The mouth, often regarded as a window to the internal state of the body,
plays an important role in reflecting one's overall health. Poor oral hygiene
has far-reaching consequences, contributing to severe conditions like heart
disease, cancer, and diabetes, while inadequate care leads to discomfort, pain,
and costly treatments. Federated Learning (FL) for object detection can be
utilized for this use case due to the sensitivity of the oral image data of the
patients. FL ensures data privacy by storing the images used for object
detection on the local device and trains the model on the edge. The updated
weights are federated to a central server where all the collected weights are
updated via The Federated Averaging algorithm. Finally, we have developed a
mobile app named OralH which provides user-friendly solutions, allowing people
to conduct self-assessments through mouth scans and providing quick oral health
insights. Upon detection of the issues, the application alerts the user about
potential oral health concerns or diseases and provides details about dental
clinics in the user's locality. Designed as a Progressive Web Application
(PWA), the platform ensures ubiquitous access, catering to users across devices
for a seamless experience. The application aims to provide state-of-the-art
segmentation and detection techniques, leveraging the YOLOv8 object detection
model to identify oral hygiene issues and diseases. This study deals with the
benefits of leveraging FL in healthcare with promising real-world results.

使用联邦学习的口腔卫生移动应用程序（OralH）能够通过口腔扫描快速检测口腔健康问题，并提供相关信息和诊所详情。

基于联邦学习的口腔疾病检测移动应用

Mobile Application for Oral Disease Detection using Federated Learning

Dental disease is one of the most common chronic diseases despite being
largely preventable. However, professional advice on optimal oral hygiene
practices is often forgotten or abandoned by patients. Therefore patients may
benefit from timely and personalized encouragement to engage in oral self-care
behaviors. In this paper, we develop an online reinforcement learning (RL)
algorithm for use in optimizing the delivery of mobile-based prompts to
encourage oral hygiene behaviors. One of the main challenges in developing such
an algorithm is ensuring that the algorithm considers the impact of the current
action on the effectiveness of future actions (i.e., delayed effects),
especially when the algorithm has been made simple in order to run stably and
autonomously in a constrained, real-world setting (i.e., highly noisy, sparse
data). We address this challenge by designing a quality reward which maximizes
the desired health outcome (i.e., high-quality brushing) while minimizing user
burden. We also highlight a procedure for optimizing the hyperparameters of the
reward by building a simulation environment test bed and evaluating candidates
using the test bed. The RL algorithm discussed in this paper will be deployed
in Oralytics, an oral self-care app that provides behavioral strategies to
boost patient engagement in oral hygiene practices.

本文开发了基于强化学习的算法，用于优化移动提示的发送，以鼓励口腔自我护理行为。研究致力于解决算法中的延迟效应问题，通过设计质量奖励并优化超参数选择，最终被部署在提供行为策略以提高口腔健康的 Oralytics 应用程序上。