Purpose: Oral health encompasses conditions such as caries, periodontal disease, tooth loss, and oral cancer. Detection of oral conditions in time is crucial for effective prevention and treatment, yet clinical inspections have limitations in identifying hidden or inaccessible lesions. Dental radiography aids in the precise diagnosis but may be prone to misinterpretation. Computational techniques offer potential solutions, including tools for cavity detection and root canal guidance, with tooth detection as a foundational step. We create a modular teeth instance segmentation model to enhance this step. Methods: We annotated 935 dentomaxillary region bounding boxes on panoramic radiographs and segmented 605 of these images, generating 14,582 tooth polygon annotations. RetinaNet detected dentomaxillary regions and Cascade Mask R-CNN identified individual teeth. Results: Our system achieved satisfactory detection of the dentomaxillary region (92.446 mAP, 0.982 F1 score) and tooth segmentation (79.222 mAP, 0.989 F1 score), exceeding the benchmarks of comparable studies. Conclusions: Our proposal effectively segments teeth on panoramic radiographs, establishing a robust basis for future modules such as tooth numbering and caries detection. Besides aiding diagnosis, it can expedite epidemiological reports and find utility in forensic medicine.

The growth of assistive technologies brings new perspectives to Speech Sound Disorders (SSD) treatment. For example, automatic Speech Recognition (ASR) tools recognize and convert sound signals into text in multiple languages. Commonly, these tools rely on models trained with samples from typically developed speakers, but most of them can deal well with sonorous variations such as accents. Hence, there is an expectation that they may also transcribe phonological disorders, such as those produced by people with SSD. However, this potential remains poorly known. Here, we analyze the potential of one of the ASR tools, Google’s speech-to-text API©, as a multilevel indicator of speech intelligibility. We used pronunciations of volunteer actors, which simulated people with a broad spectrum of speech impairments. The tool indicated speech intelligibility at a general level and was marginally capable of determining the SSD type, but it could not map the syllable exchanges accurately. In short, our results suggest that ASRs have great potential as components of assistive tools in many contexts. Our contribution goes beyond the tests themselves, as we propose a simple, robust, systematic, and automated method to quantify speech intelligibility using ASRs. The method, which still needs clinical validation, can be replicated in other versions and tools and the pronunciations of people who are genuinely SSD carriers or in other languages, as long as they use the appropriate protocols. The goal is to enhance ASR tools’ capabilities to promote even higher digital inclusion for people with phonological disorders.

Computational Thinking (CT) is a set of thinking processes used by computer scientists to formulate problems and describe solutions. In recent years, CT has been largely explored by the Computing Education community. Due to its potential to contribute to problem solving and analytical thinking, CT could also be a relevant subject in K-12 education. For such, there is a need to qualify educators in CT. In this paper, we describe our experience in a large-scale course on Computational Thinking for pre-service teachers. Our goal is to qualify pre-service teachers in CT, both in the comprehension of its definition and how it can be incorporated in classroom. Our experience indicates that the participants had a positive awareness of CT concepts, as well as an adequate understanding of the pedagogical approaches to teach the subject. They also developed positive attitudes towards the field of Computer Science.

Symmetries express the invariance of a system towards sets of mathematical transformations. In more practical terms, symmetries greatly reduce or simplify the computational efforts required to evaluate relevant properties of a system. In this paper, two methods are proposed to implement spin symmetries which simplify the analysis of the spreading of diseases in an agent-based epidemic model. We perform a set of simulations to measure the efficiency gains compared to traditional methods. Our findings show symmetry-based algorithms improve the performance of the Monte Carlo simulation and the exact Markov process.

