Can effective pedagogy be ensured in minimally invasive surgery e-learning?

INTRODUCTION: Effectiveness of e-learning diminishes without the support of a pedagogical model to guide its use. In minimally invasive surgery (MIS), this has been reported as a limitation when technology is used to deliver contents without a sound pedagogical background. MATERIAL AND METHODS: We describe how a generic pedagogical model, the 3D pedagogy framework, can be used for setting learning outcomes and activities in e-learning platforms focused on MIS cognitive skills. A demonstrator course on Nissen fundoplication was developed following the model step-by-step in the MISTELA learning platform. Course design was informed by Kolb's Experiential learning model. Content validation was performed by 13 MIS experts. RESULTS: Ten experts agreed on the suitability of content structuring done according to the pedagogical model. All experts agreed that the course provides means to assess the intended learning outcomes. CONCLUSIONS: This work showcases how a general-purpose e-learning framework can be accommodated to the needs of MIS training without limiting the course designers' pedagogical approach. Key advances for its success include: (1) proving the validity of the model in the wider scope of MIS skills and (2) raising awareness amongst stakeholders on the need of developing training plans with explicit, rather than assumed, pedagogical foundations. Abbreviations: MIS: minimally invasive surgery; TEL: technology enhanced learning.

Medical needs related to the endoscopic technology and colonoscopy for colorectal cancer diagnosis.

BACKGROUND: The high incidence and mortality rate of colorectal cancer require new technologies to improve its early diagnosis. This study aims at extracting the medical needs related to the endoscopic technology and the colonoscopy procedure currently used for colorectal cancer diagnosis, essential for designing these demanded technologies. METHODS: Semi-structured interviews and an online survey were used. RESULTS: Six endoscopists were interviewed and 103 were surveyed, obtaining the demanded needs that can be divided into: a) clinical needs, for better polyp detection and classification (especially flat polyps), location, size, margins and penetration depth; b) computer-aided diagnosis (CAD) system needs, for additional visual information supporting polyp characterization and diagnosis; and c) operational/physical needs, related to limitations of image quality, colon lighting, flexibility of the endoscope tip, and even poor bowel preparation. CONCLUSIONS: This study shows some undertaken initiatives to meet the detected medical needs and challenges to be solved. The great potential of advanced optical technologies suggests their use for a better polyp detection and classification since they provide additional functional and structural information than the currently used image enhancement technologies. The inspection of remaining tissue of diminutive polyps (< 5 mm) should be addressed to reduce recurrence rates. Few progresses have been made in estimating the infiltration depth. Detection and classification methods should be combined into one CAD system, providing visual aids over polyps for detection and displaying a Kudo-based diagnosis suggestion to assist the endoscopist on real-time decision making. Estimated size and location of polyps should also be provided. Endoscopes with 360° vision are still a challenge not met by the mechanical and optical systems developed to improve the colon inspection. Patients and healthcare providers should be trained to improve the patient's bowel preparation.

TELMA: Technology-enhanced learning environment for minimally invasive surgery.

BACKGROUND: Cognitive skills training for minimally invasive surgery has traditionally relied upon diverse tools, such as seminars or lectures. Web technologies for e-learning have been adopted to provide ubiquitous training and serve as structured repositories for the vast amount of laparoscopic video sources available. However, these technologies fail to offer such features as formative and summative evaluation, guided learning, or collaborative interaction between users. METHODOLOGY: The "TELMA" environment is presented as a new technology-enhanced learning platform that increases the user's experience using a four-pillared architecture: (1) an authoring tool for the creation of didactic contents; (2) a learning content and knowledge management system that incorporates a modular and scalable system to capture, catalogue, search, and retrieve multimedia content; (3) an evaluation module that provides learning feedback to users; and (4) a professional network for collaborative learning between users. Face validation of the environment and the authoring tool are presented. RESULTS: Face validation of TELMA reveals the positive perception of surgeons regarding the implementation of TELMA and their willingness to use it as a cognitive skills training tool. Preliminary validation data also reflect the importance of providing an easy-to-use, functional authoring tool to create didactic content. CONCLUSION: The TELMA environment is currently installed and used at the Jesús Usón Minimally Invasive Surgery Centre and several other Spanish hospitals. Face validation results ascertain the acceptance and usefulness of this new minimally invasive surgery training environment.

