Visual Fixation of Skull-Vibration-Induced Nystagmus in Patients with Peripheral Vestibulopathy.

Nystagmus induced by applying an intense vibratory stimulus to the skull (SVIN) indicates vestibular functional asymmetry. In unilateral vestibular loss, a 100 Hz bone-conducted vibration given to either mastoid immediately causes a primarily horizontal nystagmus. The test is performed in darkness to avoid visual fixation (VF) but there are no data about how much VF affects the often-intense SVIN. The aim is to analyze the amount of reduction in SVIN when VF is allowed during testing. Thus, all patients seen in a tertiary hospital for vertigo or dizziness with positive SVIN were included. SVIN was recorded for 10 s for each condition: without VF (aSVINwo) and with VF (aSVINw). We obtained an aSVINwo and an aSVINw as average slow-phase velocities (SPV) without and with VF. VF index (FISVIN) was calculated as the ratio of SPV. Among the 124 patients included, spontaneous nystagmus (SN) was found in 25% and the median slow phase velocity (mSPV) (without VF) of SN was 2.6 ± 2.4°/s. Mean FISVIN was 0.27 ± 0.29. FISVIN was 0 in 42 patients, and FISVIN between 0 and 1 was found in 82 (mean FISVIN 0.39 ± 0.02). Fixation suppression was found in all patients with SVIN in cases of peripheral vestibulopathy. FISVIN clearly delineates two populations of patients: with or without a complete visual reduction in nystagmus.

A deep learning model for prognosis prediction after intracranial hemorrhage.

BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) is a common life-threatening condition that must be rapidly diagnosed and treated. However, there is still a lack of consensus regarding treatment, driven to some extent by prognostic uncertainty. While several prediction models for ICH detection have already been published, here we present a deep learning predictive model for ICH prognosis. METHODS: We included patients with ICH (n = 262), and we trained a custom model for the classification of patients into poor prognosis and good prognosis, using a hybrid input consisting of brain CT images and other clinical variables. We compared it with two other models, one trained with images only (I-model) and the other with tabular data only (D-model). RESULTS: Our hybrid model achieved an area under the receiver operating characteristic curve (AUC) of .924 (95% confidence interval [CI]: .831-.986), and an accuracy of .861 (95% CI: .760-.960). The I- and D-models achieved an AUC of .763 (95% CI: .622-.902) and .746 (95% CI: .598-.876), respectively. CONCLUSIONS: The proposed hybrid model was able to accurately classify patients into good and poor prognosis. To the best of our knowledge, this is the first ICH prognosis prediction deep learning model. We concluded that deep learning can be applied for prognosis prediction in ICH that could have a great impact on clinical decision-making. Further, hybrid inputs could be a promising technique for deep learning in medical imaging.

Instagram's Influence on Radiology Today: Reviewing the Evolving Educational Journey from a Hospital to National Societies.

We define social media as an interactive online platform that allows users to communicate and exchange knowledge. Educational and medical profiles have slowly emerged on different social media platforms, helping to teach about and publicize diverse aspects of medicine. Radiology is one of the specialties that could potentially benefit the most from social media, as the radiologist tends to have little outside-the-hospital representation. Progressively, audiovisual content has been gaining ground on social networks: Facebook, Twitter, Instagram, Youtube, TikTok, etc. Instagram appears to be ideally suited for radiology given its image-based nature. In addition, Instagram can also be used as a tool to help radiologists share and discuss radiological images, improve communication with clinicians and patients, advertise themselves and their specialty, and humanize their profession. Nevertheless, legal matters and privacy issues should always be taken into account when using these tools. In this overview, we describe the development of social networks and communication tools in our own radiology department, focusing especially on our Instagram account, as it has had a wide impact on our hospital and radiology residents around the country. We will also provide a summary of the various social media platforms used for radiology education along with their pros and cons, including useful tips for safe and efficient use.

Prevalence of D. fragilis infection in the household contacts of a group of infected patients.

INTRODUCTION: The results of a study on the household contacts of patients with D. fragilis infection are presented. METHODS: A prospective, descriptive study was carried out on all Dientamoeba fragilis-infected patients treated at the Tropical Medicine Unit of Hospital Universitario Central de Asturias between 2012- 2017 and their household contacts. Three stool samples per patient and three stool samples from each of their household contacts were concentrated and analysed. Polymerase chain reaction (PCR) was used to detect the presence of D. fragilis in all stool samples. Co-infection with E. vermicularis was studied in both groups. Patients and contacts who failed to deliver one or more samples for diagnosis and patients without household contacts were excluded. RESULTS: 44 Patients infected with D. fragilis, as well as their 97 household contacts were enrolled. 50.5% of household contacts had a positive PCR for D. fragilis. 20 were also coinfected with E. vermicularis. The presence of infection was significantly more frequent in patients with children (34/15 versus 24/24; p= 0.064; OR: 2.267 [0.988-5.199]), E. vermicularis infection in the children being 20/29 versus 0/48 (p=0.0001), and in another family member being 29/20 versus 15/33 (p=0.008; OR: 3.190 [1.384-7.352]). CONCLUSIONS: The prevalence of D. fragilis infection in household contacts was high. It was associated with the presence of children in the family nucleus and coinfection with E. vermicularis irrespective of gender, age, rural area or contact with animals.