The aim of the study was to research the use of a simple neural network in diagnosing angina in patients complaining of chest pain. A total of 887 records were extracted from the electronic medical record system (EMR) in Selayang Hospital, Malaysia. Simple neural networks (simple perceptrons) were built and trained using a subset of 470 records with and without pre-processing using principal components analysis (PCA). These were subsequently tested on another subset of 417 records. Average sensitivity of 80.75% (95% CI 79.54%, 81.96%), specificity of 41.64% (95% CI 40.13%, 43.15%), PPV of 46.73% (95% CI 45.20%, 48.26%) and NPV of 77.39% (95% CI 76.11%, 78.67%) were achieved with the simple perceptron. When PCA pre-processing was used, the perceptrons had a sensitivity of 1.43% (95% CI 1.06%, 1.80%), specificity of 98.32% (95% CI 97.92%, 98.72%), PPV of 32.95% (95% CI 31.51%, 34.39%) and NPV of 61.33% (95% CI 59.84%, 62.82%). These results show that it is possible for a simple neural network to have respectable sensitivity and specificity levels for angina.
Bone lengthening with distraction osteogenesis involves prolonged application of an external fixator frame. Qualitative and quantitative evaluation of callus has been described using various imaging modalities but there is no simple reliable and readily available method. This study aims to investigate the use of a densitometer to analyze plain radiographic images and correlate them with the rate of new bone formation as represented by the bone healing index. A total of 34 bone lengthening procedures in 29 patients were retrospectively reviewed. We used an X-Rite 301 densitometer to measure densities of new callus on plain radiographs taken at 4 and 8 weeks after surgery. Patients aged below 16y had significantly lower BHIs indicating faster bone healing and shorter duration of treatment. The ratio of radiographic densities between centre and edge of the new bone measured from plain radiographs taken at 8 weeks correlated positively with the eventual BHI of the patient. This method provides a simple and easy way to predict the rate of bone healing at an early stage of treatment and may also allow remedial action to be taken for those with poor progress in bone formation.