Closed-loop anesthesia: foundations and applications in contemporary perioperative medicine.

A closed-loop automatically controls a variable using the principle of feedback. Automation within anesthesia typically aims to improve the stability of a controlled variable and reduce workload associated with simple repetitive tasks. This approach attempts to limit errors due to distractions or fatigue while simultaneously increasing compliance to evidence based perioperative protocols. The ultimate goal is to use these advantages over manual care to improve patient outcome. For more than twenty years, clinical studies in anesthesia have demonstrated the superiority of closed-loop systems compared to manual control for stabilizing a single variable, reducing practitioner workload, and safely administering therapies. This research has focused on various closed-loops that coupled inputs and outputs such as the processed electroencephalogram with propofol, blood pressure with vasopressors, and dynamic predictors of fluid responsiveness with fluid therapy. Recently, multiple simultaneous independent closed-loop systems have been tested in practice and one study has demonstrated a clinical benefit on postoperative cognitive dysfunction. Despite their advantages, these tools still require that a well-trained practitioner maintains situation awareness, understands how closed-loop systems react to each variable, and is ready to retake control if the closed-loop systems fail. In the future, multiple input multiple output closed-loop systems will control anesthetic, fluid and vasopressor titration and may perhaps integrate other key systems, such as the anesthesia machine. Human supervision will nonetheless always be indispensable as situation awareness, communication, and prediction of events remain irreplaceable human factors.

Optical Coherence Tomography-Based Scattering Properties of Retinal Vessels in Glaucoma Patients.

PURPOSE: Clinical trials have demonstrated that retinal blood flow deficiencies are present in patients with open-angle glaucoma (OAG). We introduce a method for facilitating retinal vessel analysis: The intensity of the distal shadow of vessels in optical coherence tomography (OCT) caused by the scattered signal is analyzed, compared between healthy subjects and OAG patients and correlated with OCT angiography (OCT-A) flow density. PATIENTS AND METHODS: We recruited 80 patients with diagnosed OAG (mean age 63.4 ± 13.2 years) and 80 healthy age-matched control subjects for comparison, and 20 patients for the correlation with OCT-A flow density. Patients received perimetry, peripapillary OCT measurements, and selected patients OCT-A of the papillary area. The vessel shadow intensity (VSI) is based on peripapillary OCT scans: the intensity of the distal vessel shadow was automatically compared to its surroundings, separated by arteries and veins. Flow density of the OCT-A scan was calculated by binarization and quantification of the pixel density. RESULTS: The VSI for arteries was in OAG patients with 7.52 ± 2.62 [%] significantly lower compared to healthy subjects (9.03 ± 3.38 [%], p = 0.0029). In veins, the VSI was as well significantly lower for OAG patients (14.9 ± 3.59 [%]) compared to healthy subjects (17.46 ± 4.45 [%], p < 0.0001). Furthermore, in OAG patients there was a significant correlation of mean deviation of the visual field results with the veins' VSI (p = 0.0006; r = -0,454). There was no significant correlation of scattering properties with OCT-A flow density (p > 0.05). CONCLUSIONS: We conclude that the OCT-based analysis of the scattering properties of retinal vessels differs significantly between patients with OAG and healthy subjects. Furthermore, changes in the scattering properties of veins correlated with the stage of the disease in terms of visual field deficits. These properties might complement existing measurements of ocular blood flow including OCT-A flow density.

An Automated Approach for Localizing Retinal Blood Vessels in Confocal Scanning Laser Ophthalmoscopy Fundus Images.

In this work, we present a rules-based method for localizing retinal blood vessels in confocal scanning laser ophthalmoscopy (cSLO) images and evaluate its feasibility. A total of 31 healthy participants (17 female; mean age: 64.0 ± 8.2 years) were studied using manual and automatic segmentation. High-resolution peripapillary scan acquisition cSLO images were acquired. The automated segmentation method consisted of image pre-processing for gray-level homogenization and blood vessel enhancement (morphological opening operation, Gaussian filter, morphological Top-Hat transformation), binary thresholding (entropy-based thresholding operation), and removal of falsely detected isolated vessel pixels. The proposed algorithm was first tested on the publically available dataset DRIVE, which contains color fundus photographs, and compared to performance results from the literature. Good results were obtained. Monochromatic cSLO images segmented using the proposed method were compared to those manually segmented by two independent observers. For the algorithm, a sensitivity of 0.7542, specificity of 0.8607, and accuracy of 0.8508 were obtained. For the two independent observers, a sensitivity of 0.6579, specificity of 0.9699, and accuracy of 0.9401 were obtained. The results demonstrate that it is possible to localize vessels in monochromatic cSLO images of the retina using a rules-based approach. The performance results are inferior to those obtained using fundus photography, which could be due to the nature of the technology.