Coronary heart disease is the second leading cause of death worldwide. As a preventable and treatable chronic disease, early screening is of great importance for disease control. However, previous screening tools relied on physician assistance, thus cannot be used on a large scale. Many facial features have been reported to be associated with coronary heart disease and may be useful for screening. However, these facial features have limitations such as fewer types, irregular definitions and poor repeatability of manual judgment, so they can not be routinely applied in clinical practice. With the development of artificial intelligence, it is possible to integrate facial features to predict diseases. A recent study published in the European Heart Journal showed that coronary heart disease can be predicted using artificial intelligence based on facial photos. Although this work still has some limitations, this novel technology will be promise for improving disease screening and diagnosis in the future.
Objective To broaden the current understanding of the usage willingness about artificial intelligence (AI) robots and relevant influence factors for elderly patients. Methods The elderly patients in the inpatient ward, outpatient department and physical examination of the Department of Geriatrics, West China Hospital of Sichuan University were selected by convenient sampling for investigation between February and April 2020, to explore the willingness of elderly patients to use AI robots and related influencing factors. Results A total of 446 elderly patients were included. There were 244 males and 202 females. The willingness to use AI robots was (14.40±3.62) points. There were statistically significant differences among the elderly patients with different ages, marital status, living conditions, educational level, current health status, current vision status, current hearing status, self-care ability and family support in their willingness to use AI robots (P<0.05). Multiple linear regression analysis showed that age, education level and family support were the influencing factors of use intention (P<0.05). Among the elderly patients, 60.76% had heard of AI robots, but only 28.03% knew the medical application of AI robots, and only 13.90% had used AI robot services. Most elderly patients (>60%) thought that some adverse factors may reduce their usage willingness, like “the price is too expensive” and “the use is complex, or I don’t know how to use”. Conclusions Elderly patients’ cognition of AI robots is still at a low level, and their willingness to use AI robots is mainly affected by age, education level and family support. It is suggested to consider the personalized needs of the elderly in terms of different ages, education levels and family support, and promote the cheap and user-friendly AI robots, so as to improve the use of AI robots by elderly patients.
China is facing the peak of an ageing population, and there is an increase in demand for intelligent healthcare services for the elderly. The metaverse, as a new internet social communication space, has shown infinite potential for application. This paper focuses on the application of the metaverse in medicine in the intervention of cognitive decline in the elderly population. The problems in assessment and intervention of cognitive decline in the elderly group were analyzed. The basic data required to construct the metaverse in medicine was introduced. Moreover, it is demonstrated that the elderly users can conduct self-monitoring, experience immersive self-healing and health-care through the metaverse in medicine technology. Furthermore, we proposed that it is feasible that the metaverse in medicine has obvious advantages in prediction and diagnosis, prevention and rehabilitation, as well as assisting patients with cognitive decline. Risks for its application were pointed out as well. The metaverse in medicine technology solves the problem of non-face-to-face social communication for elderly users, which may help to reconstruct the social medical system and service mode for the elderly population.
With the development of artificial intelligence (AI) technology, great progress has been made in the application of AI in the medical field. While foreign journals have published a large number of papers on the application of AI in epilepsy, there is a dearth of studies within domestic journals. In order to understand the global research progress and development trend of AI applications in epilepsy, a total of 895 papers on AI applications in epilepsy included in the Web of Science Core Collection and published before December 31, 2022 were selected as the research objects. The annual number of papers and their cited times, the most published authors, institutions and countries, and their cooperative relationships were analyzed, and the research hotspots and future trends in this field were explored by using bibliometrics and other methods. The results showed that before 2016, the annual number of papers on the application of AI in epilepsy increased slowly, and after 2017, the number of publications increased rapidly. The United States had the largest number of papers (n=273), followed by China (n=195). The institution with the largest number of papers was the University of London (n=36), and Capital Medical University in China had 23 papers. The author with the most published papers was Gregory Worrell (n=14), and the scholar with the most published articles in China was Guo Jiayan from Xiamen University (n=7). The application of machine learning in the diagnosis and treatment of epilepsy is an early research focus in this field, while the seizure prediction model based on EEG feature extraction, deep learning especially convolutional neural network application in epilepsy diagnosis, and cloud computing application in epilepsy healthcare, are the current research priorities in this field. AI-based EEG feature extraction, the application of deep learning in the diagnosis and treatment of epilepsy, and the Internet of things to solve epilepsy health-related problems are the research aims of this field in the future.
Objective To develop a neural network architecture based on deep learning to assist knee CT images automatic segmentation, and validate its accuracy. Methods A knee CT scans database was established, and the bony structure was manually annotated. A deep learning neural network architecture was developed independently, and the labeled database was used to train and test the neural network. Metrics of Dice coefficient, average surface distance (ASD), and Hausdorff distance (HD) were calculated to evaluate the accuracy of the neural network. The time of automatic segmentation and manual segmentation was compared. Five orthopedic experts were invited to score the automatic and manual segmentation results using Likert scale and the scores of the two methods were compared. Results The automatic segmentation achieved a high accuracy. The Dice coefficient, ASD, and HD of the femur were 0.953±0.037, (0.076±0.048) mm, and (3.101±0.726) mm, respectively; and those of the tibia were 0.950±0.092, (0.083±0.101) mm, and (2.984±0.740) mm, respectively. The time of automatic segmentation was significantly shorter than that of manual segmentation [(2.46±0.45) minutes vs. (64.73±17.07) minutes; t=36.474, P<0.001). The clinical scores of the femur were 4.3±0.3 in the automatic segmentation group and 4.4±0.2 in the manual segmentation group, and the scores of the tibia were 4.5±0.2 and 4.5±0.3, respectively. There was no significant difference between the two groups (t=1.753, P=0.085; t=0.318, P=0.752). Conclusion The automatic segmentation of knee CT images based on deep learning has high accuracy and can achieve rapid segmentation and three-dimensional reconstruction. This method will promote the development of new technology-assisted techniques in total knee arthroplasty.
