Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Entropy of gabor filtering for image quality assessment

A new algorithm for image quality assessment based on entropy of Gabor filtered images is proposed. A bank of Gabor filters is used to extract contours and directional textures. Then, the entropy of the images obtained after the Gabor filtering is calculated. Finally, a metric for the image quality is proposed. It is important to note that the quality of the image is image content-dependent, so our metric must be applied to variations of the same scene, like in image acquisition and image processing tasks. This process makes up an interesting tool to evaluate the quality of image acquisition systems or to adjust them to obtain the best possible images for further processing tasks. An image database has been created to test the algorithm with series of images degraded by four methods that simulate image acquisition usual problems. The presented results show that the proposed method accurately measures image quality, even with slight degradations

A computer vision system for visual grape grading in wine cellars

This communication describes a computer vision system for automatic visual inspection and classification of grapes in cooperative wine cellars. The system is intended to work outdoors, so robust algorithms for preprocessing and segmentation are implemented. Specific methods for illumination compensation have been developed. Gabor filtering has been used for segmentation. Several preliminary classification schemes, using artificial neural networks and Random Forest, have also been tested. The obtained results show the benefits of the system as a useful tool for classification and for objective price fixing.Xunta de Galicia | Ref. FEADER2008-1

A genetic algorithm approach for feature selection in potatoes classification by computer vision

Potato quality control has improved in the last years thanks to automation techniques like machine vision, mainly making the classification task between different quality degrees faster, safer and less subjective. We present a system that classifies potatoes depending on their external defects and diseases. Firstly, some image processing techniques are used to segment and analyze the potatoes. Then, a classifier is used to decide the group the potato belongs to. For the feature selection task, we have designed an ad-hoc genetic algorithm which maximizes the classification percentage. This approach is used to perform an optimization in the search of the better feature combination. The system shows to be effective in real operation simulations (working with unwashed potatoes covered with dust and sand,), what seems to be a good starting point in the development of the system.Xunta de Galicia | Ref. 08TIC004C