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Objective: To evaluate the efficiency of colour Doppler energy (CDE) in comparison to conventional colour Doppler sonography (CDI) in the detection of renal blood flow signals in asymptomatic patients after renal transplantation., Subjects and Methods: Fifteen asymptomatic volunteer patients after renal transplantation were examined with CDI and CDE. The examination parameters were kept constant; only the Doppler-receiver gain was varied. Assessment and comparison of blood flow signals were obtained with a self defined score system., Results: CDI showed Doppler signals in the main stem vessels, segmental and interlobar vessels in all patients; in the arcuate arteries blood flow signals were detected in only 11 of 15 patients. There was no Doppler signal in peripherally located medullary and cortical vessels. CDE showed blood flow signals in the main stem, segmental and interlobar vessels and additionally in the arcuate, interlobular and medullary as well as in the cortical vessels in all patients., Conclusion: CDE is more sensitive than CDI and improves the detection of blood flow signals in peripheral medullary and cortical vessels of renal transplants.

The case of an 41 year old female is reported, who underwent surgical correction of a congenital cyst of the common bile duct 17 years ago. Because of repeated pain attacks in the right upper quadrant in connection with fever and serochemical signs of cholestasis the patient was admitted to our unit. The exploration with ERCP and CT now showed a common channel in combination with a congenital cyst of the common bile duct. The risk of the anomalous pancreaticobiliary junction upon either the biliary tract or the pancreatic duct will be discussed, also the fact of an higher incidence of malignant tumors of the biliary system. The therapeutic procedures will be discussed.

The following article describes a girl with right upper quadrant abdominal colic following Ceftriaxon therapy for purulent meningitis. Ultrasound made it possible to demonstrate sludge-balls, floating in the gallbladder, a follow up examination was normal. Moreover the features of gallbladder precipitations following Ceftriaxon therapy will be described, and the clinical consequences will be discussed.

Mechanical ventilation of mature or premature newborns is supposed to provide pulmonary gas exchange while causing minimal side effects to the respiratory system and circulation of the infants. A certain degree of alveolar ventilation, for example, can be achieved by different ventilator settings with different airway pressures (Fig. 1), and thus the corresponding effects on lung mechanics, barotrauma, and circulation differ. Parameters indicating optimal ventilator settings cannot be measured often enough in routine daily care and sometimes are difficult to interpret because of complex interactivity factors. The aim of the computer program "RespCalc" is to shorten the empirical steps of adapting respirator settings to the status of the ventilated patient by calculating the necessary data and drawing patient-specific nomograms. The computer program "RespCalc" makes use of the mathematical relationships characterizing the determinants of lung mechanics. The following data are necessary for use of the program: patient identification, weight, alveolar ventilation, recent paCO2, compliance, and resistance. By using the time-constant tau, the minimal inspiratory time as well as a maximal possible respirator rate are displayed without inducing inadvertent PEEP at a ratio of 1:1. Afterward, the planned figures of the different respirator rates and corresponding inspiration times must be typed in. The program simultaneously checks whether the previously calculated minimal figures for in- and exhalation time are maintained. If the above-mentioned figures and the typed-in ones differ, an optical and acoustical error message appears, and thus the user is forced to correct the problem.(ABSTRACT TRUNCATED AT 250 WORDS)

A case of congenital arterioportal fistulas in a girl of 4 months of age is presented. Clinical signs of necrotising enteritis developed due to portal hypertension. The diagnosis was established via Doppler-duplex ultrasound showing a pulse-synchronous bidirectional flow pattern in the portal vein and its major branches. The diagnosis was confirmed by angiography.

An aortopulmonary septal defect or aortopulmonary window (APW) is a rare cardiovascular anomaly with direct communication between the ascending aorta and the main pulmonary artery leading to a left-to-right shunt. It is accompanied by other cardiovascular anomalies in approximately half of patients. In order to avoid irreversible sequelae, interventional or surgical treatment should be performed as soon as possible. Cardiovascular CT, as a fast, non-invasive technique with excellent spatial resolution, has an increasing role in the evaluation of patients with APW, enabling precise and detailed planning of surgical treatment of APW and associated anomalies if present. This article aims to review the anatomical and clinical features of aortopulmonary septal defect with special emphasis on its detection and characterization by a CT examination. [ABSTRACT FROM AUTHOR]

The purpose of this narrative review was to comprehensively elaborate the various components of artificial intelligence (AI), their applications in spine surgery, practical concerns, and future directions. Over the years, spine surgery has been continuously transformed in various aspects, including diagnostic strategies, surgical approaches, procedures, and instrumentation, to provide better-quality patient care. Surgeons have also augmented their surgical expertise with rapidly growing technological advancements. AI is an advancing field that has the potential to revolutionize many aspects of spine surgery. We performed a comprehensive narrative review of the various aspects of AI and machine learning in spine surgery. To elaborate on the current role of AI in spine surgery, a review of the literature was performed using PubMed and Google Scholar databases for articles published in English in the last 20 years. The initial search using the keywords "artificial intelligence" AND "spine," "machine learning" AND "spine," and "deep learning" AND "spine" extracted a total of 78, 60, and 37 articles and 11,500, 4,610, and 2,270 articles on PubMed and Google Scholar. After the initial screening and exclusion of unrelated articles, duplicates, and non-English articles, 405 articles were identified. After the second stage of screening, 93 articles were included in the review. Studies have shown that AI can be used to analyze patient data and provide personalized treatment recommendations in spine care. It also provides valuable insights for planning surgeries and assisting with precise surgical maneuvers and decision-making during the procedures. As more data become available and with further advancements, AI is likely to improve patient outcomes. [ABSTRACT FROM AUTHOR]

