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.
ObjectiveTo observe the diagnostic value of six classification intelligent auxiliary diagnosis lightweight model for common fundus diseases based on fundus color photography. MethodsA applied research. A dataset of 2 400 color fundus images from Nanjing Medical University Eye Hospital and Zhejiang Mathematical Medical Society Smart Eye Database was collected, which was desensitized and labeled by a fundus specialist. Of these, 400 each were for diabetic retinopathy, glaucoma, retinal vein occlusion, high myopia, age-related macular degeneration, and normal fundus. The parameters obtained from the classical classification models VGGNet16, ResNet50, DenseNet121 and lightweight classification models MobileNet3, ShuffleNet2, GhostNet trained on the ImageNet dataset were migrated to the six-classified common fundus disease intelligent aid diagnostic model using a migration learning approach during training as initialization parameters for training to obtain the latest model. 1 315 color fundus images of clinical patients were used as the test set. Evaluation metrics included sensitivity, specificity, accuracy, F1-Score and agreement of diagnostic tests (Kappa value); comparison of subject working characteristic curves as well as area under the curve values for different models. ResultCompared with the classical classification model, the storage size and number of parameters of the three lightweight classification models were significantly reduced, with ShuffleNetV2 having an average recognition time per sheet 438.08 ms faster than the classical classification model VGGNet16. All 3 lightweight classification models had Accuracy > 80.0%; Kappa values > 70.0% with significant agreement; sensitivity, specificity, and F1-Score for the diagnosis of normal fundus images were ≥ 98.0%; Macro-F1 was 78.2%, 79.4%, and 81.5%, respectively. ConclusionThe intelligent assisted diagnosis of common fundus diseases based on fundus color photography is a lightweight model with high recognition accuracy and speed; the storage size and number of parameters are significantly reduced compared with the classical classification model.
Objective To identify the heart sounds of aortic stenosis by deep learning model based on DenseNet121 architecture, and to explore its application potential in clinical screening aortic stenosis. Methods We prospectively collected heart sounds and clinical data of patients with aortic stenosis in Tianjin Chest Hospital, from June 2021 to February 2022. The collected heart sound data were used to train, verify and test a deep learning model. We evaluated the performance of the model by drawing receiver operating characteristic curve and precision-recall curve. Results A total of 100 patients including 11 asymptomatic patients were included. There were 50 aortic stenosis patients with 30 males and 20 females at an average age of 68.18±10.63 years in an aortic stenosis group (stenosis group). And 50 patients without aortic valve disease were in a negative group, including 26 males and 24 females at an average age of 45.98±12.51 years. The model had an excellent ability to distinguish heart sound data collected from patients with aortic stenosis in clinical settings: accuracy at 91.67%, sensitivity at 90.00%, specificity at 92.50%, and area under receiver operating characteristic curve was 0.917. Conclusion The model of heart sound diagnosis of aortic stenosis based on deep learning has excellent application prospects in clinical screening, which can provide a new idea for the early identification of patients with aortic stenosis.
Stroke is a kind of cerebrovascular disease with high incidence and disability rate. Motor dysfunction and cognitive dysfunction are common dysfunctions of stroke. Rehabilitation treatment can effectively reduce the disability rate of stroke and improve the quality of life. The short-term hospitalization and ambulatory rehabilitation treatment cannot meet the rehabilitation needs of stroke patients. Cloud rehabilitation is one of the ways to solve this problem. This article introduces the definition and application of cloud rehabilitation and artificial intelligence (including assisted rehabilitation assessment and assisted rehabilitation treatment), and summarizes the current problems in the development of stroke cloud rehabilitation in China, so as to promote the construction of remote rehabilitation based on artificial intelligence in China and provide some references for the selection of rehabilitation programs for patients with stroke.
