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.
Heart transplantation remains the most effective treatment for patients with end-stage heart failure. Over the past decade, significant advancements have been made in the field of heart transplant surgery. However, the enormous demand from heart failure patients and the severe shortage of available donor hearts continue to be major obstacles to the widespread application of heart transplantation. With the development of donor heart recovery, preservation, and evaluation techniques, the use of extended criteria donors and donation after circulatory death has increased. These technological advancements have expanded the safe ischemic time and geographic range for donor heart procurement, significantly enlarging the donor pool and driving a rapid increase in heart transplant cases. Concurrently, many new techniques have emerged in heart transplant surgery and perioperative management, particularly the rapid advancements in mechanical circulatory support and artificial intelligence, which hold the potential to revolutionize the field. This article reviews and discusses the current status and major surgical advancements in adult heart transplantation in the United States, aiming to provide insights and stimulate ongoing exploration and innovation in 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.
Objective To explore the efficiency of artificial intelligence algorithm model using preoperative blood indexes on the prediction of deep vein thrombosis (DVT) in patients with lower limb fracture before operation. Methods Patients with lower limb fracture treated in the Department of Orthopedics of Deyang People’s Hospital between January 2018 and December 2022 were retrospectively selected. Their basic and clinical data such as age, gender, height and weight, and laboratory examination indicators at admission were collected, then the neutrophi to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), and platelet to lymphocyte ratio (PLR) were calculated. According to color Doppler ultrasound indication of DVT in lower extremities at admission, the patients were divided into DVT group and non-DVT group. After data preprocessing, grey relational analysis (GRA) was used to screen the combination model of important predictive features of DVT, and BP neural network prediction model was established using the selected features. Finally, the accuracy of BP neural network prediction model was evaluated, and was compared with those of different models in clinical prediction of DVT. Results A total of 4033 patients with lower limb fracture were enrolled, including 3127 cases in the DVT group and 906 cases in the non-DVT group. GRA selected seven important predictive features: absolute lymphocyte value, NLR, MLR, PLR, plasma D-dimer, direct bilirubin, and total bilirubin. The accuracies of logistic regression analysis, random forest, decision tree, BP neural network and GRA-BP neural network combination model were 74%, 76%, 75%, 84% and 87%, respectively. The GRA-BP neural network combination model had the highest accuracy. Conclusion The GRA-BP neural network selected in this paper has the highest accuracy in preoperative DVT risk prediction in patients with lower limb fracture, which can provide a reference for the formulation of DVT prevention strategies.
The increasing number of pulmonary nodules being detected by computed tomography scans significantly increase the workload of the radiologists for scan interpretation. Limitations of traditional methods for differential diagnosis of pulmonary nodules have been increasingly prominent. Artificial intelligence (AI) has the potential to increase the efficiency of discrimination and invasiveness classification for pulmonary nodules and lead to effective nodule management. Chinese Experts Consensus on Artificial Intelligence Assisted Management for Pulmonary Nodule (2022 Version) has been officially released recently. This article closely follows the context, significance, core implications, and the impact of future AI-assisted management on the diagnosis and treatment of pulmonary nodules. It is hoped that through our joint efforts, we can promote the standardization of management for pulmonary nodules and strive to improve the long-term survival and postoperative life quality of patients with lung cancer.
