Detection of Schizophrenia Spectrum Disorders Using Saliency Maps

Modern medical diagnostic systems have greatly contributed to the increase in survival rate of patients suffering from illnesses and to lengthening of average lifespan. Some diagnostic devices, such as Magnetic Resonance, can detect illnesses that may not exhibit any symptoms yet and medical diagnostic equipment can furthermore significantly aid in confirmation of suspected, otherwise undetectable diagnoses. One of the medical fields lacking automated diagnostic tools is psychopathology, where a mental disorder presence is typically established from observations of classified symptoms and structured systematic interviews. The goal of our paper was to design and test a reliable and automated diagnostic method for detection of Schizophrenia spectrum disorders. The proposed method utilizes eye-tracker and Rorschach Inkblot Test to create a visual attention saliency map of observed subjects. These maps are then processed and analyzed using Digital Image Processing and statistical methods to determine, whether the subject exhibits signs of schizophrenia or not. The proposed approach is based on a trained classifier which separates incoming data into either healthy or schizophrenic patients. Of course, these results are only indicative and the final diagnosis rests in the hands of qualified specialists. High correlation of the proposed system's diagnosis and real clinical diagnosis however proves applicability of the proposed concepts as reliable supplementary tool for Schizophrenia detection.