical data that can be used to identify digital imaging biomarkers that inform diagnosis, prognosis and response to therapy.

Post-COVID highlights: Challenges and solutions of artificial intelligence techniques for swift identification of COVID-19.

Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to develop cost-effective, non-invasive, and rapid AI-based tools. These tools were intended to alleviate the burden on healthcare systems, control the rapid spread of the virus, and enhance intervention outcomes, all in response to this unprecedented global crisis. As we transition into a post-COVID era, we retrospectively evaluate these proposed studies and offer a review of the techniques employed in AI diagnostic models, with a focus on the solutions proposed for different challenges. This review endeavors to provide insights into the diverse solutions designed to address the multifaceted challenges that arose during the pandemic. By doing so, we aim to prepare the AI community for the development of AI tools tailored to address public health emergencies effectively.

Zinpentraxin Alfa for Idiopathic Pulmonary Fibrosis: The Randomized Phase III STARSCAPE Trial.

Rationale: A phase II trial reported clinical benefit over 28 weeks in patients with idiopathic pulmonary fibrosis (IPF) who received zinpentraxin alfa. Objectives: To investigate the efficacy and safety of zinpentraxin alfa in patients with IPF in a phase III trial. Methods: This 52-week phase III, double-blind, placebo-controlled, pivotal trial was conducted at 275 sites in 29 countries. Patients with IPF were randomized 1:1 to intravenous placebo or zinpentraxin alfa 10 mg/kg every 4 weeks. The primary endpoint was absolute change from baseline to Week 52 in FVC. Secondary endpoints included absolute change from baseline to Week 52 in percent predicted FVC and 6-minute walk distance. Safety was monitored via adverse events. Post hoc analysis of the phase II and phase III data explored changes in FVC and their impact on the efficacy results. Measurements and Main Results: Of 664 randomized patients, 333 were assigned to placebo and 331 to zinpentraxin alfa. Four of the 664 randomized patients were never administered study drug. The trial was terminated early after a prespecified futility analysis that demonstrated no treatment benefit of zinpentraxin alfa over placebo. In the final analysis, absolute change from baseline to Week 52 in FVC was similar between placebo and zinpentraxin alfa (-214.89 ml and -235.72 ml; P = 0.5420); there were no apparent treatment effects on secondary endpoints. Overall, 72.3% and 74.6% of patients receiving placebo and zinpentraxin alfa, respectively, experienced one or more adverse events. Post hoc analysis revealed that extreme FVC decline in two placebo-treated patients resulted in the clinical benefit of zinpentraxin alfa reported by phase II. Conclusions: Zinpentraxin alfa treatment did not benefit patients with IPF over placebo. Learnings from this program may help improve decision making around trials in IPF. Clinical trial registered with www.clinicaltrials.gov (NCT04552899).

High-resolution CT phenotypes in pulmonary sarcoidosis: a multinational Delphi consensus study.

One view of sarcoidosis is that the term covers many different diseases. However, no classification framework exists for the future exploration of pathogenetic pathways, genetic or trigger predilections, patterns of lung function impairment, or treatment separations, or for the development of diagnostic algorithms or relevant outcome measures. We aimed to establish agreement on high-resolution CT (HRCT) phenotypic separations in sarcoidosis to anchor future CT research through a multinational two-round Delphi consensus process. Delphi participants included members of the Fleischner Society and the World Association of Sarcoidosis and other Granulomatous Disorders, as well as members' nominees. 146 individuals (98 chest physicians, 48 thoracic radiologists) from 28 countries took part, 144 of whom completed both Delphi rounds. After rating of 35 Delphi statements on a five-point Likert scale, consensus was achieved for 22 (63%) statements. There was 97% agreement on the existence of distinct HRCT phenotypes, with seven HRCT phenotypes that were categorised by participants as non-fibrotic or likely to be fibrotic. The international consensus reached in this Delphi exercise justifies the formulation of a CT classification as a basis for the possible definition of separate diseases. Further refinement of phenotypes with rapidly achievable CT studies is now needed to underpin the development of a formal classification of sarcoidosis.

The everolimus eluting Synergy MegatronTM drug-eluting stent platform: Early outcomes from the European Synergy MegatronTM Implanters' Registry.

