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Background/Objectives: To develop a deep learning model for esophageal motility disorder diagnosis using high-resolution manometry images with the aid of Gemini. Methods: Gemini assisted in developing this model by aiding in code writing, preprocessing, model optimization, and troubleshooting. Results: The model demonstrated an overall precision of 0.89 on the testing set, with an accuracy of 0.88, a recall of 0.88, and an F1-score of 0.885. It presented better results for multiple categories, particularly in the panesophageal pressurization category, with precision = 0.99 and recall = 0.99, yielding a balanced F1-score of 0.99. Conclusions: This study demonstrates the potential of artificial intelligence, particularly Gemini, in aiding the creation of robust deep learning models for medical image analysis, solving not just simple binary classification problems but more complex, multi-class image classification tasks. [ABSTRACT FROM AUTHOR]

The goal of this paper is to provide a Machine Learning-based solution that can be utilized to automate the Chicago Classification algorithm, the state-of-the-art scheme for esophageal motility disease identification. First, the photos were preprocessed by locating the area of interest—the precise instant of swallowing. After resizing and rescaling the photos, they were utilized as input for the Deep Learning models. The InceptionV3 Deep Learning model was used to identify the precise class of the IRP. We used the DenseNet201 CNN architecture to classify the images into 5 different classes of swallowing disorders. Finally, we combined the results of the two trained ML models to automate the Chicago Classification algorithm. With this solution we obtained a top-1 accuracy and f1-score of 86% with no human intervention, automating the whole flow, from image preprocessing until Chicago classification and diagnosis. [ABSTRACT FROM AUTHOR]

In the increasingly researched field of gamification, that is the application of the game design elements in no game contexts, one of the most used techniques is the use of digital badges. Through this paper, we conducted a literature review on the use and effectiveness of badges. We include other literature reviews, empirical case studies, and application reports. This analysis shows the widespread use, the different types and specialized nature of digital badges throughout the private and public sectors. [ABSTRACT FROM AUTHOR]

A large part of the job of a university teacher in computer science is to verify student exercises. The task of verifying correctness, coding style, conventions, and grading is laborious and if done correctly leaves little time for questions. Automating aspects of this work helps all parties involved by freeing time up for questions. The solution detailed in the paper automates a number of these tasks and provides near instant feedback for the students, together with a platform to gamify student learning. [ABSTRACT FROM AUTHOR]

Background: Fine needle aspiration (FNA) cytology is a well-established diagnostic method based on the microscopic interpretation of often scant cytological material; therefore, experience, good technique and smear quality are equally important in obtaining satisfactory results. Aims of Study: We studied the use of fresh surgical pathology specimens for making so-called mock-FNA smears with the potential of cytohistological correlation. Additionally, we studied how this process aids the improvement of preparation technique and smear quality. Methods: Cytological aspirates from 32 fresh biopsy specimens from various sites: lung (20), lymph nodes (6), and breast (6) were obtained, all with a clinical diagnosis of tumor. Aspiration was performed from grossly palpable tumors. 25G needle and Cameco-type syringe holder was used with minimal or no suction. Results: Unfixed surgical specimens provided sufficient cytological material that resulted in good quality smears. After standard processing of specimens into microscopic sections from paraffin embedded tissues, cytohistological case-series were created. No significant alteration was reported in tissue architecture on hematoxylin-eosin stained sections after the aspiration procedure. A gradual, but steady improvement was observed in smear quality just after a few preparations. Discussions and Conclusions: Our study proved that surgical specimens may be used as a source of cytological material to create cytohistological correlation studies and also to improve FNA cytology skills. The use of very fine gauge needle (25G, 0,6 mm diameter) during the sampling process does not alter tissue architecture therefore the final histopathological diagnosis is not compromised. We conclude that by using fresh surgical specimens useful cytohistological collections can be created both as a teaching resource and as improving experience. [ABSTRACT FROM AUTHOR]