Background: Respiratory problems are frequent in newborns, and are mainly studied with chest X-rays, whereas CT scans are usually needed for the evaluation of rare malformations and diseases. Lung ultrasound (LUS] has been proposed as an alternative method of diagnosing a variety of respiratory conditions. In recent years, there has been a rapid increase in LUS studies, thanks to the ability of LUS to rapidly exclude complications and significantly reduce radiation exposure in this fragile population. We aimed to summarize the current knowledge about LUS. Methods: A literature search was conducted on the Medline and Cochrane databases using appropriate terms. The inclusion criteria were: English language and human species. Exclusion criteria were: non-English language, animal species, case reports, case series, non-systematic reviews, and editorials. Results: The search returned 360 results. No Cochrane reviews were found. Titles and abstracts were screened, and 37 were finally considered. Studies concerning the use of lung ultrasound for the following conditions were presented: neonatal respiratory distress syndrome, transient tachypnea of the newborn, pneumothorax, pulmonary hemorrhage, pneumonia, bronchopulmonary dysplasia, and prediction of extubation success. Conclusions: We discussed the utility of LUS for the diagnosis and treatment of neonatal diseases according to the most recent literature. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a technique to make intelligent machines, mainly by using smart computer programs. It is based on a statistical analysis of data or machine learning. Using machine learning, software algorithms are designed according to the desired application. These techniques are found to have the potential for advancement in the medical field by generating new and significant perceptions from the data generated using various types of healthcare tests. Artificial intelligence (AI) in medicine is of two types: virtual and physical. The virtual part decides the treatment using electronic health record systems using various sensors whereas the physical part assists robots to perform surgeries, implants, replacement of various organs, elderly care, etc. Using AI, a machine can examine various kinds of health care test reports in one go which could save the time, money, and increase the chances of the patient to be treated without any hassles. At present, artificial intelligence (AI) is used while deciding the treatment, and medications using various tools which could analyze X-rays, CT scans, MRIs, and any other data. During the COVID pandemic, there was a huge/massive demand for AI-supported technologies and many of those were created during that time. This study is focused on various applications of AI in healthcare. [ABSTRACT FROM AUTHOR]

The purpose of this study was to explore the effects of the integration of machine learning into daily radiological diagnostics, using the example of the machine learning software mdbrain® (Mediaire GmbH, Germany) in the diagnostic MRI workflow of patients with multiple sclerosis at the University Medicine Greifswald. The data were assessed through expert interviews, a comparison of analysis times with and without the machine learning software, as well as a process analysis of MRI workflows. Our results indicate a reduction in the screen-reading workload, improved decision-making regarding contrast administration, an optimized workflow, reduced examination times, and facilitated report communication with colleagues and patients. Our results call for a broader and quantitative analysis. [ABSTRACT FROM AUTHOR]

Highlights: • Introduce a data augmentation strategy to expand the required different morphological data during the training and learning phase, and improve the algorithm's feature learning ability for complex and diverse tumor morphology CT images. • Design attention mechanisms for encoding and decoding paths to extract fine pixel level features, improve feature extraction capabilities, and achieve efficient spatial channel feature fusion. • The deep supervision layer is used to correct and decode the final image data to provide high accuracy of results. • The effectiveness of this method has been affirmed through validation on the LITS, 3DIRCADb, and SLIVER datasets. BACKGROUND: Accurately extracting liver and liver tumors from medical images is an important step in lesion localization and diagnosis, surgical planning, and postoperative monitoring. However, the limited number of radiation therapists and a great number of images make this work time-consuming. OBJECTIVE: This study designs a spatial attention deep supervised network (SADSNet) for simultaneous automatic segmentation of liver and tumors. METHOD: Firstly, self-designed spatial attention modules are introduced at each layer of the encoder and decoder to extract image features at different scales and resolutions, helping the model better capture liver tumors and fine structures. The designed spatial attention module is implemented through two gate signals related to liver and tumors, as well as changing the size of convolutional kernels; Secondly, deep supervision is added behind the three layers of the decoder to assist the backbone network in feature learning and improve gradient propagation, enhancing robustness. RESULTS: The method was testing on LITS, 3DIRCADb, and SLIVER datasets. For the liver, it obtained dice similarity coefficients of 97.03%, 96.11%, and 97.40%, surface dice of 81.98%, 82.53%, and 86.29%, 95% hausdorff distances of 8.96 mm, 8.26 mm, and 3.79 mm, and average surface distances of 1.54 mm, 1.19 mm, and 0.81 mm. Additionally, it also achieved precise tumor segmentation, which with dice scores of 87.81% and 87.50%, surface dice of 89.63% and 84.26%, 95% hausdorff distance of 12.96 mm and 16.55 mm, and average surface distances of 1.11 mm and 3.04 mm on LITS and 3DIRCADb, respectively. CONCLUSION: The experimental results show that the proposed method is effective and superior to some other methods. Therefore, this method can provide technical support for liver and liver tumor segmentation in clinical practice. [ABSTRACT FROM AUTHOR]

