Shape annotation for intelligent image retrieval

Annotation of shapes is an important process for semantic image retrieval. In this paper, we present a shape annotation framework that enables intelligent image retrieval by exploiting in a unified manner domain knowledge and perceptual description of shapes. A semi-supervised fuzzy clustering process is used to derive domain knowledge in terms of linguistic concepts referring to the semantic categories of shapes. For each category we derive a prototype that is a visual template for the category. A novel visual ontology is proposed to provide a description of prototypes and their salient parts. To describe parts of prototypes the visual ontology includes perceptual attributes that are defined by mimicking the analogy mechanism adopted by humans to describe the appearance of objects. The effectiveness of the developed framework as a facility for intelligent image retrieval is shown through results on a case study in the domain of fish shapes

A Hospital Medical Record Quality Scoring Tool (MeReQ): Development, Validation, and Results of a Pilot Study

Hospital medical records are valuable and cost-effective documents for assessing the quality of healthcare provided to patients by a healthcare facility during hospitalization. However, there is a lack of internationally validated tools that measure the quality of the whole hospital medical record in terms of both form and content. In this study, we developed and validated a tool, named MeReQ (medical record quality) tool, which quantifies the quality of the hospital medical record and enables statistical modeling using the data obtained. The tool was applied to evaluate a sample of hospital individual patient medical records from a secondary referral hospital and to identify the departments that require quality improvement interventions and the effects of improvement actions already implemented

Effects of ACL Reconstructive Surgery on Temporal Variations of Cytokine Levels in Synovial Fluid

Anterior cruciate ligament (ACL) reconstruction restores knee stability but does not reduce the incidence of posttraumatic osteoarthritis induced by inflammatory cytokines. The aim of this research was to longitudinally measure IL-1β, IL-6, IL-8, IL-10, and TNF-α levels in patients subjected to ACL reconstruction using bone-patellar tendon-bone graft. Synovial fluid was collected within 24-72 hours of ACL rupture (acute), 1 month after injury immediately prior to surgery (presurgery), and 1 month thereafter (postsurgery). For comparison, a "control" group consisted of individuals presenting chronic ACL tears. Our results indicate that levels of IL-6, IL-8, and IL-10 vary significantly over time in reconstruction patients. In the acute phase, the levels of these cytokines in reconstruction patients were significantly greater than those in controls. In the presurgery phase, cytokine levels in reconstruction patients were reduced and comparable with those in controls. Finally, cytokine levels increased again with respect to control group in the postsurgery phase. The levels of IL-1β and TNF-α showed no temporal variation. Our data show that the history of an ACL injury, including trauma and reconstruction, has a significant impact on levels of IL-6, IL-8, and IL-10 in synovial fluid but does not affect levels of TNF-α and IL-1β

Impact of COVID-19 pandemic on outpatient visit volume in cancer patients: Results of COMETA multicenter retrospective observational study

ObjectiveTo evaluate the impact of the COVID-19 pandemic on first and follow-up visits for cancer outpatients.MethodsThis is a multicenter retrospective observational study involving three Comprehensive Cancer Care Centers (CCCCs): IFO, including IRE and ISG in Rome, AUSL-IRCCS of Reggio Emilia, and IRCCS Giovanni Paolo II in Bari) and one oncology department in a Community Hospital (Saint'Andrea Hospital, Rome). From 1 January 2020 and 31 December 2021, we evaluated the volume of outpatient consultations (first visits and follow-up), comparing them with the pre-pandemic year (2019). Results were analyzed by quarter according to the Rt (real-time indicator used to assess the evolution of the pandemic). IFO and IRCCS Giovanni Paolo II were “COVID-free” while AUSL-IRCCS RE was a “COVID-mixed” Institute. Depending on the Rt, Sain't Andrea Hospital experienced a “swinging” organizational pathway (COVID-free/ COVID-mixed).ResultsRegarding the “first appointments”, in 2020 the healthcare facilities operating in the North and Center of Italy showed a downward trend. In 2021, only AUSL-IRCCS RE showed an upward trend. Regarding the “follow-up”, only AUSL IRCCS RE showed a slight up-trend in 2020. In 2021, IFO showed an increasing trend, while S. Andrea Hospital showed a negative plateau. Surprisingly, IRCCS Giovanni Paolo II in Bari showed an uptrend for both first appointment and follow-ups during pandemic and late pandemic except for the fourth quarter of 2021.ConclusionsDuring the first pandemic wave, no significant difference was observed amongst COVID-free and COVID-mixed Institutes and between CCCCs and a Community Hospital. In 2021 (“late pandemic year”), it has been more convenient to organize COVID-mixed pathway in the CCCCs rather than to keep the Institutions COVID-free. A swinging modality in the Community Hospital did not offer positive results in term of visit volumes. Our study about the impact of COVID-19 pandemic on visit volume in cancer outpatients may help health systems to optimize the post-pandemic use of resources and improve healthcare policies

Incremental indexing of objects in pictorial databases

Object indexing is a challenging task that enables the retrieval of relevant images in pictorial databases. In this paper, we present an incremental indexing approach of picture objects based on clustering of object shapes. A semisupervised fuzzy clustering algorithm is used to group similar objects into a number of clusters by exploiting a-priori knowledge expressed as a set of pre-labeled objects. Each cluster is represented by a prototype that is manually labeled and used to annotate objects. To capture eventual updates that may occur in the pictorial database, the previously discovered prototypes are added as pre-labeled objects to the current shape set before clustering. The proposed incremental approach is evaluated on a benchmark image dataset, which is divided into chunks to simulate the progressive availability of picture objects during time

A Fuzzy Set Approach for Shape-Based Image Annotation

In this paper, we present a shape labeling approach for automatic image annotation. A fuzzy clustering process is applied to shapes represented by Fourier descriptors in order to derive a set of shape prototypes. Then, prototypes are manually annotated by textual labels corresponding to semantic categories. Based on the labeled prototypes, a new shape is automatically labeled by associating a fuzzy set that provides membership degrees of the shape to all semantic classes. Preliminary results show the suitability of the proposed approach to image annotation by encouraging its application in wider application contexts

Web user profiling using fuzzy clustering

Web personalization is the process of customizing a Web site to the preferences of users, according to the knowledge gained from usage data in the form of user profiles. In this work, we experimentally evaluate a fuzzy clustering approach for the discovery of usage profiles that can be effective in Web personalization. The approach derives profiles in the form of clusters extracted from preprocessed Web usage data. The use of a fuzzy clustering algorithm enable the generation of overlapping clusters that can capture the uncertainty among Web users navigation behavior based on their interest. Preliminary experimental results are presented to show the clusters generated by mining the access log data of a Web site

Fuzzy clustering in user segmentation

E-Government is becoming more attentive towards providing personalized services to citizens so that they can benefit from better services with less time and effort. To develop citizen-centered services, a fundamental activity consists in mining needs and preferences of users by identifying homogeneous groups of users, also known as user segments, sharing similar characteristics. Since the same user often has characteristics shared by several segments, in this work we propose an approach based on fuzzy clustering for inferring user segments that could be properly exploited to offer personalized services that better satisfy user needs and their expectations. User segments are inferred starting from data, gathered by questionnaires, which essentially describe demographic characteristics of users. For each derived segment a user profile is defined which summarizes characteristics shared by users belonging to that segment. Results obtained on a case study are reported in the last part of the paper