Displacement Tracking Techniques in Ultrasound Elastography: From Cross-Correlation to Deep Learning.

Ultrasound elastography is a non-invasive medical imaging technique that maps viscoelastic properties to characterize tissues and diseases. Elastography can be divided into two classes in a broad sense: strain elastography (SE), which relies on Hooke's law to delineate strain as a surrogate for elasticity, and shear-wave elastography (SWE), which tracks the propagation of shear waves in tissues to estimate the elasticity. As tracking the displacement field in the temporal or spatial domain is an inevitable step of both SE and SWE, the success is contingent on the displacement estimation accuracy. Recent reviews mostly focused on clinical applications of elastography, disregarding advances in displacement tracking algorithms. Herein, we comprehensively review the recently proposed displacement estimation algorithms applied to both SE and SWE. In addition to cross-correlation, block-matching (i.e., window-based), model-based, energy-based, and deep learning-based tracking techniques, we review large and lateral displacement tracking, adaptive beamforming, data enhancement, and noise-suppression algorithms facilitating better displacement estimation. We also discuss the simulation models for displacement tracking validation, clinical translation and validation of displacement tracking methods, performance evaluation metrics, and publicly available codes and data for displacement tracking in elastography. Finally, we provide experiential opinions on different tracking algorithms, list the limitations of the current state of elastographic tracking, and comment on possible future research.

A responsible framework for applying artificial intelligence on medical images and signals at the point-of-care: the PACS-AI platform.

The potential of artificial intelligence (AI) in medicine lies in its ability to enhance clinicians' capacity to analyze medical images, thereby improving diagnostic precision and accuracy, thus enhancing current tests. However, the integration of AI within healthcare is fraught with difficulties. Heterogeneity among healthcare system applications, reliance on proprietary closed-source software, and rising cyber-security threats pose significant challenges. Moreover, prior to their deployment in clinical settings, AI models must demonstrate their effectiveness across a wide range of scenarios and must be validated by prospective studies, but doing so requires testing in an environment mirroring the clinical workflow which is difficult to achieve without dedicated software. Finally, the use of AI techniques in healthcare raises significant legal and ethical issues, such as the protection of patient privacy, the prevention of bias, and the monitoring of the device's safety and effectiveness for regulatory compliance. This review describes challenges to AI integration in healthcare and provides guidelines on how to move forward. We describe an open-source solution that we developed which integrates AI models into the Picture Archives Communication System (PACS), called PACS-AI. This approach aims to increase the evaluation of AI models by facilitating their integration and validation with existing medical imaging databases. PACS-AI may overcome many current barriers to AI deployment and offers a pathway towards responsible, fair, and effective deployment of AI models in healthcare. Additionally, we propose a list of criteria and guidelines that AI researchers should adopt when publishing a medical AI model, to enhance standardization and reproducibility.

Robust and Durable Photonic Crystal with Liquid-Repellent Property for Self-Cleaning Coatings and Structural Colored Textiles.

Photonic crystal coatings with unique structural colors and self-cleaning properties have been providing an efficient way for substrate coloration. However, the enhancement of the robustness and durability of structural colored coatings to meet the requirements in diverse environments remains a challenging task. Here, to realize the application of photonic crystal films under various kinds of conditions, we present a poly(fluoroalkyl acrylate)-based colloidal photonic crystal (fCPC) coating. Fluorinated core-interlayer-shell (FCIS) colloidal particles of polystyrene (PS) core, poly(methyl methacrylate) (PMMA) interlayer, and poly(fluoroalkyl acrylate-ethyl acrylate-butyl acrylate) (P(FA-EA-BA)) shell copolymers have been first prepared by a stepwise emulsion polymerization. fCPCs with self-supporting property, reprocessing ability, friction resistance, as well as excellent wettability and liquid-repellent properties are successfully obtained via the bending-induced ordering technique (BIOT). When applied in antifouling applications, the fCPC film exhibits resistance against various oil and inorganic liquids. Furthermore, the fCPC coatings demonstrate their durability under outdoor conditions by maintaining stable color appearances during rainy and sunny conditions. Additionally, an electronic product adhered with the fCPC coatings is presented, which exhibits a surface that remains clean even after prolonged usage in comparison to the conventional CPC coating. Structural colored textiles with enhanced stability and functionalized liquid-repellent properties are achieved through a one-step process using FCIS particles. Therefore, the developed self-cleaning and comprehensive fCPC coatings capable of withstanding diverse conditions may open up new avenues for the advancement of structural coloration in decoration, vehicle, textile, and building.

