Sonography and electrophysiology were complementary for identifying ulnar nerve neuropathy in patients with leprosy, with clinical symptoms as the reference standard. This reinforces the role of sonography in the investigation of leprosy ulnar neuropathy.

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The protective function of the meniscus appears to be preserved in the presence of intrasubstance meniscal signal changes. Prevalent single tears and meniscal maceration were found to be associated with increased cartilage loss in the same compartment, especially at the PH.

OBJECTIVE. The purpose of this article is to validate both semiquantitative and quantitative ultrasound assessment of medial meniscal extrusion using MRI assessment as the reference standard. SUBJECTS AND METHODS. Ninety-three consecutive patients with chronic knee pain referred for knee MRI were evaluated by ultrasound and MRI on the same day. Two musculoskeletal radiologists assessed meniscal extrusion on ultrasound and MRI separately and independently and graded it semiquantitatively as follows: 0 (< 2 mm), 1 (≥ 2 mm and < 4 mm), and 2 (≥ 4 mm). Agreement between the ultrasound and MRI evaluations was determined using weighted kappa statistics. Intraclass correlation coefficients were used to evaluate agreement using the absolute values of extrusion (quantitative assessment). We further evaluated the diagnostic performance of ultrasound for the detection of medial meniscal extrusion using MRI as the reference standard. RESULTS. For semiquantitative grading, agreement between ultrasound and MRI was moderate for reader 1 (κ = 0.57) and substantial for reader 2 (κ = 0.64). Substantial agreement was found for both readers (intraclass correlation coefficients, 0.73 and 0.70) when comparing quantitative assessment of meniscal extrusion between ultrasound and MRI. Ultrasound showed excellent sensitivity (95% and 96% for each reader) and good specificity (82% and 70% for each reader) in the detection of meniscal extrusion. CONCLUSION. Ultrasound assessment of meniscal extrusion is reliable and can be used for both quantitative and semiquantitative assessment, exhibiting excellent diagnostic performance for the detection of meniscal extrusion compared with MRI.

The discipline of radiology and diagnostic imaging has evolved greatly in recent years. We have observed an exponential increase in the number of exams performed, subspecialization of medical fields, and increases in accuracy of the various imaging methods, making it a challenge for the radiologist to “know everything about all exams and regions”. In addition, imaging exams are no longer only qualitative and diagnostic, providing now quantitative information on disease severity, as well as identifying biomarkers of prognosis and treatment response. In view of this, computer-aided diagnosis systems have been developed with the objective of complementing diagnostic imaging and helping the therapeutic decision-making process. With the advent of artificial intelligence, “big data”, and machine learning, we are moving toward the rapid expansion of the use of these tools in daily life of physicians, making each patient unique, as well as leading radiology toward the concept of multidisciplinary approach and precision medicine. In this article, we will present the main aspects of the computational tools currently available for analysis of images and the principles of such analysis, together with the main terms and concepts involved, as well as examining the impact that the development of artificial intelligence has had on radiology and diagnostic imaging.