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Chronic kidney disease is a leading public health problem worldwide. The global prevalence of chronic kidney disease is nearly five hundred million people, with almost one million deaths worldwide. Estimated glomerular filtration rate, imaging such as conventional ultrasound, and histopathological findings are necessary as each technique provides specific information which, when taken together, may help to detect and arrest the development of chronic kidney disease, besides managing its adverse outcomes. However, estimated glomerular filtration rate measurements are hampered by substantial error margins while conventional ultrasound involves subjective assessment. Although histopathological assessment is the best tool for evaluating the severity of the renal pathology, it may lead to renal insufficiency and haemorrhage if complications occurred. Ultrasound shear wave elastography, an emerging imaging that quantifies tissue stiffness non-invasively has gained interest recently. This method applies acoustic force pulses to generate shear wave within the tissue that propagate perpendicular to the main ultrasound beam. By measuring the speed of shear wave propagation, the tissue stiffness is estimated. This paper reviews the literature and presents our combined experience and knowledge in renal shear wave elastography research. It discusses and highlights the confounding factors on shear wave elastography, current and future possibilities in ultrasound renal imaging and is not limited to new sophisticated techniques. [ABSTRACT FROM AUTHOR]

Many studies have investigated the dielectric properties of human and animal tissues, particularly to differentiate between normal cells and tumors. However, these studies are invasive as tissue samples have to be excised to measure the properties. This study aims to investigate the dielectric properties of urine in relation to bladder cancer, which is safe and non-invasive to patients. 30 healthy subjects and 30 bladder cancer patients were recruited. Their urine samples were subjected to urinalysis and cytology assessment. A vector network analyzer was used to measure the dielectric constant (Ɛ′) and loss factor (Ɛ″) at microwave frequencies of between 0.2 and 50 GHz at 25 °C, 30 °C and 37 °C. Significant differences in Ɛ′ and Ɛ″ were observed between healthy subjects and patients, especially at frequencies of between 25 and 40 GHz at 25 °C. Bladder cancer patients had significant lower Ɛ′ and higher Ɛ″ compared with healthy subjects. The Ɛ′ was negatively correlated with urinary exfoliated urothelial cell number, and Ɛ″ was positively correlated. The study achieved a receiver operating characteristic area under curve (ROC–AUC) score of 0.69099 and an optimum accuracy of 75% with a sensitivity of 80% and a specificity of 70%. The number of exfoliated urothelial cell had significant effect on the dielectric properties, especially in bladder cancer patients. Urinary dielectric properties could potentially be used as a tool to detect bladder cancer. [ABSTRACT FROM AUTHOR]

TMP is gradually becoming a fundamental element for quality assurance and control in ionizing and non-ionizing radiation imaging modalities as well as in the development of different techniques. This study aimed to evaluate and obtain polyvinyl chloride tissue mimicking material for dual-modality breast phantoms in mammography and ultrasound. Breast tissue equivalence was evaluated based on X-ray attenuation properties, speed of sound, attenuation, and acoustic impedance. There are six samples of PVC-plasticizer material with variations of PVC concentration and additives. The evaluation of X-ray attenuation was carried out using mammography from 23 to 35 kV, while the acoustic properties were assessed with mode A ultrasound and a transducer frequency of 5 MHz. A breast phantom was created from TMP material with tissue equivalence and was then evaluated using mammography as well as ultrasound to analyze its image quality. The results showed that samples A (PVC 5%, DOP 95%), B (PVC 7%, DOP 93%), C (PVC 10%, DOP 90%), E (PVC 7%, DOP 90%, graphite 3%), and F (PVC 7%, DOP 90%, silicone oil 3%) have the closest equivalent to the ACR breast phantom material with a different range of 0.01–1.39 in the 23–35 kV range. Based on the evaluation of the acoustic properties of ultrasound, A had high similarity to fat tissue with a difference of 0.03 (dB cm− 1 MHz− 1) and 0.07 (106 kg m− 2 s− 1), while B was close to the glandular tissue with a difference of 9.2 m s− 1. Multilayer breast phantom images' results showed gray levels in mammography and ultrasound modalities. Therefore, this study succeeded in establishing TMP material for mammography and ultrasound. It can also be used for simple quality assurance and control programs. [ABSTRACT FROM AUTHOR]

