Background: The painful shoulder is a relatively common clinical entity that may be attributed to a variety of pathologies, including partial rotator cuff tears. Conservative treatment or surgical intervention may be offered, depending on the extent of the partial tear and the degree of patient discomfort. Purpose: To apply ultrasound (US) imaging in order to evaluate the prevalence of partial rotator cuff tears in patients with painful shoulders. Material and Methods: Fifty-six patients (17 men, 39 women; mean age 53.7 years) were included in the study, with symptomatic impingement syndrome of the shoulder after having failed to respond to conservative treatment. All patients underwent US and magnetic resonance imaging (MRI) scans prior to surgical intervention. Results: Arthroscopy or mini-open surgery revealed 53 cases with partial tears of the rotator cuff and three with extensive tendinopathy. Both imaging modalities detected successfully 44 cases of partial tears of the supraspinatus tendon. US imaging yielded a sensitivity of 95.6%, a specificity of 70%, an accuracy of 91%, and a positive predictive accuracy of 93.6%. The corresponding values for MRI were 97.7%, 63.6%, 91%, and 91.7%, respectively. Conclusion: US imaging can be considered almost equally effective in detecting partial tears of the rotator cuff compared to MRI, particularly located in the area of the supraspinatus tendon. MRI may be reserved for doubtful or complex cases, in which delineation of adjacent structures is mandatory prior to surgical intervention.

The improved treatment of knee injuries critically relies on having an accurate and cost-effective detection. In recent years, deep-learning-based approaches have monopolized knee injury detection in MRI studies. The aim of this paper is to present the findings of a systematic literature review of knee (anterior cruciate ligament, meniscus, and cartilage) injury detection papers using deep learning. The systematic review was carried out following the PRISMA guidelines on several databases, including PubMed, Cochrane Library, EMBASE, and Google Scholar. Appropriate metrics were chosen to interpret the results. The prediction accuracy of the deep-learning models for the identification of knee injuries ranged from 72.5–100%. Deep learning has the potential to act at par with human-level performance in decision-making tasks related to the MRI-based diagnosis of knee injuries. The limitations of the present deep-learning approaches include data imbalance, model generalizability across different centers, verification bias, lack of related classification studies with more than two classes, and ground-truth subjectivity. There are several possible avenues of further exploration of deep learning for improving MRI-based knee injury diagnosis. Explainability and lightweightness of the deployed deep-learning systems are expected to become crucial enablers for their widespread use in clinical practice.

Micturating cystourethrography (MCU) is considered to be the gold-standard method used to detect and grade vesicoureteric reflux (VUR) and show urethral and bladder abnormalities. It accounts for 30-50% of all fluoroscopic examinations in children. Therefore, it is crucial to define and optimize the radiation dose received by a child during MCU examination, taking into account that children have a higher risk of developing radiation-induced cancer than adults. This study aims to quantify and evaluate, by means of thermoluminescence dosimetry (TLD), the radiation dose to the newborn and paediatric populations undergoing MCU using fluoroscopic imaging. Evaluation of entrance surface dose (ESD), organ and surface dose to specific radiosensitive organs was carried out. Furthermore, the surface dose to the co-patient, i.e. individuals helping in the support, care and comfort of the children during the examination, was evaluated in order to estimate the level of risk. 52 patients with mean age of 0.36 years who had undergone MCU using digital fluoroscopy were studied. ESD, surface doses to thyroid, testes/ovaries and co-patients were measured with TLDs. MCU with digital equipment and fluoroscopy-captured image technique can reduce the radiation dose by approximately 50% while still obtaining the necessary diagnostic information. Radiographic exposures were made in cases of the presence of reflux or of the difficulty in evaluating a finding. The radiation surface doses to the thyroid and testes are relatively low, whereas the radiation dose to the co-patient is negligible. The risks associated with MCU for patients and co-patients are negligible. The results of this study provide baseline data to establish reference dose levels for MCU examination in very young patients.