This article is based on the work practice of Deyang People’s Hospital in carrying out financial digital transformation under the background of artificial intelligence technology. It clarifies the concepts of financial digitization and artificial intelligence technology, summarizes the practical path of hospital financial digital transformation, and analyzes the specific applications and implementation effects of intelligent filling of expense reimbursement forms, intelligent review of documents, and intelligent management of medical insurance funds. These experiences have positive significance for optimizing financial business processes, improving data quality and utilization efficiency, and enhancing employee satisfaction. They can provide a reference for the digital transformation of financial management in public hospitals and the reconstruction of the value positioning of hospital financial management.
Cardiovascular diseases is the leading cause of threat to human life and health worldwide. Early risk assessment, timely diagnosis, and prognosis evaluation are critical to the treatment of cardiovascular diseases. Currently, the evaluation of diagnosis and prognosis of cardiovascular diseases mainly relies on imaging examinations such as coronary CT and coronary angiography, which are expensive, time-consuming, partly invasive, and require high professional competence of the operator, making it difficult to promote in the community or in areas where medical resources are scarce. The fundus microcirculation is a part of the human microcirculation and has similar embryological origins and physiopathological features to cardiovascular circulation. Several studies have revealed fundus imaging biomarkers associated with cardiovascular diseases, and developed and validated intelligent diagnosis and treatment models for cardiovascular diseases based on fundus imaging data. Fundus imaging is expected to be an important adjunct to cardiovascular disease diagnosis and treatment given its noninvasive and convenient nature. The purpose of this review is to summarize the current research status, challenges, and future prospects of the application of artificial intelligence based on multimodal fundus imaging data in cardiovascular disease diagnosis and treatment.
ObjectiveTo systematically evaluate the efficacy and safety of computer-aided detection (CADe) and conventional colonoscopy in identifying colorectal adenomas and polyps. MethodsThe PubMed, Embase, Cochrane Library, Web of Science, WanFang Data, VIP, and CNKI databases were electronically searched to collect randomized controlled trials (RCTs) comparing the effectiveness and safety of CADe assisted colonoscopy and conventional colonoscopy in detecting colorectal tumors from 2014 to April 2023. Two reviewers independently screened the literature, extracted data, and evaluated the risk of bias of the included literature. Meta-analysis was performed by RevMan 5.3 software. ResultsA total of 9 RCTs were included, with a total of 6 393 patients. Compared with conventional colonoscopy, the CADe system significantly improved the adenoma detection rate (ADR) (RR=1.22, 95%CI 1.10 to 1.35, P<0.01) and polyp detection rate (PDR) (RR=1.19, 95%CI 1.04 to 1.36, P=0.01). It also reduced the missed diagnosis rate (AMR) of adenomas (RR=0.48, 95%CI 0.34 to 0.67, P<0.01) and the missed diagnosis rate (PMR) of polyps (RR=0.39, 95%CI 0.25 to 0.59, P<0.01). The PDR of proximal polyps significantly increased, while the PDR of ≤5 mm polyps slightly increased, but the PDR of >10mm and pedunculated polyps significantly decreased. The AMR of the cecum, transverse colon, descending colon, and sigmoid colon was significantly reduced. There was no statistically significant difference in the withdrawal time between the two groups. Conclusion The CADe system can increase the detection rate of adenomas and polyps, and reduce the missed diagnosis rate. The detection rate of polyps is related to their location, size, and shape, while the missed diagnosis rate of adenomas is related to their location.
With the development of artificial intelligence, machine learning has been widely used in diagnosis of diseases. It is crucial to conduct diagnostic test accuracy studies and evaluate the performance of models reasonably to improve the accuracy of diagnosis. For machine learning-based diagnostic test accuracy studies, this paper introduces the principles of study design in the aspects of target conditions, selection of participants, diagnostic tests, reference standards and ethics.
ObjectiveTo explore the efficacy of artificial intelligence (AI) detection on pulmonary nodule compared with multidisciplinary team (MDT) in regional medical center.MethodsWe retrospectively analyzed the clinical data of 102 patients with lung nodules in the Xiamen Fifth Hospital from April to December 2020. There were 57 males and 45 females at age of 36-90 (48.8±11.6) years. The preoperative chest CT was imported into AI system to record the detected lung nodules. The detection rate of pulmonary nodules by AI system was calculated, and the sensitivity, specificity of AI in the different diagnosis of benign and malignant pulmonary was calculated and compared with manual film reading by MDT.ResultsA total of 322 nodules were detected by AI software system, and 305 nodules were manually detected by physicians (P<0.05). Among them, 113 pulmonary nodules were diagnosed by pathologist. Thirty-eight of 40 lung cancer nodules were AI high-risk nodules, the sensitivity was 95.0%, and 25 of 73 benign nodules were AI high-risk nodules, the specificity was 65.8%. Lung cancer nodules were correctly diagnosed by MDT, but benign nodules were still considered as lung cancer at the first diagnosis in 10 patients.ConclusionAI assisted diagnosis system has strong performance in the detection of pulmonary nodules, but it can not content itself with clinical needs in the differentiation of benign and malignant pulmonary nodules. The artificial intelligence system can be used as an auxiliary tool for MDT to detect pulmonary nodules in regional medical center.