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Kernelized fuzzy C-means clustering with weighted local information is an extensively applied robust segmentation algorithm for noisy image. However, it is difficult to effectively solve the problem of segmenting image polluted by strong noise. To address this issue, a reconstruction-aware kernel fuzzy C-mean clustering with rich local information is proposed in this paper. Firstly, the optimization modeling of guided bilateral filtering is given for noisy image; Secondly, this filtering model is embedded into kernelized fuzzy C-means clustering with local information, and a novel reconstruction-filtering information driven fuzzy clustering model for noise-corrupted image segmentation is presented; Finally, a tri-level alternative and iterative algorithm is derived from optimizing model using optimization theory and its convergence is strictly analyzed. Many Experimental results on noisy synthetic images and actual images indicate that compared with the latest advanced fuzzy clustering-related algorithms, the algorithm presented in this paper has better segmentation performance and stronger robustness to noise, and its PSNR and ACC values increase by about 0.16–3.28 and 0.01–0.08 respectively. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI), particularly deep learning, has made enormous strides in medical imaging analysis. In the field of musculoskeletal radiology, deep-learning models are actively being developed for the identification and evaluation of bone fractures. These methods provide numerous benefits to radiologists such as increased diagnostic accuracy and efficiency while also achieving standalone performances comparable or superior to clinician readers. Various algorithms are already commercially available for integration into clinical workflows, with the potential to improve healthcare delivery and shape the future practice of radiology. In this systematic review, we explore the performance of current AI methods in the identification and evaluation of fractures, particularly those in the ankle, wrist, hip, and ribs. We also discuss current commercially available products for fracture detection and provide an overview of the current limitations of this technology and future directions of the field. [ABSTRACT FROM AUTHOR]

Fetal lung development is a crucial and complex process that lays the groundwork for postnatal respiratory health. However, disruptions in this delicate developmental journey can lead to fetal lung development disorders, impacting neonatal outcomes and potentially influencing health outcomes well into adulthood. Recent research has shed light on the intriguing association between fetal lung development disorders and the development of adult diseases. Understanding these links can provide valuable insights into the developmental origins of health and disease, paving the way for targeted preventive measures and clinical interventions. This review article aims to comprehensively explore the association of fetal lung development disorders with adult diseases. We delve into the stages of fetal lung development, examining key factors influencing fetal lung maturation. Subsequently, we investigate specific fetal lung development disorders, such as respiratory distress syndrome (RDS), bronchopulmonary dysplasia (BPD), congenital diaphragmatic hernia (CDH), and other abnormalities. Furthermore, we explore the potential mechanisms underlying these associations, considering the role of epigenetic modifications, transgenerational effects, and intrauterine environmental factors. Additionally, we examine the epidemiological evidence and clinical findings linking fetal lung development disorders to adult respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), and other respiratory ailments. This review provides valuable insights for healthcare professionals and researchers, guiding future investigations and shaping strategies for preventive interventions and long-term care. [ABSTRACT FROM AUTHOR]

The need for digital transformation is constant and understanding the mechanisms that aid organizations in achieving successful transformations is crucial. This study combines the dynamic capabilities theory with the perspective of digital maturity by answering the question, "What are the capabilities underlying the ability to become a digitally mature organization?" A systematic review of studies that investigated digital transformation from a maturity perspective was conducted and the findings were consolidated into a conceptual framework structured according to the dynamic capabilities lens. The framework was then compared to insights from case studies of four digitally mature organizations using the flexible pattern-matching approach. As a result, a framework of digital transformation dynamic capability was proposed, consisting of five thematic areas of action: designing and managing transformation, fostering digital value propositions, acting in digital business ecosystems, systematizing structural changes, and supporters and enablers. The main implication of this study is the original approach for consolidating and organizing previous literature on digital transformation that can guide organizations to articulate and develop specific processes and resources to mature digitally, leading to a capacity for continuous change in the perpetually evolving digital landscape. There are also implications for theory building, as the proposed framework can serve as an agenda for future research on dynamic capabilities for digital transformation. [ABSTRACT FROM AUTHOR]

Purpose: Children with extensive lymphatic malformations of the head and neck often suffer from functional impairment and aesthetic deformity which significantly affect the quality of life and may be life-threatening. Treatment with sirolimus has the potential to improve symptoms and downsize lymphatic malformations. This systematic review summarizes the current information about sirolimus treatment of lymphatic malformations of the head and neck in children, its efficacy and side effects., Methods: A systematic search of the literature regarding studies on sirolimus treatment of children with lymphatic malformations of the head and neck was performed in PubMed, Embase, and Google Scholar up to July 2021 with the search terms "lymphatic malformation", "lymphangioma", "cystic hygroma", "low-flow malformation", "sirolimus", "rapamycin", "mTOR inhibitor" and "children"., Results: In all, 28 studies including 105 children from newborn to 17 years treated with sirolimus for lymphatic malformations of the head and neck were analyzed. The most frequent initial dose was 0.8 mg/m 2 per dose, twice daily at 12-h interval. The target blood level differed between studies, 10-15 ng/mL and 5-15 ng/mL were most often used. More than 91% of the children responded to sirolimus treatment which lasts from 6 months to 4 years. Typical side effects were hyperlipidemia, neutropenia and infections., Methods: Sirolimus could be an effective treatment for children with large complicated lymphatic malformations of the head and neck. As not all patients will benefit from treatment, the decision to treat sirolimus should be made by a multidisciplinary team., (© 2022. The Author(s).)