BACKGROUND: The Synergy MegatronTM is an everolimus-drug eluting stent that may offer advantages in the treatment of aorto-ostial disease and large proximal vessels. AIMS: To report the short- to medium-term clinical outcomes from the European Synergy MegatronTM Implanters' Registry. METHODS: This registry was an investigator-initiated study conducted at 14 European centers. The primary outcome was target lesion failure (TLF), defined as the composite of cardiovascular death, target vessel myocardial infarction (MI), and target lesion revascularisation. RESULTS: Five hundred seventy-five patients underwent PCI with MegatronTM between 2019 and 2021. Patients were 69 ± 12 years old, 26% had diabetes mellitus, 24% had moderate-severe left ventricular impairment and 59% presented with an acute coronary syndrome. 15% were deemed prohibitively high risk for surgical revascularisation. The target vessel involved the left main stem in 55%, the ostium of the RCA in 13% and was a true bifurcation (Medina 1,1,1) in 50%.  At 1 year, TLF was observed in 40 patients, with 26 (65%) occurring within the first 30 days. The cumulative incidence of TLF was 4.5% at 30 days and 8.6% (95% CI 6.3-11.7) at 1 year. The incidence of stent thrombosis was 0.5% with no late stent thromboses. By multivariate analysis, the strongest independent predictors of TLF were severe left ventricular impairment (HR 3.43, 95% CI: 1.67-6.76, p < 0.001) and a target vessel involving the left main (HR 4.00 95% CI 1.81-10.15 p = 0.001). CONCLUSIONS: Use of the Synergy MegatronTM everolimus eluting stent in a 'real-world' setting shows favorable outcomes at 30 days and 1 year.

Imaging Features of Autoimmune Disease-Related Interstitial Lung Diseases.

Interstitial lung diseases (ILDs) associated with autoimmune diseases show characteristic signs of imaging. Radiologic signs are also used in the identification of ILDs with features suggestive of autoimmune disease that do not meet the criteria for a specific autoimmune disease. Radiologists play a key role in identifying these signs and assessing their relevance as part of multidisciplinary team discussions. A radiologist may be the first health care professional to pick up signs of autoimmune disease in a patient referred for assessment of ILD or with suspicion for ILD. Multidisciplinary team discussion of imaging findings observed during follow-up may inform a change in diagnosis or identify progression, with implications for a patient's treatment regimen. This article describes the imaging features of autoimmune disease-related ILDs and the role of radiologists in assessing their relevance.

Multi-site, Multi-domain Airway Tree Modeling.

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to the quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and extensive clinical efforts for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Both quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage (https://atm22.grand-challenge.org/).

Combination of BAL and Computed Tomography Differentiates Progressive and Non-progressive Fibrotic Lung Diseases.

Rationale: Identifying patients with pulmonary fibrosis (PF) at risk of progression can guide management. Objectives: To explore the utility of combining baseline BAL and computed tomography (CT) in differentiating progressive and nonprogressive PF. Methods: The derivation cohort consisted of incident cases of PF for which BAL was performed as part of a diagnostic workup. A validation cohort was prospectively recruited with identical inclusion criteria. Baseline thoracic CT scans were scored for the extent of fibrosis and usual interstitial pneumonia (UIP) pattern. The BAL lymphocyte proportion was recorded. Annualized FVC decrease of >10% or death within 1 year was used to define disease progression. Multivariable logistic regression identified the determinants of the outcome. The optimum binary thresholds (maximal Wilcoxon rank statistic) at which the extent of fibrosis on CT and the BAL lymphocyte proportion could distinguish disease progression were identified. Measurements and Main Results: BAL lymphocyte proportion, UIP pattern, and fibrosis extent were significantly and independently associated with disease progression in the derivation cohort (n = 240). Binary thresholds for increased BAL lymphocyte proportion and extensive fibrosis were identified as 25% and 20%, respectively. An increased BAL lymphocyte proportion was rare in patients with a UIP pattern (8 of 135; 5.9%) or with extensive fibrosis (7 of 144; 4.9%). In the validation cohort (n = 290), an increased BAL lymphocyte proportion was associated with a significantly lower probability of disease progression in patients with nonextensive fibrosis or a non-UIP pattern. Conclusions: BAL lymphocytosis is rare in patients with extensive fibrosis or a UIP pattern on CT. In patients without a UIP pattern or with limited fibrosis, a BAL lymphocyte proportion of ⩾25% was associated with a lower likelihood of progression.

Arrhythmia and Death Following Percutaneous Revascularization in Ischemic Left Ventricular Dysfunction: Prespecified Analyses From the REVIVED-BCIS2 Trial.

