Modelling of interaction between a spatula and a human brain.

This paper describes a method for surgery simulation, or more specifically a learning system of how to use a brain spatula. Improper use of brain spatulas can lead to brain tissue lesions such as tearing of brain tissue and ischemia. The idea is to provide surgeons with a tool which can teach them the correlation between deformation and applied force. The system includes a Finite Element based model of the brain in a Virtual Reality setup with haptic feedback. The physical model links the shape of the deformable model with the associated force. The interaction between the spatula and the brain model is handled by a collision response method which aims at smoothing the discrete haptic feedback. The experimental results are promising even though the used force feedback device is somewhat constraining the realism.

Using a model of the colour content in retinal fundus images to screen for sight threatening diabetic retinopathy.

The aim of the study was to assess the potential use of a model of the colour content in retinal fundus images to screen for sight threatening retinopathy in diabetes. Diabetic retinopathy is the most frequent cause of blindness in the population of working age in industrialised countries, but efficient therapies do exist, and accurate and early diagnosis, and correct treatment can prevent blindness in more than 50% of all cases. However, up to 50% of cases of type 2 diabetes, which comprises 85-90% of all patients, are undiagnosed, with an average delay of 10 years between the onset of the condition and diagnosis. In an other study we have described how there is a linear relation between age and the colour composition of retinal images from non-diabetic subjects. In the present study this relation was compared to the colour composition of retinal images from diabetes patients. We found that for the patients in the present study there is a significant difference in the colour composition between normal subjects and diabetic subjects with retinopathy. Although the number of patients in our study is too small to allow any conclusion, we suggest that this difference potentially may be used as the basis for a simple screening method for sight threatening retinopathy in unrecognised diabetes, or potentially may help estimating the risk of developing diabetic late complications in newly diagnosed type 2 diabetes.

The relationship between age and colour content in fundus images.

INTRODUCTION: The morphological appearance of the ocular fundus is one of the key parameters used in the diagnosis and management of retinal disease. However, optical imperfections in the refractive media result in blurring, low luminance and contrast, and changes in the colour composition of the image which can be seen as an increasing yellowish appearance with age. The introduction of a method for quantifying this age-related change in colour content may help in diagnosing and grading pathological changes in the eye lens which are secondary to ocular and systemic diseases. METHODS: A total of 102 digitized fundus images from 102 healthy subjects (mean age = 50.4 years, range 7.0-94.3 years) were used to build a model for estimating the age of the subject from the colour content of the images. RESULTS: Estimation of age from the fundus images could be done within approximately 16 years. This variation could be reduced considerably by analysis of repeated photographs from the same examination. CONCLUSION: The colour content of fundus images can be used to estimate the ages of healthy subjects. Furthermore, when the colour content of fundus images deviates from that expected according to subject age, this may indicate causes other than age of increased light absorption in the lens, such as cumulative exposure to hyperglycaemia in diabetic patients. This could potentially be used to identify patients with undiagnosed type 2 diabetes in the general population and help to establish their risk of developing late diabetic complications as the cumulative exposure to hyperglycaemia is unknown at the time of diagnosis of the disease.