ObjectiveTo study the efficiency and difference of the artificial intelligence (AI) system based on fundus-reading in community and hospital scenarios in screening/diagnosing diabetic retinopathy (DR) among aged population, and further evaluate its application value. MethodsA combination of retrospective and prospective study. The clinical data of 1 608 elderly patients with diabetes were continuously treated in Henan Eye Hospital & Henan Eye Institute from July 2018 to March 2021, were collected. Among them, there were 659 males and 949 females; median age was 64 years old. From December 2018 to April 2019, 496 elderly diabetes patients were prospectively recruited in the community. Among them, there were 202 males and 294 female; median age was 62 years old. An ophthalmologist or a trained endocrinologist performed a non-mydriatic fundus color photographic examination in both eyes, and a 45° frontal radiograph was taken with the central fovea as the central posterior pole. The AI system was developed based on the deep learning YOLO source code, AI system based on the deep learning algorithm was applied in final diagnosis reporting by the "AI+manual-check" method. The diagnosis of DR were classified into 0-4 stage. The 2-4 stage patients were classified into referral DR group. ResultsA total of 1 989 cases (94.5%, 1 989/2 104) were read by AI, of which 437 (88.1%, 437/496) and 1 552 (96.5%, 1 552/1 608) from the community and hospital, respectively. The reading rate of AI films from community sources was lower than that from hospital sources, and the difference was statistically significant (χ2=51.612, P<0.001). The main reasons for poor image quality in the community were small pupil (47.1%, 24/51), cataract (19.6%, 10/51), and cataract combined with small pupil (21.6%, 11/51). The total negative rate of DR was 62.4% (1 241/1 989); among them, the community and hospital sources were 84.2% and 56.3%, respectively, and the AI diagnosis negative rate of community source was higher than that of hospital, and the difference was statistically significant (χ2=113.108, P<0.001). AI diagnosis required referral to DR 20.2% (401/1 989). Among them, community and hospital sources were 6.4% and 24.0%, respectively. The rate of referral for DR for AI diagnosis from community sources was lower than that of hospitals, and the difference was statistically significant (χ2=65.655, P<0.001). There was a statistically significant difference in the composition ratio of patients with different stages of DR diagnosed by AI from different sources (χ2=13.435, P=0.001). Among them, community-derived patients were mainly DR without referral (52.2%, 36/69); hospital-derived patients were mainly DR requiring referral (54.9%, 373/679), and the detection rate of treated DR was higher (14.3%). The first rank of the order of the fundus lesions number automatically identified by AI was drusen (68.4%) and intraretinal hemorrhage (48.5%) in the communities and hospitals respectively. Conclusions It is more suitable for early and negative DR screening for its high non-referral DR detection rate in the community. Whilst referral DR were mainly found in hospital scenario.
The monitoring of pregnant women is very important. It plays an important role in reducing fetal mortality, ensuring the safety of perinatal mother and fetus, preventing premature delivery and pregnancy accidents. At present, regular examination is the mainstream method for pregnant women's monitoring, but the means of examination out of hospital is scarce, and the equipment of hospital monitoring is expensive and the operation is complex. Using intelligent information technology (such as machine learning algorithm) can analyze the physiological signals of pregnant women, so as to realize the early detection and accident warning for mother and fetus, and achieve the purpose of high-quality monitoring out of hospital. However, at present, there are not enough public research reports related to the intelligent processing methods of out-of-hospital monitoring for pregnant women, so this paper takes the out-of-hospital monitoring for pregnant women as the research background, summarizes the public research reports of intelligent processing methods, analyzes the advantages and disadvantages of the existing research methods, points out the possible problems, and expounds the future development trend, which could provide reference for future related researches.
ObjectiveTo summarize the current research progress in the prediction of the efficacy of neoadjuvant therapy of breast cancer based on the application of artificial intelligence (AI) and radiomics. MethodThe researches on the application of AI and radiomics in neoadjuvant therapy of breast cancer in recent 5 years at home and abroad were searched in CNKI, Google Scholar, Wanfang database and PubMed database, and the related research progress was reviewed. ResultsAI had developed rapidly in the field of medical imaging, and molybdenum target, ultrasound and magnetic resonance imaging combined with AI had been deepened and expanded in different degrees in the application research of breast cancer diagnosis and treatment. In the research of molybdenum target combined with AI, the high sensitivity of molybdenum target to microcalcification was mostly used to improve the accuracy of early detection and diagnosis of breast cancer, so as to achieve the clinical purpose of early detection and diagnosis. However, in terms of prediction of neoadjuvant efficacy research of breast cancer, ultrasound and magnetic resonance imaging combined with AI were more prevalent, and their popularity remained unabated. ConclusionIn the monitoring of neoadjuvant therapy for breast cancer, the use of properly designed AI and radiomics models can give full play to its role in the predicting the curative effect of neoadjuvant therapy, and help to guide doctors in clinical diagnosis and treatment and evaluate the prognosis of breast cancer patients.
Objective To analyze the hot spot and future application trend of artificial intelligence technology in the field of intensive care medicine. Methods The CNKI, WanFang Data, VIP and Web of Science core collection databases were electronically searched to collect the related literature about the application of artificial intelligence in the field of critical medicine from January 1, 2013 to December 31, 2022. Bibliometrics was used to visually analyze the author, country, research institution, co-cited literature and key words. Results A total of 986 Chinese articles and 4 016 English articles were included. The number of articles published had increased year by year in the past decade, and the top three countries in English literature were China, the United States and Germany. The predictive model and machine learning were the most frequent key words in Chinese and English literature, respectively. Predicting disease progression, mortality and prognosis were the research focus of artificial intelligence in the field of critical medicine. ConclusionThe application of artificial intelligence in the field of critical medicine is on the rise, and the research hotspots are mainly related to monitoring, predicting disease progression, mortality, disease prognosis and the classification of disease phenotypes or subtypes.