Objectives: In this study, we propose an interpretable deep learning radiomics (IDLR) model based on [ 18 F]FDG PET images to diagnose the clinical spectrum of Alzheimer's disease (AD) and predict the progression from mild cognitive impairment (MCI) to AD., Methods: This multicentre study included 1962 subjects from two ethnically diverse, independent cohorts (a Caucasian cohort from ADNI and an Asian cohort merged from two hospitals in China). The IDLR model involved feature extraction, feature selection, and classification/prediction. We evaluated the IDLR model's ability to distinguish between subjects with different cognitive statuses and MCI trajectories (sMCI and pMCI) and compared results with radiomic and deep learning (DL) models. A Cox model tested the IDLR signature's predictive capability for MCI to AD progression. Correlation analyses identified critical IDLR features and verified their clinical diagnostic value., Results: The IDLR model achieved the best classification results for subjects with different cognitive statuses as well as in those with MCI with distinct trajectories, with an accuracy of 76.51% [72.88%, 79.60%], (95% confidence interval, CI) while those of radiomic and DL models were 69.13% [66.28%, 73.12%] and 73.89% [68.99%, 77.89%], respectively. According to the Cox model, the hazard ratio (HR) of the IDLR model was 1.465 (95% CI: 1.236-1.737, p < 0.001). Moreover, three crucial IDLR features were significantly different across cognitive stages and were significantly correlated with cognitive scale scores (p < 0.01)., Conclusions: Preliminary results demonstrated that the IDLR model based on [ 18 F]FDG PET images enhanced accuracy in diagnosing the clinical spectrum of AD., Key Points: Question The study addresses the lack of interpretability in existing DL classification models for diagnosing the AD spectrum. Findings The proposed interpretable DL radiomics model, using radiomics-supervised DL features, enhances interpretability from traditional DL models and improves classification accuracy. Clinical relevance The IDLR model interprets DL features through radiomics supervision, potentially advancing the application of DL in clinical classification tasks., (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

Hypoxemic respiratory failure is one of the leading causes of mortality in intensive care. Frequent assessment of individual physiological characteristics and delivery of personalized mechanical ventilation (MV) settings is a constant challenge for clinicians caring for these patients. Electrical impedance tomography (EIT) is a radiation-free bedside monitoring device that is able to assess regional lung ventilation and changes in aeration. With real-time tomographic functional images of the lungs obtained through a thoracic belt, clinicians can visualize and estimate the distribution of ventilation at different ventilation settings or following procedures such as prone positioning. Several studies have evaluated the performance of EIT to monitor the effects of different MV settings in patients with acute respiratory distress syndrome, allowing more personalized MV. For instance, EIT could help clinicians find the positive end-expiratory pressure that represents a compromise between recruitment and overdistension and assess the effect of prone positioning on ventilation distribution. The clinical impact of the personalization of MV remains to be explored. Despite inherent limitations such as limited spatial resolution, EIT also offers a unique noninvasive bedside assessment of regional ventilation changes in the ICU. This technology offers the possibility of a continuous, operator-free diagnosis and real-time detection of common problems during MV. This review provides an overview of the functioning of EIT, its main indices, and its performance in monitoring patients with acute respiratory failure. Future perspectives for use in intensive care are also addressed. [ABSTRACT FROM AUTHOR]

Oncology has emerged as a crucial field of study in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modality for the identification and characterisation of pathologies, particularly in oncology, enabling precise identification of affected organs and tissues. However, achieving accurate liver segmentation in computed tomography scans remains a challenge due to the presence of artefacts and the varying densities of soft tissues and adjacent organs. This paper compares artificial intelligence algorithms and traditional medical image processing techniques to assist radiologists in liver segmentation in computed tomography scans and evaluates their accuracy and efficiency. Despite notable progress in the field, the limited availability of public datasets remains a significant barrier to broad participation in research studies and replication of methodologies. Future directions should focus on increasing the accessibility of public datasets, establishing standardised evaluation metrics, and advancing the development of three-dimensional segmentation techniques. In addition, maintaining a collaborative relationship between technological advances and medical expertise is essential to ensure that these innovations not only achieve technical accuracy, but also remain aligned with clinical needs and realities. This synergy ensures their applicability and effectiveness in real-world healthcare environments. [ABSTRACT FROM AUTHOR]

Objective: Capillary leak syndrome (CLS) is characterized by severe systemic edema without specific treatment, resulting in a high mortality rate. This study investigated whether there is organ edema in neonatal CLS patients and specific treatment strategies to improve patient prognosis. Methods: Thirty-seven newborns diagnosed with CLS were included in this study. (1) Routine point-of-care ultrasound (POCUS) was used to identify whether the patients had visceral edema or fluid collection. (2) All patients were treated with 3% NaCl intravenously, and the clinical manifestations, laboratory indices and outcomes were compared before and after treatment. Results: (1) Diffuse severe edema was found in 92.0% of the patients. (2) The POCUS examination revealed that CLS patients exhibited significant visceral edema in addition to diffuse severe edema, which included pulmonary edema in 67.6%, cerebral edema in 37.8%, severe intestinal edema in 24.3%, severe myocardial edema in 8.1%, pericardial effusion in 5.4%, pleural effusion in 29.7% and peritoneal effusion in 18.9%. Two patients (5.45%) had only myocardial edema without other manifestations. (3) Before and after the intravenous injection of 3% NaCl, there were no significant differences in the serum sodium or potassium levels of CLS patients, while the hemoglobin and hematocrit levels were significantly lower after treatment (p < 0.01). Her plasma ALB concentration and arterial pressure returned to normal levels after the treatment was completed. (4) All the patients survived, and no side effects or complications were observed during or after treatment with 3% NaCl. Conclusions: (1) In addition to diffuse severe edema, visceral edema and effusion are common and important clinical manifestations of neonatal CLS and need to be detected by routine POCUS. (2) The intravenous injection of 3% NaCl is a safe, effective and specific treatment strategy for neonatal CLS, with a survival rate of 100% and no adverse effects. [ABSTRACT FROM AUTHOR]