Simulating federated learning for steatosis detection using ultrasound images.

We aimed to implement four data partitioning strategies evaluated with four federated learning (FL) algorithms and investigate the impact of data distribution on FL model performance in detecting steatosis using B-mode US images. A private dataset (153 patients; 1530 images) and a public dataset (55 patient; 550 images) were included in this retrospective study. The datasets contained patients with metabolic dysfunction-associated fatty liver disease (MAFLD) with biopsy-proven steatosis grades and control individuals without steatosis. We employed four data partitioning strategies to simulate FL scenarios and we assessed four FL algorithms. We investigated the impact of class imbalance and the mismatch between the global and local data distributions on the learning outcome. Classification performance was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. AUCs were 0.93 (95% CI 0.92, 0.94) for source-based partitioning scenario with FedAvg, 0.90 (95% CI 0.89, 0.91) for a centralized model, and 0.83 (95% CI 0.81, 0.85) for a model trained in a single-center scenario. When data was perfectly balanced on the global level and each site had an identical data distribution, the model yielded an AUC of 0.90 (95% CI 0.88, 0.92). When each site contained data exclusively from one single class, irrespective of the global data distribution, the AUC fell in the range of 0.34-0.70. FL applied to B-mode US images provide performance comparable to a centralized model and higher than single-center scenario. Global data imbalance and local data heterogeneity influenced the learning outcome.

Changes in the Biology and Susceptibility of Weevil (Cylas formicarius) to the Insecticide Spinetoram as a Response to Cadmium Contamination.

The sweet potato weevil Cylas formicarius is a notorious underground pest in sweet potato (Ipomoea batatas L.). However, little is known about the effects of cadmium (Cd) stress on weevil biology and resistance to pesticides and biotic agents. Therefore, we fed sweet potato weevils with Cd-contaminated sweet potato and assessed adult food intake and survival and larval developmental duration and mortality rates, as well as resistance to the insecticide spinetoram and susceptibility to the entomopathogenic fungus Beauveria bassiana. With increasing Cd concentration, the number of adult weevil feeding holes, adult survival and life span, and larval developmental duration decreased significantly, whereas larval mortality rates increased significantly. However, at the lowest Cd concentration (30 mg/L), adult feeding was stimulated. Resistance of adult sweet potato weevils to spinetoram increased at low Cd concentration, whereas Cd contamination did not affect sensitivity to B. bassiana. Thus, Cd contamination affected sweet potato weevil biology and resistance, and further studies will investigate weevil Cd accumulation and detoxification mechanisms.

Na3V2(PO4)3-decorated Na3V2(PO4)2F3 as a high-rate and cycle-stable cathode material for sodium ion batteries.

Since Na3V2(PO4)3 (NVP) possesses modest volume deformation and three-dimensional ion diffusion channels, it is a potential sodium-ion battery cathode material that has been extensively researched. Nonetheless, NVP still endures the consequences of poor electronic conductivity and low voltage platforms, which need to be further improved. On this basis, a high voltage platform Na3V2(PO4)2F3 was introduced to form a composite with NVP to increase the energy density. In this study, the sol-gel technique was successfully used to synthesize a Na3V2(PO4)2.75F0.75/C (NVPF·3NVP/C) composite cathode material. The citric acid-derived carbon layer was utilized to construct three-dimensional conducting networks to effectively promote ion and electron diffusion. Furthermore, the composites' synergistic effect accelerates the quick ionic migration and improves the kinetic reaction. In particular, NVP as the dominant phase enhanced the structural stability and significantly increased the capacitive contribution. Therefore, at 0.1C, the discharge capacity of the modified NVPF·3NVP/C composite is 120.7 mA h g-1, which is greater than the theoretical discharge capacity of pure NVP (118 mA h g-1). It discharged 110.9 mA h g-1 of reversible capacity even at an elevated multiplicity of 10C, and after 200 cycles, it retained 64.1% of its capacity. Thus, the effort produced an optimized NVPF·3NVP/C composite cathode material that may be used in the sodium ion cathode.

Serum electrolyte concentrations and risk of atrial fibrillation: an observational and mendelian randomization study.