The article titled "Retraction Note: Automated detection of chronic kidney disease using higher-order features and elongated quinary patterns from B-mode ultrasound images" has been retracted by the Editor-in-Chief and the publisher due to concerns regarding compromised editorial handling and peer review process, inappropriate references, and not being in scope of the journal or guest-edited issue. The investigation's findings have led the Editor-in-Chief to lose confidence in the results and conclusions of the article. The author, Edward J. Ciaccio, has stated that all authors disagree with the retraction. Springer Nature, the publisher, remains neutral regarding jurisdictional claims and institutional affiliations. [Extracted from the article]

Artificial intelligence (AI) is an innovative tool with the potential to impact medical physicists' clinical practices, research, and the profession. The relevance of AI and its impact on the clinical practice and routine of professionals in medical physics were evaluated by medical physicists and researchers in this field. An online survey questionnaire was designed for distribution to professionals and students in medical physics around the world. In addition to demographics questions, we surveyed opinions on the role of AI in medical physicists' practices, the possibility of AI threatening/disrupting the medical physicists' practices and career, the need for medical physicists to acquire knowledge on AI, and the need for teaching AI in postgraduate medical physics programmes. The level of knowledge of medical physicists on AI was also consulted. A total of 1019 respondents from 94 countries participated. More than 85% of the respondents agreed that AI would play an essential role in medical physicists' practices. AI should be taught in the postgraduate medical physics programmes, and that more applications such as quality control (QC), treatment planning would be performed by AI. Half of the respondents thought AI would not threaten/disrupt the medical physicists' practices. AI knowledge was mainly acquired through self-taught and work-related activities. Nonetheless, many (40%) reported that they have no skill in AI. The general perception of medical physicists was that AI is here to stay, influencing our practices. Medical physicists should be prepared with education and training for this new reality. [ABSTRACT FROM AUTHOR]

This study objectively evaluates the similarity between standard full-field digital mammograms and two-dimensional synthesized digital mammograms (2DSM) in a cohort of women undergoing mammography. Under an institutional review board–approved data collection protocol, we retrospectively analyzed 407 women with digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) examinations performed from September 1, 2014, through February 29, 2016. Both FFDM and 2DSM images were used for the analysis, and 3216 available craniocaudal (CC) and mediolateral oblique (MLO) view mammograms altogether were included in the dataset. We analyzed the mammograms using a fully automated algorithm that computes 152 structural similarity, texture, and mammographic density–based features. We trained and developed two different global mammographic image feature analysis–based breast cancer detection schemes for 2DSM and FFDM images, respectively. The highest structural similarity features were obtained on the coarse Weber Local Descriptor differential excitation texture feature component computed on the CC view images (0.8770) and MLO view images (0.8889). Although the coarse structures are similar, the global mammographic image feature–based cancer detection scheme trained on 2DSM images outperformed the corresponding scheme trained on FFDM images, with area under a receiver operating characteristic curve (AUC) = 0.878 ± 0.034 and 0.756 ± 0.052, respectively. Consequently, further investigation is required to examine whether DBT can replace FFDM as a standalone technique, especially for the development of automated objective-based methods. [ABSTRACT FROM AUTHOR]

