Factors Related to Intra-Tendinous Morphology of Achilles Tendon in Runners

The purpose of this study was to determine and explore factors (age, sex, anthropometry, running and injury/pain history, tendon gross morphology, neovascularization, ankle range of motion, and ankle plantarflexor muscle endurance) related to intra-tendinous morphological alterations of the Achilles tendon in runners. An intra-tendinous morphological change was defined as collagen fiber disorganization detected by a low peak spatial frequency radius (PSFR) obtained from spatial frequency analysis (SFA) techniques in sonography. Ninety-one runners (53 males and 38 females; 37.9 ± 11.6 years) with 8.8 ± 7.3 years of running experience participated. Height, weight, and waist and hip circumferences were recorded. Participants completed a survey about running and injury/pain history and the Victorian Institute of Sport Assessment-Achilles (VISA-A) survey. Heel raise endurance and knee-to-wall composite dorsiflexion were assessed. Brightness-mode (B-mode) sonographic images were captured longitudinally and transversely on the Achilles tendon bilaterally. Sonographic images were analyzed for gross morphology (i.e., cross-sectional area [CSA]), neovascularization, and intra-tendinous morphology (i.e., PSFR) for each participant. The factors associated with altered intra-tendinous morphology of the Achilles tendon were analyzed using a generalized linear mixed model. Multivariate analyses revealed that male sex was significantly associated with a decreased PSFR. Additionally, male sex and the presence of current Achilles tendon pain were found to be significantly related to decreased PSFR using a univariate analysis. Our findings suggested that male sex and presence of current Achilles tendon pain were related to intra-tendinous morphological alterations in the Achilles tendon of runners

Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms

Currently, diagnosis of skin diseases is based primarily on visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and in conjunction with decision-theoretic approaches used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue

Objective analysis of neck muscle boundaries for cervical dystonia using ultrasound imaging and deep learning

Objective: To provide objective visualization and pattern analysis of neck muscle boundaries to inform and monitor treatment of cervical dystonia. Methods: We recorded transverse cervical ultrasound (US) images and whole-body motion analysis of sixty-one standing participants (35 cervical dystonia, 26 age matched controls). We manually annotated 3,272 US images sampling posture and the functional range of pitch, yaw, and roll head movements. Using previously validated methods, we used 60-fold cross validation to train, validate and test a deep neural network (U-net) to classify pixels to 13 categories (five paired neck muscles, skin, ligamentum nuchae, vertebra). For all participants for their normal standing posture, we segmented US images and classified condition (Dystonia/Control), sex and age (higher/lower) from segment boundaries. We performed an explanatory, visualization analysis of dystonia muscle-boundaries. Results: For all segments, agreement with manual labels was Dice Coefficient (64±21%) and Hausdorff Distance (5.7±4 mm). For deep muscle layers, boundaries predicted central injection sites with average precision 94±3%. Using leave-one-out cross-validation, a support-vector-machine classified condition, sex, and age from predicted muscle boundaries at accuracy 70.5%, 67.2%, 52.4% respectively, exceeding classification by manual labels. From muscle boundaries, Dystonia clustered optimally into three sub-groups. These sub-groups are visualized and explained by three eigen-patterns which correlate significantly with truncal and head posture. Conclusion: Using US, neck muscle shape alone discriminates dystonia from healthy controls. Significance: Using deep learning, US imaging allows online, automated visualization, and diagnostic analysis of cervical dystonia and segmentation of individual muscles for targeted injection. The dataset is available (DOI: 10.23634/MMUDR.00624643)

An unusual unifocal presentation of Castleman’s disease in a young woman with a detailed description of sonographic findings to reduce diagnostic uncertainty: a case report

Background: Castleman’s disease is a rare lymphoproliferative disorder. It typically presents as mediastinal masses and causes a wide range of clinical symptoms. Histologically, Castleman’s disease is classified as either a hyalinic vascular or plasma cell variant. The prognosis mainly depends on the histological type and broadly varies. We herein report our sonographic findings in a patient with Castleman’s disease, including gray-scale ultrasonography, color Doppler ultrasonography, and sonoelastography ultrasonography, which have not been previously reported in the literature. These findings allowed for a preoperative diagnosis and avoidance of overly aggressive therapy. Case presentation: A 28-year-old European female patient with unicentric Castleman’s disease of hyalinic vascular type (HV) restricted to the axilla was referred to us because of a 4-month history of a painless, solitary mass located in the left axilla. The patient’s medical history was unremarkable. Conclusion: Castleman’s disease is a pathologic entity of unknown etiology and pathogenesis. In this case report of unicentric HV-type CD, we demonstrate that typical sonographic findings can lead to a preoperative diagnosis of Castleman’s disease. Core needle biopsy usually allows for a final diagnosis and helps to avoid unnecessary operations and overtreatment

A New Methodology of Viewing Extra-Axial Fluid and Cortical Abnormalities in Children with Autism via Transcranial Ultrasonography

Background: Autism spectrum disorders (ASDs) are developmental conditions of uncertain etiology which have now affected more than 1% of the school-age population of children in many developed nations. Transcranial ultrasonography (TUS) via the temporal bone appeared to be a potential window of investigation to determine the presence of both cortical abnormalities and increased extra-axial fluid (EAF). Methods: TUS was accomplished using a linear probe (10–5 MHz). Parents volunteered ASD subjects (N = 23; males 18, females 5) for evaluations (mean = 7.46 years ± 3.97 years), and 15 neurotypical siblings were also examined (mean = 7.15 years ± 4.49 years). Childhood Autism Rating Scale (CARS2(®)) scores were obtained and the ASD score mean was 48.08 + 6.79 (Severe). Results: Comparisons of the extra-axial spaces indicated increases in the ASD subjects. For EAF we scored based on the gyral summit distances between the arachnoid membrane and the cortical pia layer (subarachnoid space): (1) <0.05 cm, (2) 0.05–0.07 cm, (3) 0.08–0.10 cm, (4) >0.10 cm. All of the neurotypical siblings scored 1, whereas the ASD mean score was 3.41 ± 0.67. We also defined cortical dysplasia as the following: hypoechoic lesions within the substance of the cortex, or disturbed layering within the gray matter. For cortical dysplasia we scored: (1) none observed, (2) rare hypoechogenic lesions and/or mildly atypical cortical layering patterns, (3) more common, but separated areas of cortical hypoechogenic lesions, (4) very common or confluent areas of cortical hypoechogenicity. Again all of the neurotypical siblings scored 1, while the ASD subjects’ mean score was 2.79 ± 0.93. Conclusion: TUS may be a useful screening technique for children at potential risk of ASDs which, if confirmed with repeated studies and high resolution MRI, provides rapid, non-invasive qualification of EAF, and cortical lesions