Ultrasound assessment of Achilles enthesitis: a dedicated training program.

Objective: To describe an intensive and multimodal ultrasound (US) training program focused on Achilles enthesitis and to illustrate the learning curve of trainees without experience.

Methods: Three medical students (trainees) and two rheumatologists experienced in musculoskeletal US (trainers) were involved in the training program, which encompassed one preliminary theoretical-practical meeting and five scanning sessions (two patients per session). The students and one expert performed the US examination of the Achilles enthesis bilaterally.

Atypical Endometrial Hyperplasia and Concurrent Cancer: A Comprehensive Overview on a Challenging Clinical Condition.

The present review regarding atypical endometrial hyperplasia (AEH) focused on the main debated factors regarding this challenging clinical condition: (i) predictive variables of occult endometrial cancer (EC); (ii) the rate of EC underestimation according to different endometrial sampling methods; and (iii) the appropriateness of lymph node status assessment. When cancer is detected, approximately 90% of cases include low-risk EC, although intermediate/high-risk cases have been found in 10-13% of women with cancer. Older age, diabetes, high BMI, and increased endometrial thickness are the most recurrent factors in women with EC.

HPV-Negative Adenocarcinomas of the Uterine Cervix: From Molecular Characterization to Clinical Implications.

Cervical cancer is the fourth most common cancer in women. It is the leading cause of female deaths in developing countries. Most of these cervical neoplasms are represented by squamous lesions.

Development of a convolutional neural network for the identification and the measurement of the median nerve on ultrasound images acquired at carpal tunnel level.

Background: Deep learning applied to ultrasound (US) can provide a feedback to the sonographer about the correct identification of scanned tissues and allows for faster and standardized measurements. The most frequently adopted parameter for US diagnosis of carpal tunnel syndrome is the increasing of the cross-sectional area (CSA) of the median nerve. Our aim was to develop a deep learning algorithm, relying on convolutional neural networks (CNNs), for the localization and segmentation of the median nerve and the automatic measurement of its CSA on US images acquired at the proximal inlet of the carpal tunnel.