Brain topography on adult ultrasound images: Techniques, interpretation, and image library

PADS-Net: GAN-based radiomics using multi-task network of denoising and segmentation for ultrasonic diagnosis of Parkinson disease

Parkinson disease (PD) is a prevalent neurodegenerative disorder, and its accurate diagnosis is crucial for timely intervention. We propose the PArkinson disease Denoising and Segmentation Network (PADS-Net), to simultaneously denoise and segment transcranial ultrasound images of midbrain for accurate PD diagnosis. The PADS-Net is built upon generative adversarial networks and incorporates a multi-task deep learning framework aimed at optimizing the tasks of denoising and segmentation for ultrasound images. A composite loss function including the mean absolute error, the mean squared error and the Dice loss, is adopted in the PADS-Net to effectively capture image details. The PADS-Net also integrates radiomics techniques for PD diagnosis by exploiting high-throughput features from ultrasound images. A four-branch ensemble diagnostic model is designed by utilizing two "wings" of the butterfly-shaped midbrain regions on both ipsilateral and contralateral images to enhance the accuracy of PD diagnosis. Experimental results demonstrate that the PADS-Net not only reduced speckle noise, achieving the edge-to-noise ratio of 16.90, but also attained a Dice coefficient of 0.91 for midbrain segmentation. The PADS-Net finally achieved an area under the receiver operating characteristic curve as high as 0.87 for diagnosis of PD. Our PADS-Net excels in transcranial ultrasound image denoising and segmentation and offers a potential clinical solution to accurate PD assessment.

Research advances and trends in anatomy from 2013 to 2023: A visual analysis based on CiteSpace and VOSviewer

As the cornerstone of medicine, the development of anatomy is related to many disciplines and fields and has received extensive attention from researchers. How to integrate and grasp the cutting-edge information in this field quickly is a challenge for researchers, so the aim of this study is to analyze research in anatomy using CiteSpace and VOSviewer in order to identify research hotspots and future directions. To offer a fresh viewpoint for assessing the academic influences of researchers, nations, or institutions on anatomy, and to examine the development of hotspots in anatomical study and to forecast future trends. A total of 4637 anatomy-related publications from 2013 to 2023 were collected from Web of Science Core Collection databases. Their temporal distribution, spatial distribution, cited authors, co-cited journals, keywords, and disciplinary connections in the literature were analyzed using CiteSpace and VOSviewer, and a knowledge graph was constructed. The temporal distribution shows a general fluctuation in the amount of literature published from 2013 to 2023. In spatial distribution, the total number of published articles was highest in the United States, the United Kingdom, and China, the United States leading. Tubbs, Rhoton, Iwanaga, and LaPrade are important authors in anatomy. Clinical Anatomy, Surgical and Radiologic Anatomy, and Journal of Anatomy were the most highly cited journals. Analysis of keywords and citation emergence showed that the research hotspots and trends in anatomy focused mainly on anatomy education, digital technology, and surgical management. At the same time, anatomy showed a trend toward multidisciplinary crossover, developing closer relationships with molecular biology, immunology, and clinical medicine. Current research in anatomy focuses on innovative reform of the educational model and the application and promotion of digital technology. Also, multidisciplinary cross-fertilization is an inevitable trend for the future development of anatomy.

Neuro Point-of-Care Ultrasound

As the scope of point-of-care ultrasound (POCUS) expands in clinical medicine, its application in neurological applications offers a non-invasive, bedside diagnostic tool. With historical insights, detailed techniques and clinical applications, the chapter provides a comprehensive overview of neurology-based POCUS. It examines the applications, emphasizing its role when traditional neuroimaging is inaccessible or unsafe as well advocating for its use as an adjunctive tool, rather than a replacement of advanced imaging. The chapter covers a range of uses of neuro POCUS including assessment of midline shift, intracranial hemorrhage, hydrocephalus, vasospasm, intracranial pressure, cerebral circulatory arrest, and ultrasound-guided lumbar puncture.

Transcranial ultrasonography to detect intracranial pathology: A systematic review and meta-analysis

BACKGROUND AND PURPOSE

Transcranial ultrasonography (TCU) can be a useful diagnostic tool in evaluating intracranial pathology in patients with limited or delayed access to routine neuroimaging in critical care or austere settings. We reviewed available literature investigating the diagnostic utility of TCU for detecting pediatric and adult patient's intracranial pathology in patients with intact skulls and reported diagnostic accuracy measures.

METHODS

We performed a systematic review of PubMed^® , Cochrane Library, Embase^® , Scopus^® , Web of Science™, and Cumulative Index to Nursing and Allied Health Literature databases to identify articles evaluating ultrasound-based detection of intracranial pathology in comparison to routine imaging using broad Medical Subject Heading sets. Two independent reviewers reviewed the retrieved articles for bias using the Quality Assessment of Diagnostic Accuracy Studies tools and extracted measures of diagnostic accuracy and ultrasound parameters. Data were pooled using meta-analysis implementing a random-effects approach to examine the sensitivity, specificity, and accuracy of ultrasound-based diagnosis.

RESULTS

A total of 44 studies out of the 3432 articles screened met the eligibility criteria, totaling 2426 patients (Mean age: 60.1 ± 14.52 years). We found tumors, intracranial hemorrhage (ICH), and neurodegenerative diseases in the eligible studies. Sensitivity, specificity, and accuracy of TCU and their 95% confidence intervals were 0.80 (0.72, 0.89), 0.71 (0.59, 0.82), and 0.76 (0.71, 0.82) for neurodegenerative diseases; 0.88 (0.74, 1.02), 0.81 (0.50, 1.12), and 0.94 (0.92, 0.96) for ICH; and 0.97 (0.92, 1.03), 0.99 (0.96, 1.01), and 0.99 (0.97, 1.01) for intracranial masses. No studies reported ultrasound presets.

CONCLUSIONS

TCU has a reasonable sensitivity and specificity for detecting intracranial pathology involving ICH and tumors with clinical applications in remote locations or where standard imaging is unavailable. Future studies should investigate ultrasound parameters to enhance diagnostic accuracy in diagnosing intracranial pathology.