BACKGROUND: Ventricular arrhythmia is an important cause of mortality in patients with ischemic left ventricular dysfunction. Revascularization with coronary artery bypass graft or percutaneous coronary intervention is often recommended for these patients before implantation of a cardiac defibrillator because it is assumed that this may reduce the incidence of fatal and potentially fatal ventricular arrhythmias, although this premise has not been evaluated in a randomized trial to date. METHODS: Patients with severe left ventricular dysfunction, extensive coronary disease, and viable myocardium were randomly assigned to receive either percutaneous coronary intervention (PCI) plus optimal medical and device therapy (OMT) or OMT alone. The composite primary outcome was all-cause death or aborted sudden death (defined as an appropriate implantable cardioverter defibrillator therapy or a resuscitated cardiac arrest) at a minimum of 24 months, analyzed as time to first event on an intention-to-treat basis. Secondary outcomes included cardiovascular death or aborted sudden death, appropriate implantable cardioverter defibrillator (ICD) therapy or sustained ventricular arrhythmia, and number of appropriate ICD therapies. RESULTS: Between August 28, 2013, and March 19, 2020, 700 patients were enrolled across 40 centers in the United Kingdom. A total of 347 patients were assigned to the PCI+OMT group and 353 to the OMT alone group. The mean age of participants was 69 years; 88% were male; 56% had hypertension; 41% had diabetes; and 53% had a clinical history of myocardial infarction. The median left ventricular ejection fraction was 28%; 53.1% had an implantable defibrillator inserted before randomization or during follow-up. All-cause death or aborted sudden death occurred in 144 patients (41.6%) in the PCI group and 142 patients (40.2%) in the OMT group (hazard ratio, 1.03 [95% CI, 0.82-1.30]; P=0.80). There was no between-group difference in the occurrence of any of the secondary outcomes. CONCLUSIONS: PCI was not associated with a reduction in all-cause mortality or aborted sudden death. In patients with ischemic cardiomyopathy, PCI is not beneficial solely for the purpose of reducing potentially fatal ventricular arrhythmias. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT01920048.

Exploring computer-based imaging analysis in interstitial lung disease: opportunities and challenges.

The advent of quantitative computed tomography (QCT) and artificial intelligence (AI) using high-resolution computed tomography data has revolutionised the way interstitial diseases are studied. These quantitative methods provide more accurate and precise results compared to prior semiquantitative methods, which were limited by human error such as interobserver disagreement or low reproducibility. The integration of QCT and AI and the development of digital biomarkers has facilitated not only diagnosis but also prognostication and prediction of disease behaviour, not just in idiopathic pulmonary fibrosis in which they were initially studied, but also in other fibrotic lung diseases. These tools provide reproducible, objective prognostic information which may facilitate clinical decision-making. However, despite the benefits of QCT and AI, there are still obstacles that need to be addressed. Important issues include optimal data management, data sharing and maintenance of data privacy. In addition, the development of explainable AI will be essential to develop trust within the medical community and facilitate implementation in routine clinical practice.

Adversarial Transformer for Repairing Human Airway Segmentation.

Automated airway segmentation models often suffer from discontinuities in peripheral bronchioles, which limits their clinical applicability. Furthermore, data heterogeneity across different centres and pathological abnormalities pose significant challenges to achieving accurate and robust segmentation in distal small airways. Accurate segmentation of airway structures is essential for the diagnosis and prognosis of lung diseases. To address these issues, we propose a patch-scale adversarial-based refinement network that takes in preliminary segmentation and original CT images and outputs a refined mask of the airway structure. Our method is validated on three datasets, including healthy cases, pulmonary fibrosis, and COVID-19 cases, and quantitatively evaluated using seven metrics. Our method achieves more than a 15% increase in the detected length ratio and detected branch ratio compared to previously proposed models, demonstrating its promising performance. The visual results show that our refinement approach, guided by a patch-scale discriminator and centreline objective functions, effectively detects discontinuities and missing bronchioles. We also demonstrate the generalizability of our refinement pipeline on three previous models, significantly improving their segmentation completeness. Our method provides a robust and accurate airway segmentation tool that can help improve diagnosis and treatment planning for lung diseases.

Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation.

Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions, especially for cohorts with different lung diseases. The attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This article presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network (FANN) and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization, and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19, and pulmonary fibrosis.

Less Is More: Unsupervised Mask-Guided Annotated CT Image Synthesis With Minimum Manual Segmentations.

As a pragmatic data augmentation tool, data synthesis has generally returned dividends in performance for deep learning based medical image analysis. However, generating corresponding segmentation masks for synthetic medical images is laborious and subjective. To obtain paired synthetic medical images and segmentations, conditional generative models that use segmentation masks as synthesis conditions were proposed. However, these segmentation mask-conditioned generative models still relied on large, varied, and labeled training datasets, and they could only provide limited constraints on human anatomical structures, leading to unrealistic image features. Moreover, the invariant pixel-level conditions could reduce the variety of synthetic lesions and thus reduce the efficacy of data augmentation. To address these issues, in this work, we propose a novel strategy for medical image synthesis, namely Unsupervised Mask (UM)-guided synthesis, to obtain both synthetic images and segmentations using limited manual segmentation labels. We first develop a superpixel based algorithm to generate unsupervised structural guidance and then design a conditional generative model to synthesize images and annotations simultaneously from those unsupervised masks in a semi-supervised multi-task setting. In addition, we devise a multi-scale multi-task Fréchet Inception Distance (MM-FID) and multi-scale multi-task standard deviation (MM-STD) to harness both fidelity and variety evaluations of synthetic CT images. With multiple analyses on different scales, we could produce stable image quality measurements with high reproducibility. Compared with the segmentation mask guided synthesis, our UM-guided synthesis provided high-quality synthetic images with significantly higher fidelity, variety, and utility ( by Wilcoxon Signed Ranked test).