Trans-nasal endoscopic and intra-oral combined approach for odontogenic cysts

Maxillary cysts are a common finding in maxillofacial surgery, dentistry and otolaryngology. Treatment is surgical; a traditional approach includes Caldwell-Luc and other intra-oral approaches. In this article, we analyse the outcomes of 9 patients operated on using a combined intra-oral and trans-nasal approach to the aforementioned disease. Although the number of patients is small, the good results of this study suggest that the combined approach might be a reliable treatment option

A snapshot of knowledge about oral cancer in italy: A 505 person survey

Objectives: Patients’ knowledge about oral squamous cell carcinoma (OSCC) plays an important role in primary prevention, early diagnosis, and prognosis and survival rate. The aim of this study was to assess OSCC awareness attitudes among general population in order to provide information for educational interventions. Methods: A survey delivered as a web-based questionnaire was submitted to 505 subjects (aged from 18 to 76 years) in Italy, and the answers collected were statistically analyzed. Information was collected about existence, incidence, features of lesions, risk factors of oral cancer, and self-inspection habits, together with details about professional reference figures and preventive behaviors. Results: Chi-square tests of independence with adjusted standardized residuals highlighted correlations between population features (age, gender, educational attainment, provenance, medical relationship, or previous diagnoses of oral cancer in family) and knowledge about oral cancer. Conclusions: Knowledge about OSCC among the Italian population is limited, and it might be advisable to implement nudging and sensitive customized campaigns in order to promote awareness and therefore improve the prognosis of this disease

Chronic critical illness: the price of survival

BACKGROUND: The evolution of the techniques used in the intensive care setting over the past decades has led on one side to better survival rates in patients with acute conditions and severely impaired vital functions. On the other side, it has resulted in a growing number of patients who survive an acute event, but who then become dependent on one or more life support techniques. Such patients are called chronically critically ill patients. MATERIALS & METHODS: No absolute definition of the disease is currently available, although most patients are characterized by the need for prolonged mechanical ventilation. Mortality rates are still high even after dismissal from intensive care unit (ICU) and transfer to specialized rehabilitation care settings. RESULTS: In recent years, some studies have tried to clarify the pathophysiological characteristics underlying chronic critical illness (CCI), a disease that is also characterized by severe endocrine and inflammatory impairments, partly accounting for the almost constant set of symptoms. DISCUSSION: Currently, no specific treatment is available. However, a strategic early therapeutic approach on ICU admission might try to prevent the progress of the acute disease towards chronic critical illness

Subspace Energy Monitoring for Anomaly Detection @Sensor or @Edge

The amount of data generated by distributed monitoring systems that can be exploited for anomaly detection, along with real time, bandwidth, and scalability requirements leads to the abandonment of centralized approaches in favor of processing closer to where data are generated. This increases the interest in algorithms coping with the limited computational resources of gateways or sensor nodes. We here propose two dual and lightweight methods for anomaly detection based on generalized spectral analysis. We monitor the signal energy laying along with the principal and anti-principal signal subspaces, and call for an anomaly when such energy changes significantly with respect to normal conditions. A streaming approach for the online estimation of the needed subspaces is also proposed. The methods are tested by applying them to synthetic data and real-world sensor readings. The synthetic setting is used for design space exploration and highlights the tradeoff between accuracy and computational cost. The real-world example deals with structural health monitoring and shows how, despite the extremely low computations costs, our methods are able to detect permanent and transient anomalies that would classically be detected by full spectral analysis

Non-vascular interventional procedures: effective dose to patient and equivalent dose to abdominal organs by means of dicom images and Monte Carlo simulation

This study evaluates X-ray exposure in patient undergoing abdominal extra-vascular interventional procedures by means of Digital Imaging and COmmunications in Medicine (DICOM) image headers and Monte Carlo simulation. The main aim was to assess the effective and equivalent doses, under the hypothesis of their correlation with the dose area product (DAP) measured during each examination. This allows to collect dosimetric information about each patient and to evaluate associated risks without resorting to in vivo dosimetry. The dose calculation was performed in 79 procedures through the Monte Carlo simulator PCXMC (A PC-based Monte Carlo program for calculating patient doses in medical X-ray examinations), by using the real geometrical and dosimetric irradiation conditions, automatically extracted from DICOM headers. The DAP measurements were also validated by using thermoluminescent dosimeters on an anthropomorphic phantom. The expected linear correlation between effective doses and DAP was confirmed with an R(2) of 0.974. Moreover, in order to easily calculate patient doses, conversion coefficients that relate equivalent doses to measurable quantities, such as DAP, were obtained

A Deep Learning Method for Optimal Undersampling Patterns and Image Recovery for MRI Exploiting Losses and Projections