DNN-based systems have demonstrated unprecedented performance in terms of accuracy and speed over the past decade. However, recent work has shown that such models may not be sufficiently robust during the inference process. Furthermore, due to the data-driven learning nature of DNNs, designing interpretable and generalizable networks is a major challenge, especially when considering critical applications such as medical computer-aided diagnostics (CAD) and other medical imaging tasks. Within this context, a line of approaches incorporating prior knowledge domain information into deep learning methods has recently emerged. In particular, many of these approaches utilize known physics-based forward imaging models, aimed at improving the stability and generalization ability of DNNs for medical imaging applications. In this paper, we review recent work focused on such physics-based or physics-prior-based learning for a variety of imaging modalities and medical applications. We discuss how the inclusion of such physics priors to the training process and/or network architecture supports their stability and generalization ability. Moreover, we propose a new physics-based approach, in which an explicit physics prior, which describes the relation between the input and output of the forward imaging model, is included as an additional input into the network architecture. Furthermore, we propose a tailored training process for this extended architecture, for which training data are generated with perturbed physical priors that are also integrated into the network. Within the scope of this approach, we offer a problem formulation for a regression task with a highly nonlinear forward model and highlight possible useful applications for this task. Finally, we briefly discuss future challenges for physics-informed deep learning in the context of medical imaging. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to evaluate the effectiveness of ChatGPT-4o in assessing the methodological quality of radiomics research using the radiomics quality score (RQS) compared to human experts., Methods: Published in European Radiology, European Radiology Experimental, and Insights into Imaging between 2023 and 2024, open-access and peer-reviewed radiomics research articles with creative commons attribution license (CC-BY) were included in this study. Pre-prints from MedRxiv were also included to evaluate potential peer-review bias. Using the RQS, each study was independently assessed twice by ChatGPT-4o and by two radiologists with consensus., Results: In total, 52 open-access and peer-reviewed articles were included in this study. Both ChatGPT-4o evaluation (average of two readings) and human experts had a median RQS of 14.5 (40.3% percentage score) (p > 0.05). Pairwise comparisons revealed no statistically significant difference between the readings of ChatGPT and human experts (corrected p > 0.05). The intraclass correlation coefficient for intra-rater reliability of ChatGPT-4o was 0.905 (95% CI: 0.840-0.944), and those for inter-rater reliability with human experts for each evaluation of ChatGPT-4o were 0.859 (95% CI: 0.756-0.919) and 0.914 (95% CI: 0.855-0.949), corresponding to good to excellent reliability for all. The evaluation by ChatGPT-4o took less time (2.9-3.5 min per article) compared to human experts (13.9 min per article by one reader). Item-wise reliability analysis showed ChatGPT-4o maintained consistently high reliability across almost all RQS items., Conclusion: ChatGPT-4o provides reliable and efficient assessments of radiomics research quality. Its evaluations closely align with those of human experts and reduce evaluation time., Key Points: Question Is ChatGPT effective and reliable in evaluating radiomics research quality based on RQS? Findings ChatGPT-4o showed high reliability and efficiency, with evaluations closely matching human experts. It can considerably reduce the time required for radiomics research quality assessment. Clinical relevance ChatGPT-4o offers a quick and reliable automated alternative for evaluating the quality of radiomics research, with the potential to assess radiomics research at a large scale in the future., (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

We aimed to implement a fully automatic computed tomography (CT) image-detection programming algorithm as a pectus excavatum (PE) diagnostic tool, facilitating comprehensive chest wall deformity evaluation. We developed our algorithm using MATLAB, leveraging the Hounsfield unit threshold and region growing methods. The MATLAB graphical user interface enables the direct use of our program. We validated the model using CT images of anthropomorphic phantoms and one normal individual. The measurement values obtained by our algorithm demonstrated very small differences compared to the known anthropomorphic phantom model data and manual measurement. For algorithm testing, 17,214 chest CT scans obtained from 57 PE patients were processed by the algorithm and independently reviewed by a radiologist and a thoracic surgeon. The measurements of transverse, anteroposterior, and sternum-to-vertebral distance of the thoracic cavity, along with the calculated data of four indices, exhibited high positive correlations (0.94-0.99). The asymmetry index and maximum anteroposterior hemithorax distance exhibited moderate correlation (0.40-0.83). Our automatic PE diagnostic tool demonstrated high accuracy; four chest wall deformity indices were obtained simultaneously without any initial manual marking, correlating well with manual measurements., (© 2024. The Author(s).)