Several cases of vaccine-associated manifestations have been published including cases of inflammatory myositis. Herein, we comprehensively review the literature on the occasion of case of a woman with inflammatory myositis following COVID-19 vaccination. A 67-year-old woman presented with left arm edema, rash, and weakness after the 2^nd dose of the BTN162b2 vaccine. Raised muscle enzymes and inflammatory markers with muscle edema on MRI and myositis findings on the electromyogram established the diagnosis. She was successfully treated with methylprednisolone pulses, intravenous immunoglobulin, methotrexate, and hydroxychloroquine. Cases of inflammatory myositis, dermatomyositis, or interstitial lung disease with myositis-specific autoantibodies or myositis-associated autoantibodies within 12 weeks from SARS-CoV-2 vaccination were included. Cases with malignancy, prior or subsequent COVID-19 infection, preexisting myositis/interstitial lung disease (ILD)/dermatomyositis (DM), or other connective tissue diseases were excluded. From our search, 49 cases were identified (mean age: 56.55 + 17.17 years), 59% were women, while 12 patients received the ChAdOx1 vaccine, 27 the BNT162b2, 8 the mRNA-1273, 1 the DB15806, and 1 the Ad26.COV2.S (overall, 70% received mRNA vaccines). Muscle involvement was the most common manifestation (79.5%), followed by skin involvement (53%) and ILD (34.6%), which were more common in the m-RNA vaccinees. Muscle biopsy, MRI findings, and autoantibody profile varied significantly, while successful immunosuppressive treatment was applied in most cases. Inflammatory myositis after COVID-19 vaccination has been well documented worldwide. Current evidence in support of a pathogenic link is challenging due to significant variation in clinical manifestations, radiological, histopathological, and immunological features.

Background Disc degeneration quantification is important for monitoring the effects of new therapeutic methods, such as cell and growth factor therapy. Magnetic resonance (MR) image texture reflects biochemical and structural tissue properties and has been used for differentiating between normal and pathological status in a variety of medical applications. Purpose To investigate the suitability of textural descriptors for the quantification of intervertebral disc degeneration using conventional T2-weighted magnetic resonance images of the lumbar spine. Material and Methods A 3 Tesla scanner was used, and conventional T2- weighted MR images were obtained, and a total of 255 lumbar discs were analyzed. An atlas-based method was used for segmenting the disc regions from the images. A set of first and second order statistics describing texture of each region were calculated. The validity and reliability of these descriptors for disc degeneration severity quantification was tested through their correlation with patient age and qualitative clinical grading of degeneration severity. Texture quantification results were compared to a widely accepted method for disc degeneration quantification based on the measurement of disc's mean signal intensity. Results Out of the set of texture descriptors tested, two descriptors quantifying image intensity inhomogeneity, i.e. the grey level standard deviation and co-occurrence derived sum of squares displayed the strongest association to patient age and clinical grading of disc degeneration severity (P < 0.001). This is attributed to these inhomogeneity descriptors' capability to capture the progressive loss of nucleus-annulus distinction in the degenerative progress. Statistical analysis indicates that these descriptors can effectively separate between early stages of degeneration. Quantitative measurements are highly repeatable (intraclass correlation >0.98). Conclusion Inhomogeneity descriptors could be a valuable tool for tracking the evolution of disc degeneration and monitoring the response to treatment in a simple, precise and repeatable manner.

Hysterosalpingography (HSG) is an efficient radiological examination for the evaluation of the female reproductive tract. However, it involves unavoidable irradiation to the ovaries of women in childbearing age. Therefore, radiation dose optimisation is required in order to reduce the probability of the associated risks. This study attempts to: measure patient and staff doses, estimate the effective dose and radiation risk for HSG using digital fluoroscopic images. Thirty-seven patients with infertility were examined using two digital X-ray machines. Thermoluminescence dosimeters (TLD) were used to measure entrance surface dose (ESD) for patients and staff during the procedure. The mean ESD and thyroid surface dose of the patient were 3.60 and 0.17 mGy, respectively, while the mean ESD for the staff was 0.18 mGy per procedure. The patient overall risk for cancer and hereditary effects is 24 × 10⁻⁶, while the risk for the staff is negligible. HSG with fluoroscopic technique demonstrate improved dose characteristics, compared to the conventional radiographic-based technique, reducing the surface dose by a factor of 3, without compromising the diagnostic findings.