The use of the eye-fixation-related potential to investigate visual perception in professional domains with high attentional demand: a literature review.

INTRODUCTION: Visual attention is a cognitive skill related to visual perception and neural activity, and also moderated by expertise, in time-constrained professional domains (e.g., aviation, driving, sport, surgery). However, the contribution of both perceptual and neural processes on performance has been studied separately in the literature. DEVELOPMENT: We defend an integration of visual and neural signals to offer a more complete picture of the visual attention displayed by professionals of different skill levels when performing free-viewing tasks. Specifically, we propose to zoom the analysis in data related to the quiet eye and P300 component jointly, as a novel signal processing approach to evaluate professionals' visual attention. CONCLUSION: This review highlights the advantages of using portable eye trackers and electroencephalogram systems altogether, as a promising technique for a better understanding of early cognitive components related to attentional processes. Altogether, the eye-fixation-related potentials method may provide a better understanding of the cognitive mechanisms employed by the participants in natural settings, revealing what visual information is of interest for participants and distinguishing the neural bases of visual attention between targets and non-targets whenever they perceive a stimulus during free viewing experiments.

Clinical Validation Benchmark Dataset and Expert Performance Baseline for Colorectal Polyp Localization Methods.

Colorectal cancer is one of the leading death causes worldwide, but, fortunately, early detection highly increases survival rates, with the adenoma detection rate being one surrogate marker for colonoscopy quality. Artificial intelligence and deep learning methods have been applied with great success to improve polyp detection and localization and, therefore, the adenoma detection rate. In this regard, a comparison with clinical experts is required to prove the added value of the systems. Nevertheless, there is no standardized comparison in a laboratory setting before their clinical validation. The ClinExpPICCOLO comprises 65 unedited endoscopic images that represent the clinical setting. They include white light imaging and narrow band imaging, with one third of the images containing a lesion but, differently to another public datasets, the lesion does not appear well-centered in the image. Together with the dataset, an expert clinical performance baseline has been established with the performance of 146 gastroenterologists, who were required to locate the lesions in the selected images. Results shows statistically significant differences between experience groups. Expert gastroenterologists' accuracy was 77.74, while sensitivity and specificity were 86.47 and 74.33, respectively. These values can be established as minimum values for a DL method before performing a clinical trial in the hospital setting.

Deep learning to find colorectal polyps in colonoscopy: A systematic literature review.

Colorectal cancer has a great incidence rate worldwide, but its early detection significantly increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and removal of colorectal lesions with potential to evolve into cancer and computer-aided detection systems can help gastroenterologists to increase the adenoma detection rate, one of the main indicators for colonoscopy quality and predictor for colorectal cancer prevention. The recent success of deep learning approaches in computer vision has also reached this field and has boosted the number of proposed methods for polyp detection, localization and segmentation. Through a systematic search, 35 works have been retrieved. The current systematic review provides an analysis of these methods, stating advantages and disadvantages for the different categories used; comments seven publicly available datasets of colonoscopy images; analyses the metrics used for reporting and identifies future challenges and recommendations. Convolutional neural networks are the most used architecture together with an important presence of data augmentation strategies, mainly based on image transformations and the use of patches. End-to-end methods are preferred over hybrid methods, with a rising tendency. As for detection and localization tasks, the most used metric for reporting is the recall, while Intersection over Union is highly used in segmentation. One of the major concerns is the difficulty for a fair comparison and reproducibility of methods. Even despite the organization of challenges, there is still a need for a common validation framework based on a large, annotated and publicly available database, which also includes the most convenient metrics to report results. Finally, it is also important to highlight that efforts should be focused in the future on proving the clinical value of the deep learning based methods, by increasing the adenoma detection rate.

Unravelling the effect of data augmentation transformations in polyp segmentation.

PURPOSE: Data augmentation is a common technique to overcome the lack of large annotated databases, a usual situation when applying deep learning to medical imaging problems. Nevertheless, there is no consensus on which transformations to apply for a particular field. This work aims at identifying the effect of different transformations on polyp segmentation using deep learning. METHODS: A set of transformations and ranges have been selected, considering image-based (width and height shift, rotation, shear, zooming, horizontal and vertical flip and elastic deformation), pixel-based (changes in brightness and contrast) and application-based (specular lights and blurry frames) transformations. A model has been trained under the same conditions without data augmentation transformations (baseline) and for each of the transformation and ranges, using CVC-EndoSceneStill and Kvasir-SEG, independently. Statistical analysis is performed to compare the baseline performance against results of each range of each transformation on the same test set for each dataset. RESULTS: This basic method identifies the most adequate transformations for each dataset. For CVC-EndoSceneStill, changes in brightness and contrast significantly improve the model performance. On the contrary, Kvasir-SEG benefits to a greater extent from the image-based transformations, especially rotation and shear. Augmentation with synthetic specular lights also improves the performance. CONCLUSION: Despite being infrequently used, pixel-based transformations show a great potential to improve polyp segmentation in CVC-EndoSceneStill. On the other hand, image-based transformations are more suitable for Kvasir-SEG. Problem-based transformations behave similarly in both datasets. Polyp area, brightness and contrast of the dataset have an influence on these differences.

Validation of the three web quality dimensions of a minimally invasive surgery e-learning platform.

INTRODUCTION: E-learning web environments, including the new TELMA platform, are increasingly being used to provide cognitive training in minimally invasive surgery (MIS) to surgeons. A complete validation of this MIS e-learning platform has been performed to determine whether it complies with the three web quality dimensions: usability, content and functionality. METHODS: 21 Surgeons participated in the validation trials. They performed a set of tasks in the TELMA platform, where an e-MIS validity approach was followed. Subjective (questionnaires and checklists) and objective (web analytics) metrics were analysed to achieve the complete validation of usability, content and functionality. RESULTS: The TELMA platform allowed access to didactic content with easy and intuitive navigation. Surgeons performed all tasks with a close-to-ideal number of clicks and amount of time. They considered the design of the website to be consistent (95.24%), organised (90.48%) and attractive (85.71%). Moreover, they gave the content a high score (4.06 out of 5) and considered it adequate for teaching purposes. The surgeons scored the professional language and content (4.35), logo (4.24) and recommendations (4.20) the highest. Regarding functionality, the TELMA platform received an acceptance of 95.24% for navigation and 90.48% for interactivity. CONCLUSIONS: According to the study, it seems that TELMA had an attractive design, innovative content and interactive navigation, which are three key features of an e-learning platform. TELMA successfully met the three criteria necessary for consideration as a website of quality by achieving more than 70% of agreements regarding all usability, content and functionality items validated; this constitutes a preliminary requirement for an effective e-learning platform. However, the content completeness, authoring tool and registration process required improvement. Finally, the e-MIS validity methodology used to measure the three dimensions of web quality in this work can be applied to other clinical areas or training fields.

Virtual reality thread simulation for laparoscopic suturing training.

The level of realism in virtual reality trainers might not be proportional to its didactic value. As an example, three exercises to train suturing skills are proposed in this article. They use a discrete thread model with a simple but good enough behaviour, and constitute a training means for three laparoscopic skills: (1) Accurate grasping, which trains grasping a precise point in the thread. (2) Coordinated Pulling, which trains tightening the thread co-ordinately and in different space orientations; and (3) Knotting, which allow the surgeon to practice this manoeuvre. These three exercises, found interesting among experts in surgical training, are now being validated in MIS workshops at the Minimally Invasive Surgery Centre of Cáceres (Spain).