Background: Intervertebral disc calcification (IDC) combined with calcification in children has been sporadically reported, while ossification of the posterior longitudinal ligament (OPLL) in the cervical spine in pediatric patients is exceedingly rare. The aim of this study is to investigate the potential prognosis and outcomes associated with this condition. Case presentation: We present an unusual case involving a 10-year-old Chinese child diagnosed with calcified cervical disc herniation and ossification of the posterior longitudinal ligament. Conservative treatment measures were implemented, and at the 1-month and 6-month follow-up, the patient's pain exhibited significant improvement. Subsequent cervical MRI and CT scans revealed the complete disappearance of OPLL and substantial absorption of the calcified disc. During the three-month follow-up, CT demonstrated slight residual disc calcification, however, the patient remained asymptomatic with no discernible limitation in cervical motion. Conclusions: We conducted a comprehensive review of several cases presenting with the same diagnosis. It is noteworthy that IDC combined with OPLL in children constitutes a rare clinical entity. Despite imaging indications of potential spinal canal occupation, the majority of such cases demonstrate complete absorption following conservative treatment, with OPLL exhibiting a faster absorption rate than calcified discs. [ABSTRACT FROM AUTHOR]

In order to improve the corrosion resistance of Mg-3Gd-1Zn-0.4Zr (GZ31K) alloys for biomedical application, the alloy was micro-arc oxidation (MAO)-treated using silicate electrolyte system under various voltages (400 V, 425 V, 450 V, 475 V). The effects of voltage on the microstructure and corrosion properties of MAO coating were investigated via X-ray diffraction (XRD) and a scanning electron microscope (SEM) combined with an energy-dispersive spectrometer (EDS), X-ray photoelectron spectroscope (XPS), and electrochemical experiments. The results showed that, with the increase in voltage, the MAO coatings became thicker and the micropores on the MAO coating increased in diameter. The main phase compositions of the MAO coatings were MgO and Mg2SiO4. Potentiodynamic polarization curve results showed that MAO coatings could enhance corrosion resistances, where the corrosion current density decreased by six orders of magnitude and the corrosion potential of the specimens increased by 300 mV for the voltage of 450 V in the MAO treatment; nevertheless, the corrosion resistance rapidly deteriorated due to the creation of large micropores in the MAO coating, which provide a pathway for corrosive media when the voltage is 475 V. The electrochemical impedance spectroscopy results showed that MAO treatments could increase low-frequency modulus resistance and increase the corrosion resistance of Mg alloys. In addition, MAO-treated GZ31K alloys still exhibited uniform corrosion, which is desirable for biomedical applications. [ABSTRACT FROM AUTHOR]

The article explores the use of lung ultrasonography (LUS) in diagnosing and differentiating types of pulmonary edema. LUS has gained popularity in diagnosing lung diseases, but there is debate over its ability to accurately distinguish between alveolar and interstitial pulmonary edema. Factors such as probe types, scanning depth, and focus position can impact the appearance of B-lines, making it challenging to make definitive judgments. While LUS cannot differentiate cardiogenic and pulmonary lung edema based on B-lines, cardiac ultrasound can be used to distinguish cardiogenic pulmonary edema caused by left heart failure. Further research is needed to better understand the relationship between B-lines and pulmonary edema. [Extracted from the article]

Choledochocele is defined as a congenital dilatation of the distal intramural part of the common bile duct protruding into the wall of the descending duodenum, typically without pancreaticobiliary maljunction. However, some cases present with a similar pathophysiology to pancreaticobiliary maljunction, including reciprocal reflux of pancreatic juices and bile, leading to protein plugs, pancreatitis, and biliary tract carcinogenesis. Choledochocele is relatively rare and its anatomy, physiology, pathology, and clinical features are thus not well known. We describe a patient with choledochocele who suffered from repeated severe acute pancreatitis and underwent subtotal stomach-preserving pancreatoduodenectomy, in whom the pathological findings of choledochocele showed hyperplasia. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) is a non-invasive imaging method that allows for the acquisition of resistivity distribution information within an object without the use of radiation. EIT is widely used in various fields, such as medical imaging, industrial imaging, geological exploration, etc. Presently, most electrical impedance imaging methods are restricted to uniform domains, such as pixelated pictures. These algorithms rely on model learning-based image reconstruction techniques, which often necessitate interpolation and embedding if the fundamental imaging model is solved on a non-uniform grid. EIT technology still confronts several obstacles today, such as insufficient prior information, severe pathological conditions, numerous imaging artifacts, etc. In this paper, we propose a new electrical impedance tomography algorithm based on the graph convolutional neural network model. Our algorithm transforms the finite-element model (FEM) grid data from the ill-posed problem of EIT into a network graph within the graph convolutional neural network model. Subsequently, the parameters in the non-linear inverse problem of the EIT process are updated by using the improved Levenberg—Marquardt (ILM) method. This method generates an image that reflects the electrical impedance. The experimental results demonstrate the robust generalizability of our proposed algorithm, showcasing its effectiveness across different domain shapes, grids, and non-distributed data. [ABSTRACT FROM AUTHOR]

In this era of e-commerce, many companies are moving towards subscription-based invoicing platforms to maintain their electronic invoices. Unfortunately, fraudsters are using these platforms for different types of malicious activities. Identifying fraudsters is often challenging for many companies due to the limitation of time and other resources. A fully automated fraud detection model can be useful, but it creates a risk of false-positive identification. This paper proposed a hybrid fraud detection framework when only a small set of labelled (fraud/non-fraud) data is available, and human input is required in the final decision-making step. This framework used a combination of unsupervised and supervised machine learning, red-flag prioritization, and an augmented AI approach containing a human-in-the-loop process. It also proposed a weighted center based on the feature importance scores for the fraud risk cluster and used it in the red-flag prioritization process. Finally, the approach is illustrated using a case study to identify fraudulent users in an invoicing platform. Our hybrid framework showed promising results in identifying fraudulent users and improving human performance when human input is required to make the final decision. [ABSTRACT FROM AUTHOR]