Sample size calculation is an important factor to evaluate the reliability of the diagnostic test. In this paper, a case study of the clinical diagnostic test of artificial intelligence for identification of liver contrast-enhanced ultrasound was performed to conduct two-category and multi-categories studies. Based on sensitivity and specificity, the sample size was then estimated in combination with the statistical characteristics of disease incidence, test level and one/two-sided test. Eventually, the sample size was corrected by integrating the factors of the proportion of training/test dataset and the dropout rate of cases in the medical image recognition system. Moreover, the application of Sample Size Calculator, MedCalc, PASS, and other software can accelerate sample size calculation and reduce the amount of labor.
ObjectiveTo evaluate the diagnostic value of artificial intelligence (AI)-assisted diagnostic system for pulmonary cancer based on CT images.MethodsDatabases including PubMed, The Cochrane Library, EMbase, CNKI, WanFang Data and Chinese BioMedical Literature Database (CBM) were electronically searched to collect relevant studies on AI-assisted diagnostic system in the diagnosis of pulmonary cancer from 2010 to 2019. The eligible studies were selected according to inclusion and exclusion criteria, and the quality of included studies was assessed and the special information was identified. Then, meta-analysis was performed using RevMan 5.3, Stata 12.0 and SAS 9.4 softwares. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio were pooled and the summary receiver operating characteristic (SROC) curve was drawn. Meta-regression analysis was used to explore the sources of heterogeneity.ResultsTotally 18 studies were included with 4 771 patients. Random effect model was used for the analysis due to the heterogeneity among studies. The results of meta-analysis showed that the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnosis odds ratio and area under the SROC curve were 0.87 [95%CI (0.84, 0.90)], 0.89 [95%CI (0.84, 0.92)], 7.70 [95%CI (5.32, 11.15)], 0.14 [95%CI (0.11, 0.19)], 53.54 [95%CI (30.68, 93.42)] and 0.94 [95%CI (0.91, 0.95)], respectively.ConclusionAI-assisted diagnostic system based on CT images has high diagnostic value for pulmonary cancer, and thus it is worthy of clinical application. However, due to the limited quality and quantity of included studies, above results should be validated by more studies.
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.
With the rapid development of artificial intelligence (AI) technology, its application in hospital management is gradually becoming an important means to improve operational efficiency and the quality of patient health care. This article systematically explores the multidimensional applications of AI in hospital management, including medical services, administration, patient engagement and experience. Through in-depth analysis, the paper evaluates the potential of AI in these areas, especially the significant effect in improving operational efficiency and optimising patient healthcare services. However, the application of AI also faces many challenges, such as data privacy issues, algorithmic bias, operational management, and economic factors. This article not only identifies these challenges, but also provides specific inspiration and recommendations for hospital management in China, emphasises the importance of adaptability and continuous learning, and calls on hospital administrators to actively embrace change in order to achieve both improved patient health outcomes and operational efficiency.
As an emerging technology, artificial intelligence (AI) uses human theory and technology for robots to study, develop, learn and identify human technologies. Thoracic surgeons should be aware of new opportunities that may affect their daily practice by the direct use of AI technology, or indirect use in the relevant medical fields (radiology, pathology, and respiratory medicine). The purpose of this paper is to review the application status and future development of AI associated with thoracic surgery, diagnosis of AI-related lung cancer, prognosis-assisted decision-making programs and robotic surgery. While AI technology has made rapid progress in many areas, the medical industry only accounts for a small part of AI use, and AI technology is gradually becoming widespread in the diagnosis, treatment, rehabilitation, and care of diseases. The future of AI is bright and full of innovative perspectives. The field of thoracic surgery has conducted valuable exploration and practice on AI, and will receive more and more influence and promotion from AI.
The robotic bronchoscopy system is a new technology for lung lesion location, biopsy and interventional therapy. Its safety and effectiveness have been clinically proven. Based on many advanced technologies carried by the robotic bronchoscopy system, it is more intelligent, convenient and stable when clinicians perform bronchoscopy operations. It has higher accuracy and diagnostic rates, and less complications than bronchoscopy with the assistance of magnetic navigation and ordinary bronchoscopy. This article gave a review of the progress of robotic bronchoscopy systems, and a prospect of the combination with artificial intelligence.