Papillary breast lesions include a wide spectrum of pathologies ranging from benign to malignant. The word papillary originates from finger-like projections, or papules, which are seen when these lesions are projected under a microscope. Papillary breast lesions have an array of radiological features at presentations; hence differentiation between benign and malignant based on imaging features is challenging. Histopathological diagnosis is crucial for the distinction and further management of the lesions. Traditionally, tumor and ductal excision is the treatment of choice for malignant and atypical or benign papilloma with imaging discordance. However, current clinical practice guidance advocates complete surgical excision, even for asymptomatic and purely benign papillomas diagnosed on core needle biopsy, as they are highly associated with atypia and malignant upstage on subsequent surgery. Computer aided diagnosis (CAD) is a non-invasive method of diagnosing medical signals/images using advanced image processing followed by soft computing techniques. In this study, we have developed a non-invasive CAD system for differentiating benign versus malignant papillary breast lesions using bi-dimensional empirical mode decomposition (BEMD) and the discrete cosine transform (DCT) followed by locality sensitive discriminant analysis (LSDA). The developed model is validated using a large collection of ultrasound images of papillary breast lesions, and achieved a maximum performance of 98.63% accuracy. We have also developed a breast papillary index, which may in the future be used as a substitute for the conventional soft computing techniques. The developed model can be utilized as a tool to assist radiologists in their routine clinical practice after validation with a larger database. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is a continuing loss of kidney function, and early detection of this disease is fundamental to halting its progression to end-stage disease. Numerous methods have been proposed to detect CKD, mainly focusing on classification based upon peripheral clinical parameters and quantitative ultrasound parameters that must be manually calculated, or on shear wave elastography. No studies have been found that detect the presence or absence of CKD based solely from one B-mode ultrasound image. In this work, we propose an automated system to detect chronic kidney disease utilizing only the automatic extraction of features from a B-mode ultrasound image of the kidney, with a database of 405 images. Higher-order bispectrum and cumulants, and elongated quinary patterns, are extracted from each image to provide a final total of 24,480 features per image. These features were subjected to a locality sensitive discriminant analysis (LSDA) technique, which provides 30 LSDA coefficients. The coefficients were arranged according to their t value and inserted into various classifiers, to yield the best diagnostic accuracy using the least number of features. The best performance was obtained using a support vector machine and a radial basis function, utilizing only five features, resulting in an accuracy of 99.75%, a sensitivity of 100%, and a specificity of 99.57%. Based upon these findings, it is evident that the technique accurately and automatically identifies subjects with and without CKD from B-mode ultrasound images. [ABSTRACT FROM AUTHOR]

Purpose: Lung nodules have very diverse shapes and sizes, which makes classifying them as benign/malignant a challenging problem. In this paper, we propose a novel method to predict the malignancy of nodules that have the capability to analyze the shape and size of a nodule using a global feature extractor, as well as the density and structure of the nodule using a local feature extractor. Methods: We propose to use Residual Blocks with a 3 × 3 kernel size for local feature extraction and Non-Local Blocks to extract the global features. The Non-Local Block has the ability to extract global features without using a huge number of parameters. The key idea behind the Non-Local Block is to apply matrix multiplications between features on the same feature maps. Results: We trained and validated the proposed method on the LIDC-IDRI dataset which contains 1018 computed tomography scans. We followed a rigorous procedure for experimental setup, namely tenfold cross-validation, and ignored the nodules that had been annotated by < 3 radiologists. The proposed method achieved state-of-the-art results with AUC = 95.62%, while significantly outperforming other baseline methods. Conclusions: Our proposed deep Local–Global network has the capability to accurately extract both local and global features. Our new method outperforms state-of-the-art architecture including Densenet and Resnet with transfer learning. [ABSTRACT FROM AUTHOR]

A disaster is a crisis situation that causes significant harm to humans, the environment, the economy, and impacts upon society's ability to cope both during the event and with the aftermath. A disaster may escalate rapidly to reach unpredicted consequences. They can be naturally occurring, or man-made, or a combination of the two. In this study we have focused on the Fukushima Daiichi nuclear power plant accident of 2011 that subsequently unleashed a floodgate of scientific activities, including basic and applied research, technology development, policy reform, inter-disciplinary collaboration, citizen science and re-strategization of research networks. In this context, a bibliometric study has been conducted on 5455 publications on the Fukushima disaster indexed by Scopus for the 2011–2017 period. The aim is to identify key papers in the field, the most substantive contributors to the literature, and to begin to map out the emergence of a body of knowledge resulting from the disaster. The findings present main national and international collaborators on Fukushima disaster related research, as well as an indication of the major research themes evolving over the immediate years following the event. This allows for a deeper understanding of the longitudinal impact of a disaster on society and upon the international research community and its deliberations. [ABSTRACT FROM AUTHOR]

