ObjectiveTo evaluate the significance of Eustachian tube (ET) angles and ET pretympanic diameter on high resolution computed tomography (HRCT) Temporal bone in patients with chronic otitis media (COM).MethodsA retrospective study was carried out at Tertiary care centre. Group A included 92 ears with COM (38 patients with bilateral COM and 16 with unilateral COM); and Group B included 108 normal ears (54 patients with bilateral normal ears). Reid plane-ET angle, Tubotympanic angle and the ET pretympanic diameter was evaluated by HRCT temporal bone, and compared in the two groups. Patients with chronic otitis media (Group A) were subdivided into Group A1 (Blocked ET) and Group A2 (Patent ET). The parameters were evaluated and compared in the subgroups too.ResultsThe mean Reid plane-ET angle and Tubotympanic angle in Group A was 25.41 ± 2.57 and 148.12 ± 3.43 respectively; whereas in Group B it was 27.56 ± 3.62 and 145.14 ± 4.34 respectively. Reid plane-ET angle was significantly less in patients with COM and Tubotympanic angle was significantly more obtuse in COM patients. ET pretympanic diameter was (5.37 ± 2.10) mm in Group A and (6.47 ± 2.40) mm in Group B. It was significantly less in patients with COM. A significant correlation was found between the ET patency and the two ET parameters (Reid plane-ET angle and pretympanic diameter).ConclusionsEustachian tube angles in adults may play a significant role in the etiology of chronic otitis media. Decrease in Reid plane-ET angle and pretympanic diameter on HRCT temporal bone can be used to predict ET dysfunction and to plan the surgical management of chronic otitis media.
<p class="abstract"><strong>Background:</strong> <span lang="EN-GB">To evaluate the olfactory fossae depth according to the Keros' classification on pre functional endoscopic sinus surgery (pre-FESS) and determine the incidence and degree of asymmetry in the height of the ethmoid roof in the population of western Maharashtra</span><span lang="EN-IN">. </span></p><p class="abstract"><strong>Methods:</strong> <span lang="EN-GB">Retrospective analysis of 200 multidetector CT studies (400 sides) of paranasal sinuses performed in between January to August, 2017</span><span lang="EN-IN">. </span></p><p class="abstract"><strong>Results:</strong> <span lang="EN-GB">According to the Keros’ classification, the incidence of different types of olfactory fossae was as follows: type I: 18.5%, type II: 74.5% and type III: 7%. Asymmetry in the ethmoid roof height was found in 11.5% of cases</span><span lang="EN-IN">. </span></p><p class="abstract"><strong>Conclusions:</strong> <span lang="EN-GB">Keros’ type II was the commonest followed by type I and type III. There was asymmetry in the depth of the olfactory fossae in 11.5% cases. There was no significant gender predilection as far as type and asymmetry were considered</span><span lang="EN-IN">.</span></p>
Drowsiness and intoxication are major contributors to car accidents, posing significant risks to road safety. The implementation of effective drowsiness detection technologies could help prevent numerous fatal accidents by alerting fatigued drivers in advance. Various techniques can be adopted to monitor driver attentiveness while driving and provide timely notifications. In the context of self-driving cars, sensors play a crucial role in identifying signs of sleepiness, anger, or extreme emotional changes in drivers. These sensors continuously monitor facial expressions and detect facial landmarks to assess the driver's state and ensure safe driving. Once such changes are detected, the system promptly assumes control of the vehicle, reducing its speed, and alerts the driver through alarms to draw attention to the situation. To enhance accuracy, the proposed system integrates with the vehicle's electronics, tracking its statistics and providing precise results. In this research, we have implemented real-time image segmentation and drowsiness detection using machine learning methodologies. Specifically, an emotion detection method based on Support Vector Machines (SVM) has been employed, utilizing facial expressions. The algorithm underwent testing under varying luminance conditions and exhibited superior performance compared to existing research, achieving an 83.25% accuracy rate in detecting facial expression changes.
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