OP31THE SOUND OF SCIENCE: DEVELOPING THE SPECTRAL LIGHT ORCHESTRA

INTRODUCTION: Complete tumour removal during surgery is one of the primary factors for recurrence-free survival. The current process is unable to accurately identify tumour margins and data feedback takes too long for responsive action during surgery. Our recent study has shown the ability of Raman spectroscopy to distinguish between metastatic brain, GBM and normal brain tissue to sensitivities and specificities ranging from 85.71% to 100%. Pushing this result towards the clinic needs an assessment of the data feedback. In a surgery situation, audio feedback of tumour margins would provide a real time response allowing the surgeons visual focus to remain on the patient.Auditory perception of complex, structured information could have several advantages when compared to visual images including the capability of the human ear to detect patterns, recognise timbres and follow different strands at the same time. METHOD: Our method consists of two phases, to compute the relevant variables, we apply feature extraction to the data, extracting band-wise spectral shape descriptors such as centroid, skew and kurtosis. These are then used to modulate parameters of a synthesizer. RESULTS: The method was applied to a dataset of 311 samples, 136 from an uncontrolled and 176 from a controlled group. Based on subjective listening tests taken from around 20 participants, we were able to get manual classification accuracies of up to 83%, using a multi-stimulus technique. CONCLUSION: Raman spectroscopy is able to accurately distinguish between cancerous and non-cancerous tissue. The development of novel data extraction tools has enabled audio feedback of these molecular differences in real time allowing the surgeons visual focus to remain on the patient.