Ovarian cancer is one of the prime causes of mortality in women. Diagnosis of ovarian cancer using ultrasonography is tedious as ovarian tumors exhibit minute clinical and structural differences between the suspicious and non-suspicious classes. Early prediction of ovarian cancer will reduce its growth rate and may save many lives. Computer-aided diagnosis (CAD) is a noninvasive method for finding ovarian cancer in its early stage which can avoid patient anxiety and unnecessary biopsy. This study investigates the efficacy of a novel CAD tool to characterize suspicious ovarian cancer using Radon-transformed nonlinear features. The obtained dimension of the extracted features is reduced using Relief-F feature selection method. In this study, we have employed the fuzzy forest-based ensemble classifier in contrast to the known crisp rule-based classifiers. The proposed method is evaluated using 469 (non-suspicious: 238, suspicious: 231) subjects and achieved a maximum 80.60 ± 0.5% accuracy, 81.40% sensitivity, 76.30% specificity with fuzzy forest, an ensemble fuzzy classifier using thirty-nine features. The proposed method is robust and reproducible as it uses maximum number subjects (469) as compared to state-of-the-art techniques. Hence, it can be used as an assisting tool by gynecologists during their routine screening. [ABSTRACT FROM AUTHOR]

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To perform a systematic review and meta-analysis of the diagnostic value of prospective ECG-gating coronary CT angiography in the diagnosis of coronary artery disease. A search of biomedical databases for English literature was performed to identify studies investigating the diagnostic value of 64- or more slice CT angiography with use of prospective ECG-gating in the diagnosis of coronary artery disease. Sensitivity, specificity, positive and negative predictive value estimates pooled across studies were tested using a fixed effects model. Fourteen studies met selection criteria for inclusion in the analysis. Pooled estimates and 95% confidence interval (CI) of sensitivity, specificity, positive and negative predictive value of prospective ECG-gating coronary CT angiography for diagnosis of significant coronary stenosis were 99% (95% CI: 98, 100%), 91% (95% CI: 88, 94%), 94% (95% CI: 91, 96%) and 99% (95% CI: 97, 100%), according to the patient-based assessment. The mean values of sensitivity, specificity, positive and negative predictive value of prospective ECG-gating coronary CT angiography were 95% (95% CI: 93, 96%), 95% (95% CI: 93, 95%), 88% (95% CI: 86, 90%), and 98% (95% CI: 97, 98%), according to vessel-based assessment; 92% (95% CI: 90, 93%), 97% (95% CI: 97, 98%), 84% (95% CI: 82, 86%), 99% (95% CI: 99, 99%), according to segment-based assessment, respectively. The mean effective dose was 3.3 mSv (95% CI: 2.3, 4.1 mSv) for the prospective ECG-gating coronary CT angiography. This analysis shows that for a predominantly male population with a high disease prevalence the use of coronary CT angiography with prospective ECG gating allows for a reduced radiation exposure without a sacrifice in diagnostic efficacy. [ABSTRACT FROM AUTHOR]

The article presents a system that can by employed for the automatic mass screenings of diabetic retinopathy (DR). Also cited are the four classes of DR including normal retina, proliferative diabetic retinopathy (PDR) and macular edema (ME), the proposed integrated DR index (IDRI), and a study on the support vector machine (SVM) classifier of different kernel functions like linear and radial basis function.

The article presents a study that reviewed the use of digital fundus images for the automated detection of diabetic retinopathy. Fundus images are based on hemmorrhages, blood vessel area, and microaneurysms. It concludes that algorithms have improved which are getting closer to the classification capabilities of human ophthalmologists.