Computational simulation-based comparative analysis of standard 3D printing and conical nozzles for pneumatic and piston-driven bioprinting.

Bioprinting is an application of additive manufacturing that can deliver promising results in regenerative medicine. Hydrogels, as the most used materials in bioprinting, are experimentally analyzed to assure printability and suitability for cell culture. Besides hydrogel features, the inner geometry of the microextrusion head might have an equal impact not only on printability but also on cellular viability. In this regard, standard 3D printing nozzles have been widely studied to reduce inner pressure and get faster printings using highly viscous melted polymers. Computational fluid dynamics is a useful tool capable of simulating and predicting the hydrogel behavior when the extruder inner geometry is modified. Hence, the objective of this work is to comparatively study the performance of a standard 3D printing and conical nozzles in a microextrusion bioprinting process through computational simulation. Three bioprinting parameters, namely pressure, velocity, and shear stress, were calculated using the level-set method, considering a 22G conical tip and a 0.4 mm nozzle. Additionally, two microextrusion models, pneumatic and piston-driven, were simulated using dispensing pressure (15 kPa) and volumetric flow (10 mm3/s) as input, respectively. The results showed that the standard nozzle is suitable for bioprinting procedures. Specifically, the inner geometry of the nozzle increases the flow rate, while reducing the dispensing pressure and maintaining similar shear stress compared to the conical tip commonly used in bioprinting.

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.

EASIER: A new model for online learning of minimally invasive surgery skills.

INTRODUCTION: Technology Enhanced Learning (TEL) can provide the tools to safely master minimally invasive surgery (MIS) skills in patient-free environments and receive immediate objective feedback without the constant presence of an instructor. However, TEL-based systems tend to work isolated from one another, focus on different skills, and fail to provide contents without a sound pedagogical background. OBJECTIVE: The objective of this descriptive study is to present in detail EASIER, an innovative TEL platform for surgical and interventional training, as well as the results of its validation. METHODS: EASIER provides a Learning Management System (LMS) for institutions and content creators that can connect and integrate TEL "external assets" (virtual reality simulators, augmented box trainers, augmented videos, etc.) addressing different skills. The platform integrates all skills under an Assessment Module that measures skills' progress in different courses. Finally, it provides content creators with a pedagogical model to scaffold contents while retaining flexibility to approach course design with different training philosophies in mind. Three courses were developed and hosted in the platform to validate it with end-users in terms of usability, performance, learning results in the courses and student self-perception on learning. RESULTS: In total 111 volunteers completed the validation. The study was limited due to the COVID-19 pandemic, which limited access to external assets (virtual reality simulators). Nevertheless, usability was rated with 73.1 in the System Usability Scale. Most positive aspects on performance were easiness to access the platform, easiness to change the configuration and not requiring additional plug-ins to use the platform. The platform was rated above average in the six scales of the User Experience Questionnaire. Overall, student results improved significantly across the three courses (p < 0.05). CONCLUSIONS: This study provides, within its limitations, evidence on the usefulness of the EASIER platform for distance learning of MIS skills. Results show the potential impact of the platform and are an encouraging boost for the future, especially in the aftermath of the COVID-19 pandemic.

Effects of pneumoperitoneum and body position on the morphology of abdominal vascular structures analyzed in MRI.

PURPOSE: To analyze the complications of laparoscopy in different vascular structures using magnetic resonance imaging (MRI). MRI has become a key tool in laparoscopic surgery. During these procedures, pneumoperitoneum creation and placing the patient in the surgical position provoke different changes in the splachnic circulation. MATERIALS AND METHODS: Ten pigs were included in the study. MRI studies of the abdominal area were carried out in four different situations of abdominal pressure and body position. RESULTS: Changes in the area of the lumen of the portal vein and the abdominal aorta were analyzed in all situations. A significant reduction in the area of the abdominal aorta was observed after the pneumoperitoneum in supine and anti-Trendelemburg position. The lumen of the portal vein was significantly reduced in all analyzed situations except when placing the patient in anti-Trendelemburg without pneumoperitoneum, in which case the area was increased. CONCLUSION: The creation of pneumoperitoneum provokes morphological changes in the lumen of different abdominal vessels as a consequence of the increase of pressure. Furthermore, the combination of pneumoperitoneum together with the anti-Trendelemburg position results in a more significant reduction of the lumen of the portal vein and the abdominal aorta.