Neonatal respiratory distress syndrome (NRDS) is a common critical disease in neonates. Early diagnosis and timely treatment are crucial. Historically, X-ray imaging was the primary method for diagnosing NRDS. However, this method carries radiation exposure risks, making it unsuitable for dynamic lung condition monitoring. In addition, neonates who are critically ill require bedside imaging, but diagnostic delays are often unavoidable due to equipment transportation and positioning limitations. These challenges have been resolved with the introduction of lung ultrasound (LUS) in neonatal intensive care. The diagnostic efficacy and specificity of LUS for NRDS is superior to that of X-ray. The non-invasive, dynamic, and real-time benefits of LUS also allow for real-time monitoring of lung changes throughout treatment for NRDS, yielding important insights for guiding therapy. In this paper, we examine the ultrasonographic characteristics of NRDS and the recent progress in the application of ultrasound in the diagnosis and treatment of NRDS while aiming to promote wider adoption of this method. [ABSTRACT FROM AUTHOR]

This article issues scientific background of Cone-beam computed tomography (CBCT) and the importance of taking x-rays before and after implant placement in daily practice as a common care. The review will introduce cone-beam computed tomography guidelines, restrictions and intraoperartive issues for instance nerve damage and bleeding incidents. Modern CBCT technology enables specialists to avoid making a wrong diagnosis, which translates into a higher percentage of people with a positive treatment outcome. Diagnostic radiology is a crucial element of every dental treatment planning. CBCT market is expanding gradually since two decades, there are more than 85 distinct CBCT tools available. CBCT is a three-dimensional (3D) imaging used nowadays in dentistry with increased frequency and offers volumetric data on jaw bones and teeth with relatively low radiation doses and costs. Currently, the greatest advantage of CBCT examinations over radiographs is the fact that the image obtained is presented in a 3D projection and not, as is the case, in 2D. It has the ability to help a wider range of patients, but the use of CBCT also has negative consequences. Routine or excessive use has resulted in increased radiation doses accumulating in the patient's body, which translates into an increased risk of adverse effects. The risk varies according to the age of the patient under study and is directly proportional to it, that is, it is highest for young people and lower for older people. The potential risk is also slightly higher in the female population. For this reason, creating awareness of the mandatory patient safety management of CT scans is a key process by which Xray exposures can be optimised. [ABSTRACT FROM AUTHOR]

Introduction: Cranial CT scans are associated with radiation exposure to the eye lens, which is a particularly radiosensitive organ. Children are more vulnerable to radiation than adults. Therefore, it is essential to use the available dose reduction techniques to minimize radiation exposure. According to the European Consensus on patient contact shielding by the IRCP from 2021, shielding is not recommended in most body areas anymore. This study aims to evaluate whether bismuth shielding as well as its combination with other dose-saving technologies could still be useful. Methods: Cranial CT scans of a pediatric anthropomorphic phantom were performed on two up-to-date MDCT scanners. Eye lens dose measurements were performed using thermoluminescent dosimeters. Furthermore, the impact of BS and of the additional placement of standoff foam between the patient and BS on image quality was also assessed. Results: Bismuth shielding showed a significant lens dose reduction in both CT scanners (GE: 41.50 ± 4.04%, p < 0.001; Siemens: 29.75 ± 6.55%, p = 0.00). When combined with AEC, the dose was lowered even more (GE: 60.75 ± 3.30%, p < 0.001; Siemens: 41.25 ± 8.02%, p = 0.00). The highest eye dose reduction was achieved using BS + AEC + OBTCM (GE: 71.25 ± 2.98%, p < 0.001; Siemens: 58.75 ± 5.85%, p < 0.001). BS caused increased image noise in the orbital region, which could be mitigated by foam placement. Eye shielding had no effect on the image noise in the cranium. Conclusions: The use of BS in cranial CT can lead to a significant dose reduction, which can be further enhanced by its combination with other modern dose reduction methods. BS causes increase in image noise in the orbital region but not in the cranium. The additional use of standoff foam reduces image noise in the orbital region. [ABSTRACT FROM AUTHOR]

The most common acute infection and leading cause of death in children worldwide is pneumonia. Clinical and laboratory tests essentially diagnose community-acquired pneumonia (CAP). CAP can be caused by bacteria, viruses, or atypical microorganisms. Imaging is usually reserved for children who do not respond to treatment, need hospitalisation, or have hospital-acquired pneumonia. This review discusses the imaging findings for acute CAP complications and the diagnostic role of each imaging modality. Pleural effusion, empyema, necrotizing pneumonia, abscess, pneumatocele, pleural fistulas, and paediatric acute respiratory distress syndrome (PARDS) are acute CAP complications. When evaluating complicated CAP patients, chest radiography, lung ultrasonography, computed tomography (CT), and magnetic resonance imaging (MRI) can be used, with each having their own pros and cons. Imaging is usually not needed for CAP diagnosis, but it is essential for complicated cases and follow-ups. Lung ultrasound can supplement chest radiography (CR), which starts the diagnostic algorithm. Contrast-enhanced computed tomography (CECT) is used for complex cases. Advances in MRI protocols make it a viable alternative for diagnosing CAP and its complications. [ABSTRACT FROM AUTHOR]

