Cone Beam Computed Tomographic Imaging: Perspective, Challenges, and the Impact of Near-Trend Future Applications

Cone beam computed tomography (CBCT) can be considered as a valuable imaging modality for improving diagnosis and treatment planning to achieve true guidance for several craniofacial surgical interventions. A new concept and perspective in medical informatics is the highlight discussion about the new imaging interactive workflow. The aim of this article was to present, in a short literature review, the usefulness of CBCT technology as an important alternative imaging modality, highlighting current practices and near-term future applications in cutting-edge thought-provoking perspectives for craniofacial surgical assessment. This article explains the state of the art of CBCT improvements, medical workstation, and perspectives of the dedicated unique hardware and software, which can be used from the CBCT source. In conclusion, CBCT technology is developing rapidly, and many advances are on the horizon. Further progress in medical workstations, engineering capabilities, and improvement in independent software-some open source-should be attempted with this new imaging method. The perspectives, challenges, and pitfalls in CBCT will be delineated and evaluated along with the technological developments.

Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

A Bayesian destructive weighted Poisson cure rate model and an application to a cutaneous melanoma data

In this article, we propose a new Bayesian flexible cure rate survival model, which generalises the stochastic model of Klebanov et al. [Klebanov LB, Rachev ST and Yakovlev AY. A stochastic-model of radiation carcinogenesis - latent time distributions and their properties. Math Biosci 1993; 113: 51-75], and has much in common with the destructive model formulated by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)]. In our approach, the accumulated number of lesions or altered cells follows a compound weighted Poisson distribution. This model is more flexible than the promotion time cure model in terms of dispersion. Moreover, it possesses an interesting and realistic interpretation of the biological mechanism of the occurrence of the event of interest as it includes a destructive process of tumour cells after an initial treatment or the capacity of an individual exposed to irradiation to repair altered cells that results in cancer induction. In other words, what is recorded is only the damaged portion of the original number of altered cells not eliminated by the treatment or repaired by the repair system of an individual. Markov Chain Monte Carlo (MCMC) methods are then used to develop Bayesian inference for the proposed model. Also, some discussions on the model selection and an illustration with a cutaneous melanoma data set analysed by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)] are presented.

O uso de um software na (re)habilitação de crianças com deficiência auditiva

OBJETIVO: Verificar a aplicabilidade de um software na (re)habilitação de crianças com deficiência auditiva. MÉTODOS: A amostra foi composta por 17 crianças com deficiência auditiva, sendo dez usuárias de Implante Coclear (IC) e sete usuárias de Aparelho de Amplificação Sonora Individual (AASI). Foi utilizado o "Software Auxiliar na Reabilitação de Distúrbios Auditivos (SARDA)". Aplicou-se o protocolo de treinamento durante 30 minutos, duas vezes por semana, pelo tempo necessário para a finalização das estratégias que compõe software. Para mensurar a aplicabilidade do software no treinamento da habilidade de percepção da fala no silêncio e no ruído, foram realizadas avaliações com o Hearing in Noise Test (HINT) pré e pós o treinamento auditivo. Os dados foram analisados estatisticamente. RESULTADOS: O grupo de usuários de IC necessitou em média 12,2 dias para finalizar as estratégias e o grupo de usuários de AASI em média 10,14 dias. Os dois grupos apresentaram diferença entre as avaliações pré e pós no silêncio e no ruído. As crianças mais novas apresentaram maior dificuldade durante a execução das estratégias, porém não houve correlação entre a idade e o desempenho. Não houve influência do tipo do dispositivo eletrônico durante o treinamento. As crianças apresentaram maior dificuldade na estratégia que envolvia estímulos não verbais e na estratégia com estímulos verbais que treina a habilidade de atenção sustentada. A atenção e a motivação da criança durante a estimulação foram fundamentais para o bom rendimento do treinamento auditivo. CONCLUSÃO: O treinamento auditivo com o SARDA foi eficaz, pois propiciou melhora na habilidade de percepção da fala, no silêncio e no ruído, das crianças com deficiência auditiva.