Novel Pixelwise Co-Registered Hematoxylin-Eosin and Multiphoton Microscopy Image Dataset for Human Colon Lesion Diagnosis.

Colorectal cancer presents one of the most elevated incidences of cancer worldwide. Colonoscopy relies on histopathology analysis of hematoxylin-eosin (H&E) images of the removed tissue. Novel techniques such as multi-photon microscopy (MPM) show promising results for performing real-time optical biopsies. However, clinicians are not used to this imaging modality and correlation between MPM and H&E information is not clear. The objective of this paper is to describe and make publicly available an extensive dataset of fully co-registered H&E and MPM images that allows the research community to analyze the relationship between MPM and H&E histopathological images and the effect of the semantic gap that prevents clinicians from correctly diagnosing MPM images. The dataset provides a fully scanned tissue images at 10x optical resolution (0.5 µm/px) from 50 samples of lesions obtained by colonoscopies and colectomies. Diagnostics capabilities of TPF and H&E images were compared. Additionally, TPF tiles were virtually stained into H&E images by means of a deep-learning model. A panel of 5 expert pathologists evaluated the different modalities into three classes (healthy, adenoma/hyperplastic, and adenocarcinoma). Results showed that the performance of the pathologists over MPM images was 65% of the H&E performance while the virtual staining method achieved 90%. MPM imaging can provide appropriate information for diagnosing colorectal cancer without the need for H&E staining. However, the existing semantic gap among modalities needs to be corrected.

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.

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.

Approaches towards training in human risk management of surgical technology.

A safe application of modern surgical technology and computer-assisted surgery devices is based on an operation by adequately trained surgeons who are familiar with the benefits and limitations of the devices. We analyzed the in-depth interviews with seven Spanish and 10 German surgeons. Together with other studies, this analysis highlights the need for specific training in technological competence for surgeons. One way to train technological competence is to help surgeons understanding the basic principles of medical devices as well as explaining the basic concepts of risk analysis and risk management. Based on this premise, a stage model for risk assessment was developed and adapted for the training of surgeons. This was developed further into a train the trainer (TTT) concept, which was then evaluated for two example cases. During TTT-training, the trainers (expert surgeons) performed a risk analysis for several medical devices. Afterwards, the trainers organized a surgical workshop for surgical trainees (resident surgeons), in which high-fidelity simulators and the original medical devices were used. The results showed that the surgeons performed the risk analysis correctly with the stage model and afterwards were able to successfully apply the results in the workshop context.

Construct and face validity of SINERGIA laparoscopic virtual reality simulator.

PURPOSE: Laparoscopic techniques have nowadays become a gold standard in many surgical procedures, but they imply a more difficult learning skills process. Simulators have a fundamental role in the formative stage of new surgeons. This paper presents the construct and face validity of SINERGIA laparoscopic virtual reality simulator in order to decide whether it can be considered as an assessment tool. METHODS: Twenty people participated in this study, 14 were novices and 6 were experts. Five tasks of SINERGIA were included in the study: coordination, navigation, navigation and touch, precise grasping and coordinate traction. For each one of these tasks, a certain number of metrics are automatically recorded. All subjects accomplished each task only once and filled in two questionnaires. A statistical analysis was made and results from both groups were compared with the Mann-Whitney U-test, considering significant differences when P < or = 0.05. Internal consistency of the system has been analyzed with the Cronbach's alpha test. RESULTS: Novices and experts positively rated SINERGIA characteristics. At least one of the evaluated metrics of each exercise presented significant differences between both groups. Nevertheless, all metrics under study gave a better punctuation to the executions accomplished by experts (lower time, higher efficiency, fewer errors. . .) than to those made by novices. CONCLUSION: SINERGIA laparoscopic virtual reality simulator is able to discriminate subjects according to their level of experience in laparoscopic surgery; therefore, it can be used within a training program as an assessment tool.