Objective: Complications after Endovascular Aneurysm Repair (EVAR) can be fatal. Patient follow-up for surveillance imaging is becoming more challenging as fewer patients are seen, particularly after the first year. The aim of this study was to develop an artificial intelligence model to predict the complication probability of individual patients to better identify those needing more intensive post-operative surveillance. Methods: Pre-operative CTA 3D reconstruction images of AAA from 273 patients who underwent EVAR from 2011-2020 were collected. Of these, 48 patients had post-operative complications including endoleak, AAA rupture, graft limb occlusion, renal artery occlusion, and neck dilation. A deep convolutional neural network model (VascAI©) was developed which utilized pre-operative 3D CT images to predict risk of complications after EVAR. The model was built with TensorFlow software and run on the Google Colab Platform. An initial training subset of 40 randomly selected patients with complications and 189 without were used to train the AI model while the remaining 8 positive and 36 negative cases tested its performance and prediction accuracy. Data down-sampling was used to alleviate data imbalance and data augmentation methodology to further boost model performance. Results: Successful training was completed on the 229 cases in the training set and then applied to predict the complication probability of each individual in the held-out performance testing cases. The model provided a complication sensitivity of 100% and identified all the patients who later developed complications after EVAR. Of 36 patients without complications, 16 (44%) were falsely predicted to develop complications. The results therefore demonstrated excellent sensitivity for identifying patients who would benefit from more stringent surveillance and decrease the frequency of surveillance in 56% of patients unlike to develop complications. Conclusion: AI models can be developed to predict the risk of post-operative complications with high accuracy. Compared to existing methods, the model developed in this study did not require any expert-annotated data but only the AAA CTA images as inputs. This model can play an assistive role in identifying patients at high risk for post-EVAR complications and the need for greater compliance in surveillance. [ABSTRACT FROM AUTHOR]

In pediatric radiology, balancing diagnostic accuracy with reduced radiation exposure is paramount due to the heightened vulnerability of younger patients to radiation. Technological advancements in computed tomography (CT) reconstruction techniques, especially model-based iterative reconstruction and deep learning image reconstruction, have enabled significant reductions in radiation doses without compromising image quality. Deep learning image reconstruction, powered by deep learning algorithms, has demonstrated superiority over traditional techniques like filtered back projection, providing enhanced image quality, especially in pediatric head and cardiac CT scans. Photon-counting detector CT has emerged as another groundbreaking technology, allowing for high-resolution images while substantially reducing radiation doses, proving highly beneficial for pediatric patients requiring frequent imaging. Furthermore, cloud-based dose tracking software focuses on monitoring radiation exposure, ensuring adherence to safety standards. However, the deployment of these technologies presents challenges, including the need for large datasets, computational demands, and potential data privacy issues. This article provides a comprehensive exploration of these technological advancements, their clinical implications, and the ongoing efforts to enhance pediatric radiology's safety and effectiveness. [ABSTRACT FROM AUTHOR]

Point-of-care critical ultrasound (POC-CUS) screening plays an increasingly important role in the treatment of critically ill infants. Without POC-CUS, the lives of many infants would not be saved in time and correctly. A premature infant with systemic multiple organ system dysfunction caused by fungal sepsis was treated and nursed under the guidance of POC-CUS monitoring, and the infant was ultimately cured. This premature infant had systemic multiple organ system dysfunction and disseminated intravascular coagulation (DIC) caused by fungal sepsis. In the hypercoagulable state of early-stage DIC, cardiac thrombosis could be found using ultrasound screening. For this case, right renal artery thrombosis was found via renal artery Doppler ultrasound examination. Due to the severity of this disease, ultrasound-guided peripherally inserted central catheter (PICC) insertion and ultrasound checks of the PICC tip's position were performed, which ensured the success of this one-time catheterization and shortened the catheterization time. Lung ultrasound is used for the diagnosis and differential diagnosis of pulmonary diseases, and to guide the application of mechanical ventilation. Because the abdominal circumference of the patient's markedly enlarged abdominal circumference, bloody stool, and absence of bowel sounds, abdominal ultrasonography was performed, which revealed a markedly enlarged liver, significant peritoneal effusion, and necrotizing enterocolitis. Guided by POC-CUS monitoring, we had the opportunity to implement timely and effective treatment that ultimately saved this critically ill patient's life. The successful treatment of this newborn infant fully reflects the importance of carrying out POC-CUS screening. [ABSTRACT FROM AUTHOR]

The basic salp swarm algorithm (SSA) is a novel nature-inspired swarm intelligence optimization algorithm based on the foraging behavior of salp individuals in the deep sea. Since its development, the salp swarm algorithm has attracted widespread interest from scholars both at home and abroad for solving complex real-world practical problems. With continuous research, the SSA algorithm has revealed some shortcomings such as slow convergence speed and low accuracy. To enhance the optimization capability of the algorithm, in this paper, we propose an improved hybrid algorithm called TLSSA based on two phases of the teaching–learning-based optimization method: the teaching phase and the learner phase. In the teaching phase, students' ability is improved by updating the difference between the teacher and the class average level, which helps to improve the overall learning ability of the salp population, resulting in higher quality solutions. In the learning phase, by simulating the discussion, statement, and communication between students, the average level of the individual is improved, and the global search speed of the algorithm is accelerated. To verify the effectiveness and competitiveness of the proposed method, it is first tested on 30 IEEE CEC 2017 benchmark functions. The test results demonstrate that the proposed TLSSA method obtains better overall performance compared to 8 mainstream meta-heuristics and 8 advanced algorithms. After that, we applied the proposed method to solve two classical real-world engineering design problems and feature selection. Again, the experimental results show that our method has significant advantages over the traditional methods in solving these practical problems. The remarkable performance of our proposed improved TLSSA algorithm in solving theoretical and practical complex optimization problems also provides potential possibilities for applying more intelligent optimization algorithms to solve complex optimization problems in real-life situations in the future. [ABSTRACT FROM AUTHOR]