Fabián Narváez

Computer aided diagnosis systems (CAD) have been developed to assist radiologists in the detection and diagnosis of abnormalities and a large number of pattern recognition techniques have been proposed to obtain a second opinion. Most of these strategies have been evaluated using different datasets making their performance incomparable. In this work, an open access database of thyroid ultrasound images is presented. The dataset consists of a set of B-mode Ultrasound images, including a complete annotation and diagnostic description of suspicious thyroid lesions by expert radiologists. Several types of lesions as thyroiditis, cystic nodules, adenomas and thyroid cancers were included while an accurate lesion delineation is provided in XML format. The diagnostic description of malignant lesions was confirmed by biopsy. The proposed new database is expected to be a resource for the community to assess different CAD systems.

This review aims to characterize the current landscape of exoskeletons designed to promote medical care and occupational safety in industrial settings. Extensive exploration of scientific databases spanning industries, health, and medicine informs the classification of exoskeletons according to their distinctive attributes and specific footholds on the human physique. Within the scope of this review, a comprehensive analysis is presented, contextualizing the integration of exoskeletons based on different work activities. The reviewers extracted the most relevant articles published between 2008 and 2023 from IEEE, Proquest, PubMed, Science Direct, Scopus, Web of Science, and other databases. In this review, the PRISMA-ScR checklist was used, and a Cohen's kappa coefficient of 0.642 was applied, implying moderate agreement among the reviewers; 75 primary studies were extracted from a total of 344. The future of exoskeletons in contributing to occupational health and safety will depend on continued collaboration between researchers, designers, healthcare professionals, and industries. With the continued development of technologies and an increasing understanding of how these devices interact with the human body, exoskeletons will likely remain valuable for improving working conditions and safety in various work environments.

Most CBIR-based CAD systems (Content Based Image Retrieval systems for Computer Aided Diagnosis) identify lesions that are eventually relevant. These systems base their analysis upon a single independent view. This article presents a CBIR framework which automatically describes mammographic masses with the BI-RADS lexicon, fusing information from the two mammographic views. After an expert selects a Region of Interest (RoI) at the two views, a CBIR strategy searches similar masses in the database by automatically computing the Mahalanobis distance between shape and texture feature vectors of the mammography. The strategy was assessed in a set of 400 cases, for which the suggested descriptions were compared with the ground truth provided by the data base. Two information fusion strategies were evaluated, allowing a retrieval precision rate of 89.6% in the best scheme. Likewise, the best performance obtained for shape, margin and pathology description, using a ROC methodology, was reported as AUC = 0.86, AUC = 0.72 and AUC = 0.85, respectively.