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.

Nowadays, bioprinting is rapidly evolving and hydrogels are a key component for its success. In this sense, synthesis of hydrogels, as well as bioprinting process, and cross-linking of bioinks represent different challenges for the scientific community. A set of unified criteria and a common framework are missing, so multidisciplinary research teams might not efficiently share the advances and limitations of bioprinting. Although multiple combinations of materials and proportions have been used for several applications, it is still unclear the relationship between good printability of hydrogels and better medical/clinical behavior of bioprinted structures. For this reason, a PRISMA methodology was conducted in this review. Thus, 1,774 papers were retrieved from PUBMED, WOS, and SCOPUS databases. After selection, 118 papers were analyzed to extract information about materials, hydrogel synthesis, bioprinting process, and tests performed on bioprinted structures. The aim of this systematic review is to analyze materials used and their influence on the bioprinting parameters that ultimately generate tridimensional structures. Furthermore, a comparison of mechanical and cellular behavior of those bioprinted structures is presented. Finally, some conclusions and recommendations are exposed to improve reproducibility and facilitate a fair comparison of results.

Training and assessment paradigms for laparoscopic surgical skills are evolving from traditional mentor-trainee tutorship towards structured, more objective and safer programs. Accreditation of surgeons requires reaching a consensus on metrics and tasks used to assess surgeons' psychomotor skills. Ongoing development of tracking systems and software solutions has allowed for the expansion of novel training and assessment means in laparoscopy. The current challenge is to adapt and include these systems within training programs, and to exploit their possibilities for evaluation purposes. This paper describes the state of the art in research on measuring and assessing psychomotor laparoscopic skills. It gives an overview on tracking systems as well as on metrics and advanced statistical and machine learning techniques employed for evaluation purposes. The later ones have a potential to be used as an aid in deciding on the surgical competence level, which is an important aspect when accreditation of the surgeons in particular, and patient safety in general, are considered. The prospective of these methods and tools make them complementary means for surgical assessment of motor skills, especially in the early stages of training. Successful examples such as the Fundamentals of Laparoscopic Surgery should help drive a paradigm change to structured curricula based on objective parameters. These may improve the accreditation of new surgeons, as well as optimize their already overloaded training schedules.

Colorectal cancer is one of the world leading death causes. Fortunately, an early diagnosis allows for effective treatment, increasing the survival rate. Deep learning techniques have shown their utility for increasing the adenoma detection rate at colonoscopy, but a dataset is usually required so the model can automatically learn features that characterize the polyps. In this work, we present the PICCOLO dataset, that comprises 3433 manually annotated images (2131 white-light images 1302 narrow-band images), originated from 76 lesions from 40 patients, which are distributed into training (2203), validation (897) and test (333) sets assuring patient independence between sets. Furthermore, clinical metadata are also provided for each lesion. Four different models, obtained by combining two backbones and two encoder–decoder architectures, are trained with the PICCOLO dataset and other two publicly available datasets for comparison. Results are provided for the test set of each dataset. Models trained with the PICCOLO dataset have a better generalization capacity, as they perform more uniformly along test sets of all datasets, rather than obtaining the best results for its own test set. This dataset is available at the website of the Basque Biobank, so it is expected that it will contribute to the further development of deep learning methods for polyp detection, localisation and classification, which would eventually result in a better and earlier diagnosis of colorectal cancer, hence improving patient outcomes.

Currently, tissue engineering has been dedicated to the development of 3D structures through bioprinting techniques that aim to obtain personalized, dynamic, and complex hydrogel 3D structures. Among the different materials used for the fabrication of such structures, proteins and polysaccharides are the main biological compounds (biopolymers) selected for the bioink formulation. These biomaterials obtained from natural sources are commonly compatible with tissues and cells (biocompatibility), friendly with biological digestion processes (biodegradability), and provide specific macromolecular structural and mechanical properties (biomimicry). However, the rheological behaviors of these natural-based bioinks constitute the main challenge of the cell-laden printing process (bioprinting). For this reason, bioprinting usually requires chemical modifications and/or inter-macromolecular crosslinking. In this sense, a comprehensive analysis describing these biopolymers (natural proteins and polysaccharides)-based bioinks, their modifications, and their stimuli-responsive nature is performed. This manuscript is organized into three sections: (1) tissue engineering application, (2) crosslinking, and (3) bioprinting techniques, analyzing the current challenges and strengths of biopolymers in bioprinting. In conclusion, all hydrogels try to resemble extracellular matrix properties for bioprinted structures while maintaining good printability and stability during the printing process.