Using VR to Investigate the Relationship between Visual Acuity and Severity of Simulated Oscillopsia.

PURPOSE: Oscillopsia is a debilitating symptom resulting from involuntary eye movement most commonly associated with acquired nystagmus. Investigating and documenting the effects of oscillopsia severity on visual acuity (VA) is challenging. This paper aims to further understanding of the effects of oscillopsia using a virtual reality simulation. METHODS: Fifteen right-beat horizontal nystagmus waveforms, with different amplitude (1°, 3°, 5°, 8° and 11°) and frequency (1.25 Hz, 2.5 Hz and 5 Hz) combinations, were produced and imported into virtual reality to simulate different severities of oscillopsia. Fifty participants without ocular pathology were recruited to read logMAR charts in virtual reality under stationary conditions (no oscillopsia) and subsequently while experiencing simulated oscillopsia. The change in VA (logMAR) was calculated for each oscillopsia simulation (logMAR VA with oscillopsia - logMAR VA with no oscillopsia), removing the influence of different baseline VAs between participants. A one-tailed paired t-test was used to assess statistical significance in the worsening in VA caused by the oscillopsia simulations. RESULTS: VA worsened with each incremental increase in simulated oscillopsia intensity (frequency x amplitude), either by increasing frequency or amplitude, with the exception of statistically insignificant changes at lower intensity simulations. Theoretical understanding predicted a linear relationship between increasing oscillopsia intensity and worsening VA. This was supported by observations at lower intensity simulations but not at higher intensities, with incremental changes in VA gradually levelling off. A potential reason for the difference at higher intensities is the influence of frame rate when using digital simulations in virtual reality. CONCLUSIONS: The frequency and amplitude were found to equally affect VA, as predicted. These results not only consolidate the assumption that VA degrades with oscillopsia but also provide quantitative information that relates these changes to amplitude and frequency of oscillopsia.

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

BACKGROUND: Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson's Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. RESULTS: The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson's disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. CONCLUSIONS: Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context.

A novel diagnostic aid for intra-abdominal adhesion detection in cine-MRI: pilot study and initial diagnostic impressions.

OBJECTIVE: A non-invasive diagnostic technique for abdominal adhesions is not currently available. Capture of abdominal motion due to respiration in cine-MRI has shown promise, but is difficult to interpret. This article explores the value of a complimentary diagnostic aid to facilitate the non-invasive detection of abdominal adhesions using cine-MRI. METHOD: An image processing technique was developed to quantify the amount of sliding that occurs between the organs of the abdomen and the abdominal wall in sagittal cine-MRI slices. The technique produces a "sheargram" which depicts the amount of sliding which has occurred over 1-3 respiratory cycles. A retrospective cohort of 52 patients, scanned for suspected adhesions, made 281 cine-MRI sagittal slices available for processing. The resulting sheargrams were reported by two operators and compared with expert clinical judgment of the cine-MRI scans. RESULTS: The sheargram matched clinical judgment in 84% of all sagittal slices and 93-96% of positive adhesions were identified on the sheargram. The sheargram displayed a slight skew towards sensitivity over specificity, with a high positive adhesion detection rate but at the expense of false positives. CONCLUSION: Good correlation between sheargram and absence/presence of inferred adhesions indicates quantification of sliding motion has potential to aid adhesion detection in cine-MRI. ADVANCES IN KNOWLEDGE: This is the first attempt to clinically evaluate a novel image processing technique quantifying the sliding motion of the abdominal contents against the abdominal wall. The results of this pilot study reveal its potential as a diagnostic aid for detection of abdominal adhesions.