Forecasting pulmonary air leak duration following lung surgery using transpleural airflow data from a digital pleural drainage device.

BACKGROUND: Prolonged air leak (PAL) is often the limiting factor for hospital discharge after lung surgery. Our goal was to develop a statistical model that reliably predicts pulmonary air leak resolution by applying statistical time series modeling and forecasting techniques to digital drainage data. METHODS: Autoregressive Integrated Moving Average (ARIMA) modeling was used to forecast air leak flow from transplural air flow data. The results from ARIMA were retrospectively internally validated with a group of 100 patients who underwent lung resection between December 2012 and March 2017, for whom digital pleural drainage data was available for analysis and a persistent air leak was the limiting factor for chest tube removal. RESULTS: The ARIMA model correctly identified 82% (82/100) of patients as to whether or not the last chest tube removal was appropriate. The performance characteristics of the model in properly identifying patients whose air leak would resolve and who would therefore be candidates for safe chest tube removal were: sensitivity 80% (95% CI, 69-88%), specificity 88% (95% CI, 68-97%), positive predictive value 95% (95% CI, 86-99%), and negative predictive value 59% (95% CI, 42-79%). The false positive and false negative rate was 12% (95% CI, 12-31%) and 20% (95% CI, 12-31%). CONCLUSIONS: We were able to validate a statistical model that that reliably predicted resolution of pulmonary air leak resolution over a 24-hour period. This information may improve the care of patients with chest tube by optimizing duration of pleural drainage.

Fractal-wavelet image denoising revisited.

The essence of fractal image denoising is to predict the fractal code of a noiseless image from its noisy observation. From the predicted fractal code, one can generate an estimate of the original image. We show how well fractal-wavelet denoising predicts parent wavelet subtress of the noiseless image. The performance of various fractal-wavelet denoising schemes (e.g., fixed partitioning, quadtree partitioning) is compared to that of some standard wavelet thresholding methods. We also examine the use of cycle spinning in fractal-based image denoising for the purpose enhancing the denoised estimates. Our experimental results show that these fractal-based image denoising methods are quite competitive with standard wavelet thresholding methods for image denoising. Finally, we compare the performance of the pixel- and wavelet-based fractal denoising schemes.

Fractal image denoising.

Over the past decade, there has been significant interest in fractal coding for the purpose of image compression. However, applications of fractal-based coding to other aspects of image processing have received little attention. We propose a fractal-based method to enhance and restore a noisy image. If the noisy image is simply fractally coded, a significant amount of the noise is suppressed. However, one can go a step further and estimate the fractal code of the original noise-free image from that of the noisy image, based upon a knowledge (or estimate) of the variance of the noise, assumed to be zero-mean, stationary and Gaussian. The resulting fractal code yields a significantly enhanced and restored representation of the original noisy image. The enhancement is consistent with the human visual system where extra smoothing is performed in flat and low activity regions and a lower degree of smoothing is performed near high frequency components, e.g., edges, of the image. We find that, for significant noise variance (sigma > or = 20), the fractal-based scheme yields results that are generally better than those obtained by the Lee filter which uses a localized first order filtering process similar to fractal schemes. We also show that the Lee filter and the fractal method are closely related.