Compressed Sensing was recently proposed to reduce the long acquisition time of Magnetic Resonance Imaging by undersampling the signal frequency content and then algorithmically reconstructing the original image. We propose a way to significantly improve the above method by exploiting a deep neural network to tackle both problems of frequency sub-sampling and image reconstruction simultaneously, thanks to the introduction of a new loss function to drive the training and the addition of a post-processing non-neural stage. Furthermore, we highlight how some of the quantities along the processing chain can be used as a proxy of the quality of the recovered image, thus allowing a self-assessment of the whole technique. All improvements hinge on the possibility of identifying constraints to which the final image must obey and suitably enforce them. The effectiveness of our approach is tested on real-world MRI acquisitions from the fastMRI public database and achieves an appreciable improvement in Peak Signal-to-Noise Ratio with respect to the original CS-based proposal with speed-up factors 4 and 8

Surgical anatomy of the facial nerve: from middle cranial fossa approach to endoscopic approach. A pictorial review.

Purpose: The pathology of the facial nerve is extremely varied and extensive knowledge of the surgical anatomy in different approaches is required to manage it. During the last 15 years, the development of endoscopic ear surgery has significantly changed anatomical concepts, introducing new surgical approaches. The aim of this review is to illustrate five different surgical approaches to the facial nerve: the endoscopic approach, the middle cranial fossa approach, two translabyrinthine approaches (one simple and one endoscopic-assisted) with decompression of the whole petrous portion of the facial nerve, and a transotic approach with temporal craniotomy. Methods: Representative cases of middle and/or inner ear pathologies, surgically treated at our ENT Department, were selected to illustrate each of the five different approaches involving the facial nerve throughout its course. Results: In all cases, the pathology was removed with effective decompression of the facial nerve. The surgical anatomy in each surgical approach is described and illustrated. Conclusions: Facial nerve surgery is challenging for ENT specialists. An excellent knowledge of facial nerve anatomy is needed to eradicate pathology, avoiding nerve injuries and providing a good outcome after surgery

Frequency reallocation based on cochlear place frequencies in cochlear implants: a pilot study

Purpose: The aim of this study is to evaluate speech perception outcomes after a frequency reallocation performed through the creation of an anatomically based map obtained with Otoplan®, a tablet-based software that allows the cochlear duct length to be calculated starting from CT images. Methods: Ten postlingually deafened patients who underwent cochlear implantation with MED-EL company devices from 2015 to 2019 in the Tertiary referral center University Hospital of Verona have been included in a retrospective study. The postoperative CT scans were evaluated with Otoplan®; the position of the intracochlear electrodes was obtained, an anatomical mapping was carried out and then it was submitted to the patients. All patients underwent pure tonal and speech audiometry before and after the reallocation and the audiological results were processed considering the Speech Recognition Threshold (SRT), the Speech Awareness Threshold (SAT) and the Pure Tone Average (PTA). The differences in the PTA, SAT and SRT values before and after the reallocation were determined. The results were statistically processed using the software Stata with a significance value of α < 0.05. Results: The mean values of SRT (61.25 dB versus 51.25 dB) and SAT (49 dB versus 41 dB) were significantly lower (p: 0.02 and p: 0.04, respectively) after the reallocation. No significant difference was found between PTA values (41.5 dB versus 39.25 dB; p: 0.18). Conclusions: Our preliminary results demonstrate better speech discrimination and rapid adaptation in implanted postlingually deaf patients after anatomic mapping and subsequent frequency reallocation

Training Binary Layers by Self-Shrinking of Sigmoid Slope: Application to Fast MRI Acquisition

Deep Neural Networks (DNN) have become popular and widespread because they combine computational power and flexibility, but they may present critical hyper-parameters that need to be tuned before the model can be trained. Recently, the use of trainable binary masks in the field of Magnetic Resonance Imaging (MRI) acquisition brought new state-of-the-art results, but with the disadvantage of introducing a bulky hyper-parameter, which tuning is usually time-consuming. We present a novel callback-based method that is applied during training and turns the tuning problem into a triviality, also bringing non-negligible performance improvements. We test our method on the fastMRI dataset

Low-power fixed-point compressed sensing decoder with support oracle

Approaches for reconstructing signals encoded with Compressed Sensing (CS) techniques, and based on Deep Neural Networks (DNNs) are receiving increasing interest in the literature. In a recent work, a new DNN-based method named Trained CS with Support Oracle (TCSSO) is introduced, relying the signal reconstruction on the two separate tasks of support identification and measurements decoding. The aim of this paper is to improve the TCSSO framework by considering actual implementations using a finite-precision hardware. Solutions with low memory footprint and low computation requirements by employing fixed-point notation and by reducing the number of bits employed are considered. Results using synthetic electrocardiogram (ECG) signals as a case study show that this approach, even when used in a constrained-resources scenario, still outperform current state-of-art CS approaches