Search

Fournier's gangrene (FG) is a serious pathology of the soft tissues and fascia of the perineum and genital region with a high morbidity and mortality rate. In recent years, the SGLT-2 inhibitor oral antidiabetic has been related to this entity. According to the new warnings from the main drug agencies, a compilation of cases has been initiated to establish or deny a possible causal relationship. Most of these cases have been reported in men. However, it is important not to underestimate this entity in the gynecological field, since it is extremely serious and requires intense and rapid aggressive treatment based on surgery and empiric antibiotherapy. Later, some cares are needed to involve surgical reconstruction of the defects introduced by debridement. As a result of the low incidence of FG, clinical trials' data may be insufficient to robustly assess this issue because of the limited numbers of participants. Real-world evidence may help to clarify the association between SGLT2i and FG. The aim of this review is to describe and compare the reported cases of GF in diabetic women who received SGLT2 inhibitors as antiglycemic agents.

Background: The possible presence of malignant adnexal mass should be considered during pregnancy. For this reason, it is important to keep in mind such possibility while performing routine obstetric ultrasounds to diagnose asymptomatic ovarian cancer in the early stages., Case Presentation: 27-year-old pregnant patient with a known adnexal tumour occurring at week 20 and enlarged supraclavicular lymph nodes of 3 cm size who was diagnosed with metastases from low-grade papillary serous ovarian carcinoma. The patient, obstetricians, neonatologists and oncologists agreed on initiating neoadjuvant chemotherapy and performing an elective C-section at week 34. She gave birth to a female infant weighing 2040 g who is currently in good health, and continues receiving follow-up care by a medical oncologist., Conclusions: An early diagnosis of gynaecologic malignancies during pregnancy is of critical importance because, although they are very rare, managing and treating carcinomas at an early stage allow us to increase maternal and fetal well-being and to offer more alternatives to our patients.

Confusion matrices have been used as a tool for the analysis of speech perception or human speech recognition (HSR) for decades. However, they are rarely employed in automatic speech recognition (ASR) mainly due to the lack of a systematic procedure for their exploration. The generalization of formal concept analysis employed in this paper provides a conceptual interpretation of confusion matrices that enables the analysis of the structure of confusions for both human and machine performances. Generalized formal concept analysis transforms confusion matrices into ordered lattices of confusion events, supporting classic results in HSR that identify a hierarchy of virtual articulatory-acoustic channels. Translating this technique into ASR, a detailed map of the relationships among the speech units employed in the system can be traced to make different sources of confusions apparent: the influence of the lexicon, segmentation errors, dialectal variations or limitations of the feature extraction procedures, among others.

Manual segmentation is often used for evaluation of automatic or semi-automatic segmentation. The purpose of this paper is to describe the inter and intraindividual variability, the dubiety of manual segmentation as a gold standard and to find reasons for the discrepancy. We realized two experiments. In the first one ten ENT surgeons, ten medical students and one engineer outlined the right maxillary sinus and ethmoid sinuses manually on a standard CT dataset of a human head. In the second experiment two participants outlined maxillary sinus and ethmoid sinuses five times consecutively. Manual segmentation was accomplished with custom software using a line segmentation tool. The first experiment shows the interindividual variability of manual segmentation which is higher for ethmoidal sinuses than for maxillary sinuses. The variability can be caused by the level of experience, different interpretation of the CT data or different levels of accuracy. The second experiment shows intraindividual variability which is lower than interindividual variability. Most variances in both experiments appear during segmentation of ethmoidal sinuses and outlining hiatus semilunaris. Concerning the inter and intraindividual variances the segmentation result of one manual segmenter could not directly be used as gold standard for the evaluation of automatic segmentation algorithms.

Segmentation of medical image data is getting more and more important over the last years. The results are used for diagnosis, surgical planning or workspace definition of robot-assisted systems. The purpose of this paper is to find out whether manual or semi-automatic segmentation is adequate for ENT surgical workflow or whether fully automatic segmentation of paranasal sinuses and nasal cavity is needed. We present a comparison of manual and semi-automatic segmentation of paranasal sinuses and the nasal cavity. Manual segmentation is performed by custom software whereas semi-automatic segmentation is realized by a commercial product (Amira). For this study we used a CT dataset of the paranasal sinuses which consists of 98 transversal slices, each 1.0 mm thick, with a resolution of 512 x 512 pixels. For the analysis of both segmentation procedures we used volume, extension (width, length and height), segmentation time and 3D-reconstruction. The segmentation time was reduced from 960 minutes with manual to 215 minutes with semi-automatic segmentation. We found highest variances segmenting nasal cavity. For the paranasal sinuses manual and semi-automatic volume differences are not significant. Dependent on the segmentation accuracy both approaches deliver useful results and could be used for e.g. robot-assisted systems. Nevertheless both procedures are not useful for everyday surgical workflow, because they take too much time. Fully automatic and reproducible segmentation algorithms are needed for segmentation of paranasal sinuses and nasal cavity.

Functional endoscopic sinus surgery (FESS) is a minimal invasive approach adopted in case of chronic sinusitis (inflammation of the paranasal sinuses). The paranasal sinuses are hollow structures within the bones surrounding the nasal cavity. During FESS the surgeon moves the endoscope and other surgical instruments within the nasal cavity following specific paths to approach each one of the paranasal sinuses. The purpose of this study was to reconstruct these paths to access the paranasal sinuses using volumetric CT data. The results will be used for Finite Element modeling and simulations for Robot Assisted Endonasal Surgery.