Co może uzyskać otolaryngolog, stosując sztuczne sieci neuronowe?

Online Estimation Method for Respiratory Parameters Based on a Pneumatic Model

Mechanical ventilation is an important method to help people breathe. Respiratory parameters of ventilated patients are usually tracked for pulmonary diagnostics and respiratory treatment assessment. In this paper, to improve the estimation accuracy of respiratory parameters, a pneumatic model for mechanical ventilation was proposed. Furthermore, based on the mathematical model, a recursive least-squares algorithm was adopted to estimate the respiratory parameters. Finally, through experimental and numerical study, it was demonstrated that the proposed estimation method was effective and the method can be used in pulmonary diagnostics and treatment.

Pressure dynamic characteristics of pressure controlled ventilation system of a lung simulator

Mechanical ventilation is an important life support treatment of critically ill patients, and air pressure dynamics of human lung affect ventilation treatment effects. In this paper, in order to obtain the influences of seven key parameters of mechanical ventilation system on the pressure dynamics of human lung, firstly, mechanical ventilation system was considered as a pure pneumatic system, and then its mathematical model was set up. Furthermore, to verify the mathematical model, a prototype mechanical ventilation system of a lung simulator was proposed for experimental study. Last, simulation and experimental studies on the air flow dynamic of the mechanical ventilation system were done, and then the pressure dynamic characteristics of the mechanical system were obtained. The study can be referred to in the pulmonary diagnostics, treatment, and design of various medical devices or diagnostic systems.

Outcome prediction in endoscopic surgery for chronic rhinosinusitis - a multidimensional model

PURPOSE

Chronic rhinosinusitis (CRS) is one of the most common diseases in the modern society. In recent years endoscopic sinus surgery (ESS) has become the treatment of choice for patients with CRS refractory to medical therapy. ESS proved to be successful in most, but not all patients with CRS. Currently there is no direct method available to distinguish between patients who are likely to benefit from ESS and those who are not. The aim of this study was to build multidimensional models (artificial neural networks) to predict early outcomes of ESS in individual patients.

MATERIAL/METHODS

The study group comprised of 115 patients operated for CRS in the Department of Otolaryngology, Jagiellonian University Collegium Medicum, Cracow. The neural models were created using the Statistica Neural Network computer software package. The models required only information easily achievable for every patient before surgery. Consequently, the models could be readily applicable in everyday clinical practice. To define the results of surgery three different mathematical descriptions were compared. The models' predictions were compared with the actual results of surgery 3-6 months postoperatively.

RESULTS

The models were able to predict the early outcome of surgery in 90% of the patients but their quality depended on mathematical representation of the surgery result. The best models were characterized by 93% sensitivity and 86% specificity.

CONCLUSIONS

The results of ESS depend on many factors, so reliable outcome prognoses can be produced only by multidimensional models. Artificial neural networks are a promising multidimensional tool facilitating clinical decision making in patients with CRS.