Human motion recognition (HAR) is the technological base of intelligent medical treatment, sports training, video monitoring and many other fields, and it has been widely concerned by all walks of life. This paper summarized the progress and significance of HAR research, which includes two processes: action capture and action classification based on deep learning. Firstly, the paper introduced in detail three mainstream methods of action capture: video-based, depth camera-based and inertial sensor-based. The commonly used action data sets were also listed. Secondly, the realization of HAR based on deep learning was described in two aspects, including automatic feature extraction and multi-modal feature fusion. The realization of training monitoring and simulative training with HAR in orthopedic rehabilitation training was also introduced. Finally, it discussed precise motion capture and multi-modal feature fusion of HAR, as well as the key points and difficulties of HAR application in orthopedic rehabilitation training. This article summarized the above contents to quickly guide researchers to understand the current status of HAR research and its application in orthopedic rehabilitation training.
ObjectiveTo summarize the application status of artificial intelligence (AI) in the diagnosis and treatment of gastrointestinal tumors using image deep learning, as well as its application prospect. MethodLiteratures on AI in the field of gastrointestinal tumors in recent years were reviewed and summarized.ResultsAI had developed rapidly in the medical field. The gastrointestinal endoscopy, imaging examination, and pathological diagnosis assisted by AI technology could assist doctors to make more accurate diagnosis opinions, and make the diagnosis and treatment of gastrointestinal tumors develop towards a more accurate and efficient direction. However, the application of AI in the medical field had just begun, and it still needed to be popularized for a long time.ConclusionThe gastrointestinal endoscopy system, imaging examination system, and pathological diagnosis assisted by AI technology all show high specificity and sensitivity, which obviously reflects its high efficiency and accuracy.
Esophageal cancer is a serious threat to the health of Chinese people. The key to solve this problem is early diagnosis and early treatment, and the most important method is endoscopic screening. The rapid development of artificial intelligence (AI) technology makes its application and research in the field of digestive endoscopy growing, and it is expected to become the "right-hand man" for endoscopists in the early diagnosis of esophageal cancer. Currently, the application of multimodal and multifunctional AI systems has achieved good performance in the diagnosis of superficial esophageal squamous cell carcinoma and precancerous lesions. This study summarized and reviewed the research progress of AI in the diagnosis of superficial esophageal squamous cell carcinoma and precancerous lesions, and also explored its development direction in the future.
Lung cancer is a most common malignant tumor of the lung and is the cancer with the highest morbidity and mortality worldwide. For patients with advanced non-small cell lung cancer who have undergone epidermal growth factor receptor (EGFR) gene mutations, targeted drugs can be used for targeted therapy. There are many methods for detecting EGFR gene mutations, but each method has its own advantages and disadvantages. This study aims to predict the risk of EGFR gene mutation by exploring the association between the histological features of the whole slides pathology of non-small cell lung cancer hematoxylin-eosin (HE) staining and the patient's EGFR mutant gene. The experimental results show that the area under the curve (AUC) of the EGFR gene mutation risk prediction model proposed in this paper reached 72.4% on the test set, and the accuracy rate was 70.8%, which reveals the close relationship between histomorphological features and EGFR gene mutations in the whole slides pathological images of non-small cell lung cancer. In this paper, the molecular phenotypes were analyzed from the scale of the whole slides pathological images, and the combination of pathology and molecular omics was used to establish the EGFR gene mutation risk prediction model, revealing the correlation between the whole slides pathological images and EGFR gene mutation risk. It could provide a promising research direction for this field.
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.
In recent years, deep learning has provided a new method for cancer prognosis analysis. The literatures related to the application of deep learning in the prognosis of cancer are summarized and their advantages and disadvantages are analyzed, which can be provided for in-depth research. Based on this, this paper systematically reviewed the latest research progress of deep learning in the construction of cancer prognosis model, and made an analysis on the strengths and weaknesses of relevant methods. Firstly, the construction idea and performance evaluation index of deep learning cancer prognosis model were clarified. Secondly, the basic network structure was introduced, and the data type, data amount, and specific network structures and their merits and demerits were discussed. Then, the mainstream method of establishing deep learning cancer prognosis model was verified and the experimental results were analyzed. Finally, the challenges and future research directions in this field were summarized and expected. Compared with the previous models, the deep learning cancer prognosis model can better improve the prognosis prediction ability of cancer patients. In the future, we should continue to explore the research of deep learning in cancer recurrence rate, cancer treatment program and drug efficacy evaluation, and fully explore the application value and potential of deep learning in cancer prognosis model, so as to establish an efficient and accurate cancer prognosis model and realize the goal of precision medicine.
