Audit on the use of radiological investigations in the management of rhinosinusitis

Objectives: The aim of this audit is to establish the cost to the Maltese health system from the use of radiological imaging in managing rhinosinusitis and to identify areas in which these costs can be minimised by following guidelines on the management of rhinosinusitis. Methods: All plain radiographs and computed tomography scans (CT) of the paranasal sinuses requested in the Mater Dei Hospital over a one year period were analysed. Data was collected regarding: the quantity of investigations ordered, age of the patients, cost and requesting department. Results: Over one year: 205 CT scans and 113 sets of plain radiographs of the paranasal sinuses were requested, costing a total of euro103,440. The majority (73%) were elective requests made by ENT consultants. Five percent of CT scans were requested for patients less than 10 years of age. Conclusion: Rhinosinusitis is diagnosed clinically, only requiring radiological investigation in more complex cases best managed by specialists in ENT. Plain radiographs have limited use in the management of rhinosinusitis. Judicious use of imaging requests whilst following clinical guidelines is required to save money and minimise patient exposure to ionising radiation.peer-reviewe

Objective auditory brainstem response classification using machine learning

The objective of this study was to use machine learning in the form of a deep neural network to objectively classify paired auditory brainstem response waveforms into either: ‘clear response’, ‘inconclusive’ or ‘response absent’. A deep convolutional neural network was constructed and fine-tuned using stratified 10-fold cross-validation on 190 paired ABR waveforms. The final model was evaluated on a test set of 42 paired waveforms. The full dataset comprised 232 paired ABR waveforms recorded from eight normal-hearing individuals. The dataset was obtained from the PhysioBank database. The paired waveforms were independently labelled by two audiological scientists in order to train the network and evaluate its performance. The trained neural network was able to classify paired ABR waveforms with 92.9% accuracy. The sensitivity and the specificity were 92.9% and 96.4%, respectively. This neural network may have clinical utility in assisting clinicians with waveform classification for the purpose of hearing threshold estimation. Further evaluation using a large clinically obtained dataset would provide further validation with regard to the clinical potential of the neural network in diagnostic adult testing, newborn testing and in automated newborn hearing screening