Microbiota ; Forecasting

Humans ; Female ; Cardiovascular Diseases/epidemiology* ; Forecasting

With the development of artificial intelligence (AI) technology, it has a wide range of explorations in orthodontics. AI has greater application prospects in precise measurement, multidimensional diagnosis, treatment planning and efficacy prediction. At the same time, there are certain limitations in the application of AI, such as risks caused by individual variability, black box properties and unclear delineation of medical responsibilities. This paper summarized the history and current status of AI applications in orthodontics and discussed future development trends, to provide reference for clinical orthodontics.
Humans ; Artificial Intelligence ; Orthodontics ; Dental Care ; Forecasting ; Delivery of Health Care

In recent years, the research focus on determinants of chronic non-communicable diseases has shifted from non-spatial factors (e.g., lifestyle behaviors) to spatial factors (e.g., natural and built environments). As the intersection of lifecourse epidemiology and spatial epidemiology, spatial lifecourse epidemiology is a research area in the field of health geography. It combines advanced spatial technologies, including geographic information systems, surveying, remote sensing, location-based services and artificial intelligence, to accurately retrace, measure, and simulate individuals' exposures across the life course (i.e., exposome); and adopts lifecourse models, including the accumulation of risk model and critical/sensitive period models, to investigate the impact of individuals' exposures in the past on their health status at different stages of life. This paper introduces the theories, main analysis approaches and focus of spatial lifecourse epidemiology in the research of chronic non-communicable diseases for the purpose of better understanding and applications of spatial lifecourse epidemiology in the research of determinants of chronic non-communicable diseases, providing important reference for future research, facilitating the development of health geography to eventually achieve precise health management over the lifecourse.
Artificial Intelligence ; Epidemiology ; Forecasting ; Geography ; Health Status ; Humans ; Noncommunicable Diseases/epidemiology*

Artificial Intelligence ; Forecasting ; Humans ; Rare Diseases ; Urinary Bladder Neoplasms/diagnosis*

Acquired Immunodeficiency Syndrome/epidemiology* ; Bayes Theorem ; COVID-19/prevention & control* ; China/epidemiology* ; Communicable Disease Control/trends* ; Disease Notification/statistics & numerical data* ; Forecasting ; Humans ; Incidence ; Interrupted Time Series Analysis ; SARS-CoV-2

Minimally invasive surgery represented by laparoscopic technique has been carried out in China for more than 30 years. Gastrointestinal minimally invasive surgery has been widely recognized and popularized. Today, when the development of minimally invasive technology has reached the ceiling, the authors, who have experienced the innovation of minimally invasive gastrointestinal surgery for more than 30 years, review the gradual, unpredictable but inevitable characteristics of the innovation and development of minimally invasive surgery; figure out that standardized promotion and systematic training are the main reasons for the success of minimally invasive surgery in gastrointestinal surgery; realize that the application and promotion of new medical technology are inseparable from the support of solid clinical and basic evidence; recognize that the re-innovation after the popularization and standardization of gastrointestinal minimally invasive surgery and how to avoid involution are the driving force to seize the development momentum of minimally invasive technology. We make a multidimensional thinking on the development of gastrointestinal minimally invasive surgery, and objectively analyze its development track, in order to calmly rise to the challenges of future technological development.
Digestive System Surgical Procedures/methods* ; Forecasting ; Gastrointestinal Tract/surgery* ; Humans ; Laparoscopy/methods* ; Minimally Invasive Surgical Procedures/methods*

Objectives: Hand, foot and mouth disease (HFMD) is a widespread infectious disease that causes a significant disease burden on society. To achieve early intervention and to prevent outbreaks of disease, we propose a novel warning model that can accurately predict the incidence of HFMD. Methods: We propose a spatial-temporal graph convolutional network (STGCN) that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019. The 2011-2018 data served as the training and verification set, while data from 2019 served as the prediction set. Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error. Results: As the first application using a STGCN for disease forecasting, we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level, especially for cities of significant concern. Conclusions This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance, which may significantly reduce the morbidity associated with HFMD in the future.
China/epidemiology* ; Cities/epidemiology* ; Data Visualization ; Disease Outbreaks/statistics & numerical data* ; Forecasting/methods* ; Hand, Foot and Mouth Disease/prevention & control* ; Humans ; Incidence ; Neural Networks, Computer ; Reproducibility of Results ; Spatio-Temporal Analysis ; Time Factors

China/epidemiology* ; Forecasting ; Humans ; Incidence ; Scarlet Fever/epidemiology*

In recent years, the application of artificial intelligence technology in the field of orthodontics has gradually increased, and deep learning, as a hot direction, has also been rapidly applied in the detection, evaluation, diagnosis, prediction and effect evaluation. At present, deep learning research has the advantages of high efficiency and accuracy, but it also has limitations such as weak interpretability and insufficient data volume. This paper reviewed the proposal and development of deep learning, the application in orthodontic diagnosis and treatment, as well as the limitations and countermeasures of the popularization, and prospect of the future research.
Humans ; Artificial Intelligence ; Deep Learning ; Dental Care ; Forecasting ; Orthodontics