Ehlers-Danlos syndrome (EDS) is a rare, heterogeneous group of genetic connective tissue disorders that affect collagen proteins. Currently, they are classified into 13 subtypes, many of which share general characteristics such as thin, hyperextensible skin and joint hypermobility. Vascular Ehlers-Danlos syndrome (vEDS) is characterized by tissue fragility, which predisposes individuals to premature arterial, uterine, or intestinal rupture. In this case, a young female presented with proptosis, skin hyperelasticity, and multiple joint dislocations. On computed tomography angiography (CTA), a direct caroticocavernous fistula, along with multiple segments of narrowing and ectasia in the internal carotid arteries and vertebral arteries, were detected, leading to a diagnosis of vEDS. This case report highlights the importance of clinical evaluation and the role of imaging in detecting this rare condition.
Background: The COVID-19 pandemic has claimed numerous lives in the last three years. With new variants emerging every now and then, the world is still battling with the management of COVID-19. Purpose: To utilize a deep learning model for the automatic detection of severity scores from chest CT scans of COVID-19 patients and compare its diagnostic performance with experienced human readers. Methods: A deep learning model capable of identifying consolidations and ground-glass opacities from the chest CT images of COVID-19 patients was used to provide CT severity scores on a 25-point scale for definitive pathogen diagnosis. The model was tested on a dataset of 469 confirmed COVID-19 cases from a tertiary care hospital. The quantitative diagnostic performance of the model was compared with three experienced human readers. Results: The test dataset consisted of 469 CT scans from 292 male (average age: 52.30) and 177 female (average age: 53.47) patients. The standalone model had an MAE of 3.192, which was lower than the average radiologists' MAE of 3.471. The model achieved a precision of 0.69 [0.65, 0.74] and an F1 score of 0.67 [0.62, 0.71], which was significantly superior to the average reader precision of 0.68 [0.65, 0.71] and F1 score of 0.65 [0.63, 0.67]. The model demonstrated a sensitivity of 0.69 [95% CI: 0.65, 0.73] and specificity of 0.83 [95% CI: 0.81, 0.85], which was comparable to the performance of the three human readers, who had an average sensitivity of 0.71 [95% CI: 0.69, 0.73] and specificity of 0.84 [95% CI: 0.83, 0.85]. Conclusion: The AI model provided explainable results and performed at par with human readers in calculating CT severity scores from the chest CT scans of patients affected with COVID-19. The model had a lower MAE than that of the radiologists, indicating that the CTSS calculated by the AI was very close in absolute value to the CTSS determined by the reference standard.
Background: While the past decades have seen a rise in the number of cases diagnosed with cancer, breast cancer in particular has become the most frequently diagnosed cancer in women over the past decade. The figures for associated mortality are on a decline in most Western and developed nations, but in contrast they continue to remain high in transitional nations like India. Materials and Methods: After receiving approval by IEC and IRB, we set-up a prospective 2-year long screening programme combined with outreach camps to ensure representation of the larger population and include urban, rural and tribal population. Strict screening criteria were enforced and trained female paramedical staff were assigned to the camp for patient counselling and breast cancer awareness. Investigation was performed at the tertiary care institute utilising both full-field digital breast mammography and tomosynthesis. Biopsy was advised for highly suspicious lesions. Results: The study encompassed n=1017 Indian women and revealed that 39% (n=397) of them belonged to 41-50 years age group. BIRADS categorisation of the lesions revealed that while majority (57%; n=580) women had no detectable abnormality, nearly 22% (n=224) had lesions suspected to be benign while 10% (n=99) of them had lesions with a suspicion of high index of malignancy. 43% (n=437) of the populace had dense breasts (type-C). Most of the BIRADS-5 lesions (36/38) were confirmed as malignant on histopathology. Conclusion:We propose a model for screening mammography and also presents the results of this programme which we implemented to screen populace from a large and densely populated geographic region. The model was successful in being self-sustainable and received a good turnout on the back of community outreach breast awareness camps and by incentivizing the women by performing mammograms completely free of cost and also providing them reports.
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