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Tuberculosis (TB) of the thyroid gland is extremely rare, even in areas where the prevalence of TB is very high. Only a few such cases have been reported in the literature. The clinical course may resemble toxic goiter or acute thyroiditis. We report the case of a 23-year-old male who presented with painless swelling of the thyroid gland associated with mild fever and weight loss. On ultrasound neck, a large echogenic solid lesion was seen. Fine-needle aspiration cytology was suggestive of colloid goiter, but histopathology study suggested TB of the thyroid gland along with colloid goiter. We report this case to emphasize that TB of the thyroid gland, although rare, should be kept in the differential diagnosis of thyroid nodules, especially in countries such as India which are endemic for TB. [ABSTRACT FROM AUTHOR]

Background: This study aimed to evaluate the characteristics of incidental findings (IFs) on computed tomography (CT) scans of trauma admissions, examine associations between IFs and gender and age-groups, and discuss the management strategies. Methods: The CT reports were retrospectively reviewed to evaluate IFs. Cases were divided into five age-groups (0--19, 20--39, 40--59, 60--79, ≥ 80). IFs were classified as "Group 1": congenital anomalies that do not require further investigation, nondegenerative/minor degenerative findings; "Group 2": findings that do not require immediate intervention, require outpatient follow-up/in case of symptoms; and "Group 3": findings that require immediate intervention/further investigation. Results: There were 2385 CT scans and 1802 incidental findings (IFs) in 783 trauma cases. CT scans with IFs constituted 50.2%. The percentage of IFs was 75.6% in males and 24.4% in females, and they occurred in 4.8%, 27.6%, 44.3%, 20.9%, and 2.4% of age groups 1 to 5, respectively. Group 1 had 34.6%, group 2 had 54.6%, and group 3 had 10.8% IFs. There was not any significant association between the classification and gender or age-groups. In terms of organs, IFs of the thyroid and gall bladder & bile ducts were significantly higher among females (P = 0.044 and P < 0.001, respectively), while IFs in the head & neck region were significantly higher in males (P < 0.001). Incidental findings in the kidney, liver, adrenal gland, and vascular structures differed significantly across age-groups (P < 0.05). Conclusion: There were no significant relationships between the classification of IFs and gender or age-groups. However, the distribution of IFs was significantly associated with gender and age-groups in terms of organs. Healthcare professionals should consider this relationship when following up and treating patients. [ABSTRACT FROM AUTHOR]

The article describes the case of a 70-year-old male diagnosed with primary tuberculosis (TB) of the thyroid gland. The patient presented with a large fluctuating right neck mass present for the previous 4 months. Topics covered include the findings of the contrast enhanced compute tomography of the neck, the treatment given to the patient and the variability of the presentation of the primary thyroid TB.

• Multi-classifying thyroid disease leads to enhanced diagnostic decision-making. • Ultrasound image modalities outperform CT scans regarding CAD detection. • Xception generalizes well to different medical image modalities. • Adoptions of CAD in the clinic need to earn confidence among doctors and patients. [Display omitted] Background and Objective: As one of the largest endocrine organs in the human body, the thyroid gland regulates daily metabolism. Early detection of thyroid disease leads to reduced mortality rates. The diagnosis of thyroid disease is usually made by radiologists and pathologists, which heavily relies on their experience and expertise. To mitigate human false-positive diagnostic rates, this paper proves that deep learning-driven techniques yield promising performance for automatic detection of thyroid diseases which offers clinicians assistance regarding diagnostic decision-making. Method: This research study is the first of its kind, which adopts two pre-operative medical image modalities for multi-classifying thyroid disease types (i.e., normal, thyroiditis, cystic, multi-nodular goiter, adenoma, and cancer). Using the current state-of-the-art performing deep convolutional neural network (CNN) architecture, this study builds a thyroid disease diagnostic model for distinguishing among the disease types. Results: The model obtains unprecedented performance for both medical image sets, and it reaches an accuracy of 0.972 and 0.942 for ultrasound images and computed tomography (CT) scans correspondingly. Conclusion: The experimental results illustrate that the selected CNN can be adapted to both image modalities, indicating the feasibility of the deep learning model and emphasizing its further applications in clinics. [ABSTRACT FROM AUTHOR]