Aim The aim of this article was to evaluate uterocervical angle (UCA) and cervical length (CL) measured at 16 to 24 weeks of gestation using transvaginal sonography (TVS) as predictors of spontaneous preterm birth. Methods In this prospective study, TVS was performed in 159 primigravidas with a singleton, uncomplicated pregnancy at 16 to 24 weeks of gestation to measure the anterior UCA and CL. All the cases were followed until labor to document gestational age at delivery. Results The risk of spontaneous preterm birth was higher in women with obtuse UCA (>95 degrees) with sensitivity of 86.7%, specificity of 93.0%, positive predictive value of 83.0%, negative predictive value of 94.6%, and p-value of <0.001. The difference between the means was statistically significant (p-value < 0.001). UCAs ≥105degrees and 95 to 105 degrees were found to be significantly associated with spontaneous preterm births at <34 weeks and 34 to 37 weeks, respectively. CL <2.5 cm was found to predict spontaneous preterm births at <37 weeks with sensitivity of 31.1%, specificity of 95.6%, and p-value of <0.001. UCA was found to be a better predictor of spontaneous preterm birth with a higher coefficient of variation (56.4%) when compared with CL (16.9%). Conclusions UCA proved to be a novel ultrasound parameter that can serve as a better predictor of spontaneous preterm births in comparison to CL. A strong correlation exists between obtuse UCA and a risk of spontaneous preterm birth.

Objectives To describe the extent and imaging findings of COVID-associated rhino-orbital-cerebral mucormycosis on magnetic resonance imaging and to evaluate the value of MRI severity score in grading the extent of involvement. Methods Proven cases of ROCM with a history of concurrent or recently (<6 weeks) treated COVID-19 underwent MRI at the initial presentation. Findings were charted for each anatomical structure and the extent of involvement was scored for sinonasal, extra-sinus soft tissues, orbits, and brain. MR severity score was defined by summing up the individual scores of each compartment (sinonasal 20, orbital 20, soft tissue 10, and brain 10) and a total score out of 60 was assigned. Results A total of 47 patients were included in our study with variable involvement of sinonasal compartment ( n = 43), extra-sinus soft tissue ( n = 25), orbits ( n = 23), and brain ( n = 17). In the sinonasal compartment, T2, DWI, and post-contrast T1 were the most useful sequences. A significantly higher mean sinonasal score was associated with mortality ( p = 0.007). In the orbits, a combination of STIR (orbital fat and extraconal muscles), DWI (optic nerves), and post-contrast images (superior ophthalmic vein) were the most accurate sequences. A higher mean orbital score was associated with vision loss ( p = 0.001). Patients with uncontrolled diabetes had greater extent of cranial involvement. Conclusion A combination of magnetic resonance sequences is required to correctly evaluate the involvement of individual structures and thus to assign the correct MR scoring. The proposed MR severity score can effectively and objectively evaluate the severity of COVID-associated ROCM.

The optical coherence tomography (OCT) is useful in viewing cross-sectional retinal images and detecting various forms of retinal disorders from those images. Image processing methods and computational algorithms underlying this paper try to detect the shadowing region beneath exudates automatically. This paper presents a novel method for detecting hard exudates from retinal OCT images, often associated with macular edema near or within the outer plexiform layer. In this paper, an algorithm can automatically detect the presence of hard exudates in retinal OCT images, and these exudates appear as highly reflective spots. Still, they do not appear as noticeable bright spots because of their minute sizes in predevelopment phases. In the proposed work, we are using a method to detect the presence of hard exudates by analyzing their shadowing effect instead of focusing on brightness spots. The raster scanning operation is performed by traversing the retina horizontally, and noting up any change in normalized summation of brightness intensity (summing up the intensity from top to bottom retinal layers and normalized concerning retinal width) leads to the detection of minute as well as the presence for the detection of large exudates detection by differentiating this brightness intensity graph. The shadow region helps identify the hard exudates; in our proposed method, the output for three input images has been shown. There is an excellent agreement between the results generated by the proposed algorithm and the diagnostic opinion made by the ophthalmologist. The proposed method automatically detects the hard exudates using shadow regions, and it does not need any parameter settings or manual intervention. It can yield significant results by giving the position of shadow regions, which indicates the presence of exudates.