For people with speech disorders, language therapies usually employ speech exercises performed at home, without close specialized supervision. There are fundamental movements to train speech ability called NonVerbal Praxis (NVP). These movements include blows and tongue snaps, which develop both the neuromotor capacities of people with apraxia and the pronunciation of phonemes. However, current research on NVP recognition has employed standard speech recognition techniques without much success. This paper describes a method to identify NVP under field conditions. The method covers the input audio signal segmentation until the audio segments are classified and scored. Our method was experimented with, and NVP sounds were efficiently classified, which helped guide patients and therapists through home therapy. It also provides valuable information for speech-language professionals to evaluate the patient's therapeutic performance.

The literature describes examples of software frameworks providing developers with generic and reusable functionality for building healthcare applications.Using concepts and technologies from Information Retrieval, Machine Learning, and Semantic Web, we present a novel software framework called HSSF (Health Surveillance Software Framework) which aims to facilitate the development of applications to support health professionals in the prevention of chronic diseases.The main contribution of this paper includes lessons learned distilled from (i) the reuse and evolution of the HSSF components on the development of three new health surveillance applications, and (ii) a quantitative evaluation of the HSSF reusability in terms of time spent and artifacts reused on such development task.Lessons learned are summarized as advantages and drawbacks regarding HSSF reusability.The HSSF allows healthcare applications not only to relate scientific research evidences, exams and treatments, but also to incorporate them together into the clinical practice.

The use of computer systems for management and imaging analysis has brought many advantages to medical organizations. This paper comprises a survey regarding the Lyria PACS system implemented in 2011 at Hospital das Clinicas, Ribeirão Preto USP - Brazil. It presents the benefits of this system, the main challenges faced during its implementation, the heterogeneity of infrastructure resources and the monetary economy obtained after the installation of Lyria PACS in short and medium periods of time. The results are shown using statistics and growth of use.

CISS+ is a new surveillance system designed to aid clinicians seeking to help patients make healthy decisions about their lifestyle and health. It takes clinical records as queries anduses these records to find articles in the scientific literature that describe risk factors relevant to the patients' history and medical state. CISS+ is a revision of the earlier CISS system, which useda simple n-gram text retrieval strategy. CISS+ enhances CISS byusing higher-relevance terms and concepts taken from the UMLS and MetaMap systems and by using automated techniques related to relevance feedback to refine queries. CISS+ was evaluated viaa small set of experimental queries that were sent to other widely available biomedical research search systems. CISS+ returned asmall number of results that had better relevance to the goal of identifying risks factors.

This paper demonstrates our ongoing experience on a documenter system for medical grand rounds. The system captures and synchronizes the set of material presented and corresponding physicians' interactions, automatically relates clinical cases of patients, and then generates web-accessible documents with all information captured. The resulting documentation can be used for several purposes such as teaching, research and presurgical decision taking.

Interacting with services that create links automatically via Web users are able to identify relationships among documents stored in different repositories. The fact that automatic linking services do not use queries performed by a human user has impact in the use of information retrieval techniques for the identification of relationships. Information retrieval techniques can lead to the identification of relationships that should not have been generated (generating non-relevant links) at the same time that fail to identify all relevant relationships (poor recall). Towards improving the quality of the relationships identified we have investigated some design issues considered during the automatic linking of textual repositories. The investigations have used a collection of documents from online Brazilian Newspapers and the Cystic Fibrosis Collection. The results of the investigations have defined procedures infrastructures and consequently the requirements for a configurable linking service made also available as a contribution of this work.

We present a framework for the automatic generation of links based on salient semantic structures extracted from homogeneous web repositories, and discuss an imple-mentation of the framework. For this study, we consider homogeneous the repositories of the eClass, an instrumented environment that automatically captures details of a lecture and provides effective multimedia-enhanced web-based in-terfaces for users to review the lecture, and the CoWeb, a web-based service for collaborative authoring of web-based material. We exploited Latent Semantic Analysis over data indexed by a general public license search engine. We exper-imented our service with data from a graduate course sup-ported by both eClass and CoWeb repositories. We present the results of the Latent Semantic Analysis linking service in the light of results previously obtained with our previous works.