RESUMEN
Ultrasound (US) of major salivary glands (MSG) evaluates echogenicity, border features and vascularization, with elastography, it can detect tissue elasticity and glandular fibrosis, related to inflammation in Primary Sjögren's syndrome (pSS). This study aimed to develop a novel technique by pixel analysis for evaluation and interpretation of elastography in MSG in pSS. A cross-sectional and observational multicenter study was conducted. The US of MSG performed in orthogonal planes in grayscale, Doppler, and shear-wave elastography. For elastography images of each gland were analyzed with the open-source program ImageJ to perform a pixel analysis. Statistical analysis was performed with the IBM-SPSS v25 program. Fifty-nine women with a mean age of 57.69 (23-83) years were recruited; pSS mean duration of 87 (5-275) months, and 12 healthy women without sicca symptoms as a control group with a mean age of 50.67 (42-60) years. Intragroup analysis showed p-values >0.05 between sicca symptoms, ocular/dryness tests, biopsy, US, and pixel analysis; correlation between Hocevar and pixel analysis was not found (rho < 0.1, p >0.5). MSG anatomical size was 41.7 ± 28.2 mm vs. 67.6 ± 8.8 mm (p ≤ 0.0001); unstimulated whole saliva flow rate was 0.80 ± 0.80 ml/5 min vs. 1.85 ± 1.27 ml/5 min (p = 0.016). The elastography values (absolute number of pixels) were 572.38 ± 99.21 vs. 539.69 ± 93.12 (p = 0.290). A cut-off point risk for pSS identified with less than 54% of red pixels in the global MSG mass [OR of 3.8 95% CI (1.01-15.00)]. Pixel analysis is a new tool that could lead to a better understanding of the MSG chronic inflammatory process in pSS.
RESUMEN
Depression is a common mental illness characterized by sadness, lack of interest, or pleasure. According to the DSM-5, there are nine symptoms, from which an individual must present 4 or 5 in the last two weeks to fulfill the diagnosis criteria of depression. Nevertheless, the common methods that health care professionals use to assess and monitor depression symptoms are face-to-face questionnaires leading to time-consuming or expensive methods. On the other hand, smart homes can monitor householders' health through smart devices such as smartphones, wearables, cameras, or voice assistants connected to the home. Although the depression disorders at smart homes are commonly oriented to the senior sector, depression affects all of us. Therefore, even though an expert needs to diagnose the depression disorder, questionnaires as the PHQ-9 help spot any depressive symptomatology as a pre-diagnosis. Thus, this paper proposes a three-step framework; the first step assesses the nine questions to the end-user through ALEXA or a gamified HMI. Then, a fuzzy logic decision system considers three actions based on the nine responses. Finally, the last step considers these three actions: continue monitoring through Alexa and the HMI, suggest specialist referral, and mandatory specialist referral.