BACKGROUND: Atrial fibrillation (AF) is a prevalent arrhythmic condition resulting in increased stroke risk and is associated with high mortality. Electrolyte imbalance can increase the risk of AF, where the relationship between AF and serum electrolytes remains unclear. METHODS: A total of 15,792 individuals were included in the observational study, with incident AF ascertainment in the Atherosclerosis Risk in Communities (ARIC) study. The Cox regression models were applied to calculate the hazard ratio (HR) and 95% confidence interval (CI) for AF based on different serum electrolyte levels. Mendelian randomization (MR) analyses were performed to examine the causal association. RESULTS: In observational study, after a median 19.7 years of follow-up, a total of 2551 developed AF. After full adjustment, participants with serum potassium below the 5th percentile had a higher risk of AF relative to participants in the middle quintile. Serum magnesium was also inversely associated with the risk of AF. An increased incidence of AF was identified in individuals with higher serum phosphate percentiles. Serum calcium levels were not related to AF risk. Moreover, MR analysis indicated that genetically predicted serum electrolyte levels were not causally associated with AF risk. The odds ratio for AF were 0.999 for potassium, 1.044 for magnesium, 0.728 for phosphate, and 0.979 for calcium, respectively. CONCLUSIONS: Serum electrolyte disorders such as hypokalemia, hypomagnesemia and hyperphosphatemia were associated with an increased risk of AF and may also serve to be prognostic factors. However, the present study did not support serum electrolytes as causal mediators for AF development.

Interpretation, Reporting, and Clinical Applications of Liver MR Elastography.

Chronic liver disease is highly prevalent and often leads to fibrosis or cirrhosis and complications such as liver failure and hepatocellular carcinoma. The diagnosis and staging of liver fibrosis is crucial to determine management and mitigate complications. Liver biopsy for histologic assessment has limitations such as sampling bias and high interreader variability that reduce precision, which is particularly challenging in longitudinal monitoring. MR elastography (MRE) is considered the most accurate noninvasive technique for diagnosing and staging liver fibrosis. In MRE, low-frequency vibrations are applied to the abdomen, and the propagation of shear waves through the liver is analyzed to measure liver stiffness, a biomarker for the detection and staging of liver fibrosis. As MRE has become more widely used in clinical care and research, different contexts of use have emerged. This review focuses on the latest developments in the use of MRE for the assessment of liver fibrosis; provides guidance for image acquisition and interpretation; summarizes diagnostic performance, along with thresholds for diagnosis and staging of liver fibrosis; discusses current and emerging clinical applications; and describes the latest technical developments.

Risk Factors for Hospitalization Duration Longer Than 24 Hours Following Percutaneous Radiofrequency Ablation of Liver Tumours.

PURPOSE: Prior studies have described complications of radiofrequency ablation (RFA) of liver tumours. The aim of this study was to identify risk factors for hospitalization duration longer than 24 hours following RFA of liver tumours. METHODS: This retrospective, single-centre study included patients with liver tumours undergoing RFA between October 2017 and July 2020. Medical records were reviewed to collect patient, tumours, and procedure characteristics for each RFA session. The association between potential risk factors and duration of hospitalization (less than or more than 24 hours) was analyzed using univariate and multivariate logistic regressions. RESULTS: Our study included 291 patients (mean age: 65.2 ± 11.2 [standard deviation]; 201 men) undergoing 324 RFA sessions. Sixty-eight sessions (21.0%) resulted in hospitalization of more than 24 hours. Multivariate analysis identified each additional needle insertion per session (OR 1.4; 95% CI [1.1-1.9]; P = .02), RFA performed in segment V (OR 2.8; 95% CI [1.4-5.7]; P = .004), and use of artificial pneumothorax (OR 14.5; 95% CI [1.4-146.0]; P = .02) as potential risk factors. A history of hepatic encephalopathy (OR 2.6; 95% CI [1.1-6.0]; P = .03) was only significant in univariate analysis. Post-hoc, subgroup analysis of patients with hepatocellular carcinoma (69.8%) did not identify other risk factors. CONCLUSION: Risk factors for a hospitalization duration longer than 24 hours include a higher number of needle insertions per session, radiofrequency ablation in segment V, and use of an artificial pneumothorax.

Human-scale navigation of magnetic microrobots in hepatic arteries.

