Estimation Of Clinical Parameters Research Articles

Reducing segmentation failures in cardiac MRI via late feature fusion and GAN-based augmentation

Cardiac magnetic resonance (CMR) image segmentation is an integral step in the analysis of cardiac function and diagnosis of heart related diseases. While recent deep learning-based approaches in automatic segmentation have shown great promise to alleviate the need for manual segmentation, most of these are not applicable to realistic clinical scenarios. This is largely due to training on mainly homogeneous datasets, without variation in acquisition, which typically occurs in multi-vendor and multi-site settings, as well as pathological data. Such approaches frequently exhibit a degradation in prediction performance, particularly on outlier cases commonly associated with difficult pathologies, artifacts and extensive changes in tissue shape and appearance. In this work, we present a model aimed at segmenting all three cardiac structures in a multi-center, multi-disease and multi-view scenario. We propose a pipeline, addressing different challenges with segmentation of such heterogeneous data, consisting of heart region detection, augmentation through image synthesis and a late-fusion segmentation approach. Extensive experiments and analysis demonstrate the ability of the proposed approach to tackle the presence of outlier cases during both training and testing, allowing for better adaptation to unseen and difficult examples. Overall, we show that the effective reduction of segmentation failures on outlier cases has a positive impact on not only the average segmentation performance, but also on the estimation of clinical parameters, leading to a better consistency in derived metrics.

Evaluation of machine learning models as decision aids for anesthesiologists.

Machine Learning (ML) models have been developed to predict perioperative clinical parameters. The objective of this study was to determine if ML models can serve as decision aids to improve anesthesiologists' prediction of peak intraoperative glucose values and postoperative opioid requirements. A web-based tool was used to present actual surgical case and patient information to 10 practicing anesthesiologists. They were asked to predict peak glucose levels and post-operative opioid requirements for 100 surgical patients with and without presenting ML model estimations of peak glucose and opioid requirements. The accuracies of the anesthesiologists' estimates with and without ML estimates as reference were compared. A questionnaire was also sent to the participating anesthesiologists to obtain their feedback on ML decision support. The accuracy of peak glucose level estimates by the anesthesiologists increased from 79.0 ± 13.7% without ML assistance to 84.7 ± 11.5% (< 0.001) when ML estimates were provided as reference. The accuracy of opioid requirement estimates increased from 18% without ML assistance to 42% (p < 0.001) when ML estimates were provided as reference. When ML estimates were provided, predictions of peak glucose improved for 8 out of the 10 anesthesiologists, while predictions of opioid requirements improved for 7 of the 10 anesthesiologists. Feedback questionnaire responses revealed that the anesthesiologist primarily used the ML estimates as reference to modify their clinical judgement. ML models can improve anesthesiologists' estimation of clinical parameters. ML predictions primarily served as reference information that modified an anesthesiologist's clinical estimate.

Corneal Endothelial Cell Segmentation by Classifier-Driven Merging of Oversegmented Images.

Corneal endothelium images obtained by in vivo specular microscopy provide important information to assess the health status of the cornea. Estimation of clinical parameters, such as cell density, polymegethism, and pleomorphism, requires accurate cell segmentation. State-of-the-art techniques to automatically segment the endothelium are error-prone when applied to images with low contrast and/or large variation in cell size. Here, we propose an automatic method to segment the endothelium. Starting with an oversegmented image comprised of superpixels obtained from a stochastic watershed segmentation, the proposed method uses intensity and shape information of the superpixels to identify and merge those that constitute a cell, using support vector machines. We evaluated the automatic segmentation on a data set of in vivo specular microscopy images (Topcon SP-1P), obtaining 95.8% correctly merged cells and 2.0% undersegmented cells. We also evaluated the parameter estimation against the results of the vendor's built-in software, obtaining a statistically significant better precision in all parameters and a similar or better accuracy. The parameter estimation was also evaluated on three other data sets from different imaging modalities (confocal microscopy, phase-contrast microscopy, and fluorescence confocal microscopy) and tissue types (ex vivo corneal endothelium and retinal pigment epithelium). In comparison with the estimates of the data sets' authors, we achieved statistically significant better accuracy and precision in all parameters except pleomorphism, where a similar accuracy and precision were obtained.

Cost-effectiveness of Revascularization for Limb Preservation in Patients with Marginal Functional Status

Revascularization and limb salvage attempts are often offered to patients with foot wounds and chronic limb ischemia who are thought to be good-risk candidates, but some skepticism remains about the utility of these efforts for elderly patients with marginal functional status. We sought to determine whether limb preservation efforts in this population could be justified from a patient-centered, cost-effectiveness perspective. A probabilistic Markov model was used to simulate the clinical outcomes, health utilities, and costs over a 10-year period with various management strategies. Clinical parameter estimates were obtained from previous clinical trials and large observational series. Cost estimates were obtained from cost literature and also a single-center study that reviewed total costs accumulated (including secondary amputations, wound care, outpatient nursing care, and nursing home costs). Cost (in 2011 U.S. dollars) per year of ambulation (with limb preservation or with a prosthesis after amputation) was the primary measure of cost-effectiveness. The total 10-year costs of revascularization--either endovascular or surgical--were lower than the costs of either local wound care alone or primary amputation. Revascularization strategies also produced more health benefits as measured in terms of years of ambulatory ability, years of limb salvage, or quality-adjusted life-years. In none of the scenarios modeled in deterministic sensitivity analyses did primary amputation prove to be cost-effective. Revascularization and limb preservation attempts appear less costly and provide more health benefits than wound care alone or primary amputation, even among patients with marginal functional status at baseline.