With the change of medical diagnosis and treatment mode, the quality of medical image directly affects the diagnosis and treatment of the disease for doctors. Therefore, realization of intelligent image quality control by computer will have a greater auxiliary effect on the radiographer’s filming work. In this paper, the research methods and applications of image segmentation model and image classification model in the field of deep learning and traditional image processing algorithm applied to medical image quality evaluation are described. The results demonstrate that deep learning algorithm is more accurate and efficient than the traditional image processing algorithm in the effective training of medical image big data, which explains the broad application prospect of deep learning in the medical field. This paper developed a set of intelligent quality control system for auxiliary filming, and successfully applied it to the Radiology Department of West China Hospital and other city and county hospitals, which effectively verified the feasibility and stability of the quality control system.
Objective To systematically evaluate the diagnostic value of artificial intelligence assisted narrow-band imaging endoscopy diagnostic system for colorectal adenomatous polyps. Methods Pubmed, Embase, Web of Science, Cochrane Library, SinoMed, China National Knowledge Infrastructure, Chongqing VIP and Wanfang databases were searched. The diagnostic trials of the artificial intelligence assisted narrow-band imaging endoscopy diagnostic system for colorectal adenomatous polyps were comprehensively searched. The search time limit was from January 1, 2000 to October 31, 2022. The included studies were evaluated according to the Quality Assessment of Diagnostic Accuracy Studies-2, and the data were meta-analysed with RevMan 5.3, Meta-Disc 1.4 and Stata 13.0 statistical softwares. Results Finally, 11 articles were included, including 2178 patients. Meta-analysis results of the artificial intelligence assisted narrow-band imaging endoscopy diagnostic system for colorectal adenomatous polyps showed that the pooled sensitivity was 0.91, the pooled specificity was 0.88, the pooled positive likelihood ratio was 7.41, the pooled negative likelihood ratio was 0.10, the pooled diagnostic odds ratio was 76.45, and the area under the summary receiver operating characteristic curve was 0.957. Among them, 5 articles reported the diagnosis of small adenomatous polyps (diameter <5 mm) by the artificial intelligence assisted narrow-band imaging endoscopy diagnostic system. The results showed that the pooled sensitivity and the pooled specificity were 0.93 and 0.91, respectively, and the area under the summary receiver operating characteristic curve was 0.971. Five articles reported the accuracy of endoscopic diagnosis for adenomatous polyps of those with insufficient experience. The results showed that the pooled sensitivity and the pooled specificity were 0.84 and 0.76, respectively. The area under the summary receiver operating characteristic curve was 0.848. Compared with the artificial intelligence assisted narrow-band imaging endoscopy diagnostic system, the difference was statistically significant (Z=1.979, P=0.048). Conclusion The artificial intelligence assisted narrow-band imaging endoscopy diagnostic system has a high diagnostic accuracy, which can significantly improve the diagnostic accuracy for colorectal adenomatous polyps of those with insufficient endoscopic experience, and can effectively compensate for the adverse impact of their lack of endoscopic experience.
Compared with the previous automatic segmentation neural network for the target area which considered the target area as an independent area, a stacked neural network which uses the position and shape information of the organs around the target area to regulate the shape and position of the target area through the superposition of multiple networks and fusion of spatial position information to improve the segmentation accuracy on medical images was proposed in this paper. Taking the Graves’ ophthalmopathy disease as an example, the left and right radiotherapy target areas were segmented by the stacked neural network based on the fully convolutional neural network. The volume Dice similarity coefficient (DSC) and bidirectional Hausdorff distance (HD) were calculated based on the target area manually drawn by the doctor. Compared with the full convolutional neural network, the stacked neural network segmentation results can increase the volume DSC on the left and right sides by 1.7% and 3.4% respectively, while the two-way HD on the left and right sides decrease by 0.6. The results show that the stacked neural network improves the degree of coincidence between the automatic segmentation result and the doctor's delineation of the target area, while reducing the segmentation error of small areas. The stacked neural network can effectively improve the accuracy of the automatic delineation of the radiotherapy target area of Graves' ophthalmopathy.
Artificial intelligence belongs to the field of computer science. In the past few decades, artificial intelligence has shown broad application prospects in the medical field. With the development of computer technology in recent years, doctors and computer scientists have just begun to discover its potential for clinical application, especially in the field of congenital heart disease. Artificial intelligence now has been successfully applied to the prediction, intelligent diagnosis, medical image segmentation and recognition, clinical decision support of congenital heart disease. This article reviews the application of artificial intelligence in congenital cardiology.