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Background Facial features and measurements are utilized to analyze patients’ faces for various reasons, including surgical planning, scientific communications, patient-surgeon communications, and post-surgery evaluations. Objectives There are numerous descriptions regarding these features and measurements scattered throughout the literature and we did not encounter a current compilation of these parameters in the medical literature. Methods A narrative literature review of the published medical literature for facial measurements used for facial analysis in rhinoplasty was done through the electronic databases MEDLINE/PubMed and Google Scholar, along with a citation search. Results A total of 61 facial features were identified. 45 points (25 bilateral, 20 unilateral), five lines (three bilateral, two unilateral), eight planes, and three areas. A total of 122 measurements were identified: 48 distances (6 bilateral, 42 unilateral), 57 angles (13 bilateral, 44 unilateral), and 17 ratios. Supplemental Figures were created to depict all features and measurements using either a frontal, lateral or basal view of the face. Conclusions This paper provides the most comprehensive and current compilation of facial measurements to date. We believe this compilation will guide further developments (methodologies and software tools) for analyzing nasal structures and assessing the objective outcomes of facial surgeries, in particular rhinoplasty. Moreover, it will improve the communication as a reference for facial measurements of facial surface anthropometry, in particular rhinoplasty.

The cognitive capabilities of children develop during the early years of their life. Research shows that learning a foreign language helps develop cognitive skills. Moreover, learning a foreign language has become essential and an increasing number of parents would like their kids to start learning a foreign language at an early age. However, engaging little kids with learning activities is challenging. In this study, we propose a framework for developing a language learning software tool utilizing Augmented Reality (AR), Voicebots, and ChatGPT (an AI utilizing the Large Language Model) technologies to provide a unique product for small kids to teach a foreign language. With AR and Voicebots, the product will grab attention, motivate and provide an entertaining learning environment. The capabilities of ChatGPT will be utilized to efficiently prepare the content for the software tool. We utilize the capabilities of ChatGPT to generate interactive dialogs that will be hosted at Google DialogFlow. We believe the framework and the design principles we propose in this study can be a blueprint for developing highly effective foreign language teaching software.

The increased availability and affordability of 3D scanning technology due to recent advancements have made pre and post-operative analysis for facial surgeries more convenient and accurate through the use of 3D patient scans. However, to perform a more comprehensive analysis using area and volume measurements, it is necessary to develop new software tools that include the appropriate algorithms. Without the right algorithms, any software tool cannot perform its intended tasks effectively. In this study, we introduce two algorithms along with their open-source code to calculate the surface area and volume of a specific subsection of a 3D model. The first algorithm partitions the area/volume into smaller segmentations and applies raycasting to find the points on the face surface, and then finds an approximate measurement depending on the resolution selected. In contrast, the second algorithm utilizes the vertex information stored in the 3D model file to calculate the area and volume measurements for the designated region. The advantage of the second algorithm is that it examines every point in the model, resulting in the highest degree of precision possible. We have also used ChatGPT to generate the code for area/volume calculation and found that the logic of the ChatGPT-generated code is surprisingly similar to the second algorithm. We have presented the results of the measurements on certain regions of the face for both of the algorithms and compared the temporal performance of the algorithms.

This study focuses on the utilization of Large Language Models (LLMs) for the rapid development of applications, with a spotlight on LangChain, an open-source software library. LLMs have been rapidly adopted due to their capabilities in a range of tasks, including essay composition, code writing, explanation, and debugging, with OpenAI’s ChatGPT popularizing their usage among millions ofusers. The crux of the study centers around LangChain, designed to expedite the development of bespoke AI applications using LLMs. LangChain has been widely recognized in the AI community for its ability to seamlessly interact with various data sources and applications. The paper provides an examination of LangChain's core features, including its components and chains, acting as modular abstractions and customizable, use-case-specific pipelines, respectively. Through a series of practical examples, the study elucidates the potential of this framework in fostering the swift development of LLM-based applications.