Italian National Registry of Alopecia Areata: an epidemiological study of 699 Italian patients.

Background: Alopecia areata (AA) is an organ-specific autoimmune disease that affects the hair follicles of the scalp and the rest of the body causing hair loss. Due to the unpredictable course of AA and the different degrees of severity of hair loss, only a few well-designed clinical studies with a low number of patients are available. Also, there is no specific cure, but topical and systemic anti-inflammatory and immune system suppressant drugs are used for treatment.

Dissecting human anatomy learning process through anatomical education with augmented reality: AEducAR 2.0, an updated interdisciplinary study.

Anatomical education is pivotal for medical students, and innovative technologies like augmented reality (AR) are transforming the field. This study aimed to enhance the interactive features of the AEducAR prototype, an AR tool developed by the University of Bologna, and explore its impact on human anatomy learning process in 130 second-year medical students at the International School of Medicine and Surgery of the University of Bologna. An interdisciplinary team of anatomists, maxillofacial surgeons, biomedical engineers, and educational scientists collaborated to ensure a comprehensive understanding of the study's objectives.

Morpho-functional analysis of the temporomandibular joint following mandible-first bimaxillary surgery with mandible-only patient-specific implants.

The aim of this study was to evaluate condylar and glenoid fossa remodeling after bimaxillary orthognathic surgery guided by patient-specific mandibular implants. In total, 18 patients suffering from dentofacial dysmorphism underwent a virtually planned bimaxillary mandibular PSI-guided orthognathic procedure. One month prior to surgery, patients underwent a CBCT scan and optical scans of the dental arches; these datasets were re-acquired 1 month and at least 9 months postsurgery.

Automated Prediction of Photographic Wound Assessment Tool in Chronic Wound Images.

Many automated approaches have been proposed in literature to quantify clinically relevant wound features based on image processing analysis, aiming at removing human subjectivity and accelerate clinical practice. In this work we present a fully automated image processing pipeline leveraging deep learning and a large wound segmentation dataset to perform wound detection and following prediction of the Photographic Wound Assessment Tool (PWAT), automatizing the clinical judgement of the adequate wound healing. Starting from images acquired by smartphone cameras, a series of textural and morphological features are extracted from the wound areas, aiming to mimic the typical clinical considerations for wound assessment.