Using external actuation sources to navigate untethered drug-eluting microrobots in the bloodstream offers great promise in improving the selectivity of drug delivery, especially in oncology, but the current field forces are difficult to maintain with enough strength inside the human body (>70-centimeter-diameter range) to achieve this operation. Here, we present an algorithm to predict the optimal patient position with respect to gravity during endovascular microrobot navigation. Magnetic resonance navigation, using magnetic field gradients in clinical magnetic resonance imaging (MRI), is combined with the algorithm to improve the targeting efficiency of magnetic microrobots (MMRs). Using a dedicated microparticle injector, a high-precision MRI-compatible balloon inflation system, and a clinical MRI, MMRs were successfully steered into targeted lobes via the hepatic arteries of living pigs. The distribution ratio of the microrobots (roughly 2000 MMRs per pig) in the right liver lobe increased from 47.7 to 86.4% and increased in the left lobe from 52.2 to 84.1%. After passing through multiple vascular bifurcations, the number of MMRs reaching four different target liver lobes had a 1.7- to 2.6-fold increase in the navigation groups compared with the control group. Performing simulations on 19 patients with hepatocellular carcinoma (HCC) demonstrated that the proposed technique can meet the need for hepatic embolization in patients with HCC. Our technology offers selectable direction for actuator-based navigation of microrobots at the human scale.

LI-RADS CT and MRI Ancillary Feature Association with Hepatocellular Carcinoma and Malignancy: An Individual Participant Data Meta-Analysis.

Background The independent contribution of each Liver Imaging Reporting and Data System (LI-RADS) CT or MRI ancillary feature (AF) has not been established. Purpose To evaluate the association of LI-RADS AFs with hepatocellular carcinoma (HCC) and malignancy while adjusting for LI-RADS major features through an individual participant data (IPD) meta-analysis. Materials and Methods Medline, Embase, Cochrane Central Register of Controlled Trials, and Scopus were searched from January 2014 to January 2022 for studies evaluating the diagnostic accuracy of CT and MRI for HCC using LI-RADS version 2014, 2017, or 2018. Using a one-step approach, IPD across studies were pooled. Adjusted odds ratios (ORs) and 95% CIs were derived from multivariable logistic regression models of each AF combined with major features except threshold growth (excluded because of infrequent reporting). Liver observation clustering was addressed at the study and participant levels through random intercepts. Risk of bias was assessed using a composite reference standard and Quality Assessment of Diagnostic Accuracy Studies 2. Results Twenty studies comprising 3091 observations (2456 adult participants; mean age, 59 years ± 11 [SD]; 1849 [75.3%] men) were included. In total, 89% (eight of nine) of AFs favoring malignancy were associated with malignancy and/or HCC, 80% (four of five) of AFs favoring HCC were associated with HCC, and 57% (four of seven) of AFs favoring benignity were negatively associated with HCC and/or malignancy. Nonenhancing capsule (OR = 3.50 [95% CI: 1.53, 8.01]) had the strongest association with HCC. Diffusion restriction (OR = 14.45 [95% CI: 9.82, 21.27]) and mild-moderate T2 hyperintensity (OR = 10.18 [95% CI: 7.17, 14.44]) had the strongest association with malignancy. The strongest negative associations with HCC were parallels blood pool enhancement (OR = 0.07 [95% CI: 0.01, 0.49]) and marked T2 hyperintensity (OR = 0.18 [95% CI: 0.07, 0.45]). Seventeen studies (85%) had a high risk of bias. Conclusion Most LI-RADS AFs were independently associated with HCC, malignancy, or benignity as intended when adjusting for major features. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Crivellaro in this issue.

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever­growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi­society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.

Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.Key points • The incorporation of artificial intelligence (AI) in radiological practice demands increased monitoring of its utility and safety.• Cooperation between developers, clinicians, and regulators will allow all involved to address ethical issues and monitor AI performance.• AI can fulfil its promise to advance patient well-being if all steps from development to integration in healthcare are rigorously evaluated.

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA.

Artificial intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. KEY POINTS.

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. This article is simultaneously published in Insights into Imaging (DOI 10.1186/s13244-023-01541-3), Journal of Medical Imaging and Radiation Oncology (DOI 10.1111/1754-9485.13612), Canadian Association of Radiologists Journal (DOI 10.1177/08465371231222229), Journal of the American College of Radiology (DOI 10.1016/j.jacr.2023.12.005), and Radiology: Artificial Intelligence (DOI 10.1148/ryai.230513). Keywords: Artificial Intelligence, Radiology, Automation, Machine Learning Published under a CC BY 4.0 license. ©The Author(s) 2024. Editor's Note: The RSNA Board of Directors has endorsed this article. It has not undergone review or editing by this journal.

