Diagnostic performance of artificial intelligence in interpreting thyroid nodules on ultrasound images: a multicenter retrospective study.

Background: Thyroid nodules are commonly identified through ultrasound imaging, which plays a crucial role in the early detection of malignancy. The diagnostic accuracy, however, is significantly influenced by the expertise of radiologists, the quality of equipment, and image acquisition techniques. This variability underscores the critical need for computational tools that support diagnosis.

Explainable Automated TI-RADS Evaluation of Thyroid Nodules.

A thyroid nodule, a common abnormal growth within the thyroid gland, is often identified through ultrasound imaging of the neck. These growths may be solid- or fluid-filled, and their treatment is influenced by factors such as size and location. The Thyroid Imaging Reporting and Data System (TI-RADS) is a classification method that categorizes thyroid nodules into risk levels based on features such as size, echogenicity, margin, shape, and calcification.

FLAIR vascular hyperintensity-DWI mismatch and DWI-FLAIR mismatch ASPECTS for prediction of good outcome after recanalization in anterior circulation stroke; multicenter observational study.

Introduction: Acute ischemic stroke is a medical emergency caused by decreased blood flow to the brain, leading cause of long-term disability. Recanalization, one of the most concerning difficulties linked with intracranial arterial occlusion, has been used to reduce mortality in ischemic stroke treatment. The mismatch concepts MR PWI-DWI or DWI-FLAIR can help identify patients for thrombolysis.

Improving GAN Learning Dynamics for Thyroid Nodule Segmentation.

Thyroid nodules are lesions requiring diagnosis and follow-up. Tools for detecting and segmenting nodules can help physicians with this diagnosis. Besides immediate diagnosis, automated tools can also enable tracking of the probability of malignancy over time.