This work presents an application of the paraconsistent artificial neural network (PANN) in the analysis of cephalometric variables and provides an orthodontic diagnosis. An expert's analysis is subject to the inherent imprecision of measurements, registers, and individual variability of physician visual analysis. Patient input cephalometric values are compared with means drawn from individuals considered normal in the cephalometric point of view by the PANN. This reference is constituted by individuals from 6 to 18 years old, both genders. The applied cephalometric analysis was targeted to measure skeletal and dental discrepancies and established a cephalometric diagnosis. The analysis results in degrees of skeletal, anteroposterior, and dental discrepancy, pertinent to upper and lower incisors. A sample of 120 orthodontic patients was processed by the proposed model and three orthodontic experts. Comparisons between the model and the human expert's performance provided kappa indexes that varied from moderate to almost perfect agreement. The agreement between the model and specialist's performance was equivalent. In addition, the model pointed out contradictions presented in the data that were not noticed by the orthodontists, which highlight the contribution that this kind of system could carry out in the orthodontics decision support.

BackgroundLiver transplantation has received increased attention in the medical field since the 1980s following the introduction of new immunosuppressants and improved surgical techniques. Currently, transplantation is the treatment of choice for patients with end-stage liver disease, and it has been expanded for other indications. Liver transplantation outcomes depend on donor factors, operating conditions, and the disease stage of the recipient. A retrospective cohort was studied to identify mortality and graft failure rates and their associated factors. All adult liver transplants performed in the state of São Paulo, Brazil, between 2006 and 2012 were studied.Methods and FindingsA hierarchical Poisson multiple regression model was used to analyze factors related to mortality and graft failure in liver transplants. A total of 2,666 patients, 18 years or older, (1,482 males; 1,184 females) were investigated. Outcome variables included mortality and graft failure rates, which were grouped into a single binary variable called negative outcome rate. Additionally, donor clinical, laboratory, intensive care, and organ characteristics and recipient clinical data were analyzed. The mortality rate was 16.2 per 100 person-years (py) (95% CI: 15.1–17.3), and the graft failure rate was 1.8 per 100 py (95% CI: 1.5–2.2). Thus, the negative outcome rate was 18.0 per 100 py (95% CI: 16.9–19.2). The best risk model demonstrated that recipient creatinine ≥ 2.11 mg/dl [RR = 1.80 (95% CI: 1.56–2.08)], total bilirubin ≥ 2.11 mg/dl [RR = 1.48 (95% CI: 1.27–1.72)], Na+ ≥ 141.01 mg/dl [RR = 1.70 (95% CI: 1.47–1.97)], RNI ≥ 2.71 [RR = 1.64 (95% CI: 1.41–1.90)], body surface ≥ 1.98 [RR = 0.81 (95% CI: 0.68–0.97)] and donor age ≥ 54 years [RR = 1.28 (95% CI: 1.11–1.48)], male gender [RR = 1.19(95% CI: 1.03–1.37)], dobutamine use [RR = 0.54 (95% CI: 0.36–0.82)] and intubation ≥ 6 days [RR = 1.16 (95% CI: 1.10–1.34)] affected the negative outcome rate.ConclusionsThe current study confirms that both donor and recipient characteristics must be considered in post-transplant outcomes and prognostic scores. Our data demonstrated that recipient characteristics have a greater impact on post-transplant outcomes than donor characteristics. This new concept makes liver transplant teams to rethink about the limits in a MELD allocation system, with many teams competing with each other. The results suggest that although we have some concerns about the donors features, the recipient factors were heaviest predictors for bad outcomes.

Análise de decisão multicritério para alocação de recursos e avaliação de tecnologias em saúde: tão longe e tão perto?Multi-criteria decision analysis for health technology resource allocation and assessment: so far and so near?Análisis de decisión multicriterio para la asignación de recursos y evaluación de tecnologías en salud: ¿tan lejos y tan cerca? Este é um artigo publicado em acesso aberto (Open Access) sob a licença Creative Commons Attribution, que permite uso, distribuição e reprodução em qualquer meio, sem restrições, desde que o trabalho original seja corretamente citado.

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EEG visual analysis has proved useful in aiding AD diagnosis, being indicated in some clinical protocols. However, such analysis is subject to the inherent imprecision of equipment, patient movements, electric registers, and individual variability of physician visual analysis.ObjectivesTo employ the Paraconsistent Artificial Neural Network to ascertain how to determine the degree of certainty of probable dementia diagnosis.MethodsTen EEG records from patients with probable Alzheimer disease and ten controls were obtained during the awake state at rest. An EEG background between 8 Hz and 12 Hz was considered the normal pattern for patients, allowing a variance of 0.5 Hz.ResultsThe PANN was capable of accurately recognizing waves belonging to Alpha band with favorable evidence of 0.30 and contrary evidence of 0.19, while for waves not belonging to the Alpha pattern, an average favorable evidence of 0.19 and contrary evidence of 0.32 was obtained, indicating that PANN was efficient in recognizing Alpha waves in 80% of the cases evaluated in this study. Artificial Neural Networks – ANN – are well suited to tackle problems such as prediction and pattern recognition. The aim of this work was to recognize predetermined EEG patterns by using a new class of ANN, namely the Paraconsistent Artificial Neural Network – PANN, which is capable of handling uncertain, inconsistent and paracomplete information. An architecture is presented to serve as an auxiliary method in diagnosing Alzheimer disease.Conclusions We believe the results show PANN to be a promising tool to handle EEG analysis, bearing in mind two considerations: the growing interest of experts in visual analysis of EEG, and the ability of PANN to deal directly with imprecise, inconsistent, and paracomplete data, thereby providing a valuable quantitative analysis.

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Paraconsistent logic (PL) is a type of non-classical logic that accepts contradiction as a fundamental concept and has produced valuable results in the analysis of uncertainties. In this work, algorithms based on a type of PL-paraconsistent annotated logic of two values (PAL2v)-are interconnected into a network of paraconsistent analysis (PANnet). PANnet was applied to a dataset comprising 146 Raman spectra of skin tissue biopsy fragments of which 30 spectra were determined to represent normal skin tissue (N), 96 were determined to represent tissue with basal cell carcinoma, and 19 were determined to be tissue with melanoma (MEL). In this database, paraconsistent analysis was able to correctly discriminate 136 out of a total of 145 fragments, obtaining a 93.793 % correct diagnostic accuracy. The application of PAL2v in the analysis of Raman spectroscopy signals produces better discrimination of cells than conventional statistical processes and presents a good graphical overview through its associated lattice structure. The technique of PAL2v-based data processing can be fundamental in the development of a computational tool dedicated to support the diagnosis of skin cancer using Raman spectroscopy.