Mycobacterium vaccae alleviates allergic airway inflammation and airway hyper-responsiveness in asthmatic mice by altering intestinal microbiota.

Host immunity can influence the composition of the gut microbiota and consequently affect disease progression. Previously, we reported that a Mycobacterium vaccae vaccine could ameliorate allergic inflammation in asthmatic mice by regulating inflammatory immune processes. Here, we investigated the anti-inflammatory effects of M. vaccae on allergic asthma via gut microbiota modulation. An ovalbumin (OVA)-induced asthmatic murine model was established and treated with M. vaccae. Gut microbiota profiles were determined in 18 BALB/c mice using 16S rDNA gene sequencing and metabolomic profiling was performed using liquid chromatography quadrupole time-of-flight mass spectrometry. Mycobacterium vaccae alleviated airway hyper-reactivity and inflammatory infiltration in mice with OVA-induced allergic asthma. The microbiota of asthmatic mice is disrupted and that this can be reversed with M. vaccae. Additionally, a total of 24 differential metabolites were screened, and the abundance of PI(14:1(9Z)/18:0), a glycerophospholipid, was found to be correlated with macrophage numbers (r = 0.52, p = 0.039). These metabolites may affect chemokine (such as macrophage chemoattractant protein-1) concentrations in the serum, and ultimately affect pulmonary macrophage recruitment. Our data demonstrated that M. vaccae might alleviate airway inflammation and hyper-responsiveness in asthmatic mice by reversing imbalances in gut microbiota. These novel mechanistic insights are expected to pave the way for novel asthma therapeutic strategies.

Always positive covalent organic nanosheet enabling pH-independent adsorption and removal of Cr(â ¥).

Rapid and highly effective removal of hexavalent chromium (Cr(Ⅵ)) is extremely vital to water resources restoration and environmental protection. To overcome the pH limitation faced by most ionic absorbents, an always positive covalent organic nanosheet (CON) material was prepared and its Cr(VI) adsorption and removal capability was investigated in detail. As-prepared EB-TFB CON (TFB = 1,3,5-benzaldehyde, EB = ethidium bromide) shows strong electropositivity in the tested pH range of 1 ∼ 10, display a pH-independent Cr(VI) removal ability, and work well for Cr(VI) pollution treatment with good anti-interference capability and reusability in a wide pH range covering almost all Cr(VI)-contaminated real water samples, thus eliminating the requirement for pH adjustment. Moreover, the nanosheet structure, which is obtained by a facile ultrasonic-assisted self-exfoliation, endows EB-TFB CON with fully exposed active sites and shortened mass transfer channels, and the Cr(VI) adsorption equilibrium can be reached within 15 min with a high adsorption capacity of 280.57 mg·g-1. The proposed Cr(VI) removal mechanism, which is attributed to the synergetic contributions of electrostatic adsorption, ion exchange and chemical reduction, is demonstrated by experiments and theoretical calculations. This work not only provides a general Cr(VI) absorbent without pH limitation, but also presents a paradigm to prepare ionic CONs with relatively constant surface charges.

Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.

Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches.

Quantitative ultrasound (QUS) analyzes the ultrasound (US) backscattered data to find the properties of scatterers that correlate with the tissue microstructure. Statistics of the envelope of the backscattered radio frequency (RF) data can be utilized to estimate several QUS parameters. Different distributions have been proposed to model envelope data. The homodyned K-distribution (HK-distribution) is one of the most comprehensive distributions that can model US backscattered envelope data under diverse scattering conditions (varying scatterer number density and coherent scattering). The scatterer clustering parameter ( α ) and the ratio of the coherent to diffuse scattering power ( k ) are the parameters of this distribution that have been used extensively for tissue characterization in diagnostic US. The estimation of these two parameters (which we refer to as HK parameters) is done using optimization algorithms in which statistical features such as the envelope point-wise signal-to-noise ratio (SNR), skewness, kurtosis, and the log-based moments have been utilized as input to such algorithms. The optimization methods minimize the difference between features and their theoretical value from the HK model. We propose that the true value of these statistical features is a hyperplane that covers a small portion of the feature space. In this article, we follow two approaches to reduce the effect of sample features' error. We propose a model projection neural network based on denoising autoencoders to project the noisy features into this space based on this assumption. We also investigate if the noise distribution can be learned by the deep estimators. We compare the proposed methods with conventional methods using simulations, an experimental phantom, and data from an in vivo animal model of hepatic steatosis. The network weight and a demo code are available online at ht.tp://code.sonography.ai.