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.
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 application of inpatient electronic medical records (EMRs) is a crucial component of modern healthcare informatization, and also a key factor in improving medical quality and safety. Establishing standardized EMRs for thoracic surgery helps to standardize treatment processes, improve medical efficiency, enhance quality of care, and better ensure patient safety. It also facilitates the collection and use of standardized and structured data, promoting clinical decision-making, the application of artificial intelligence, and the development of specialized clinical centers. Considering relevant national policies, information standards, clinical practice challenges and latest research findings in thoracic surgery EMRs, Chinese Association of Thoracic Surgeons, Cross-Strait Medicine Exchange Association’s Thoracic Surgery Professional Committee, WU Jieping Medical Foundation’s Lung Cancer Professional Committee, Zhejiang Provincial Thoracic Surgeons Associations and Fujian Provincial Thoracic Surgeons Associations have explored innovative paths for EMRs development. Through multiple rounds of professional discussions and research, the "Chinese expert consensus on quality control and management of electronic medical records for thoracic surgery (2024 version)" was formulated. It aims to provide a reference for the construction and application of inpatient EMRs for thoracic surgeons and information professionals across China, promoting continuous improvement in the informatization and medical standards of the thoracic surgery field, and contributing to the construction of "healthy China".
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.
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.
ObjectiveTo review and evaluate the research progress of the robot-assisted joint arthroplasty.MethodsThe domestic and foreign related research literature on robot-assisted joint arthroplasty was extensively consulted. The advantages, disadvantages, effectiveness, and future prospects were mainly reviewed and summarized.ResultsThe widely recognized advantages of robot-assisted joint arthroplasty are digital and intelligent preoperative planning, accurate intraoperative prosthesis implantation, and quantitative soft tissue balance, as well as good postoperative imaging prosthesis position and alignment. However, the advantages of effectiveness are still controversial. The main disadvantages of robot-assisted joint arthroplasty are the high price of the robot system, the prolonged operation time, and the increased radioactive damage of the imaging-dependent system.ConclusionCompared to traditional arthroplasty, robot-assisted joint arthroplasty can improve the accuracy of the prosthesis position and assist in the quantitative assessment of soft tissue tension, and the repeatability rate is high. In the future, further research is needed to evaluate the clinical function and survival rate of the prosthesis, as well as to optimize the robot system.
ObjectiveTo evaluate the use of machine learning algorithms for the prediction and characterization of cardiac thrombosis in patients with valvular heart disease and atrial fibrillation. MethodsThis article collected data of patients with valvular disease and atrial fibrillation from West China Hospital of Sichuan University and its branches from 2016 to 2021. From a total of 2 515 patients who underwent valve surgery, 886 patients with valvular disease and atrial fibrillation were included in the study, including 545 (61.5%) males and 341 (38.5%) females, with a mean age of 55.62±9.26 years, and 192 patients had intraoperatively confirmed cardiac thrombosis. We used five supervised machine learning algorithms to predict thrombosis in patients. Based on the clinical data of the patients (33 features after feature screening), the 10-fold nested cross-validation method was used to evaluate the predictive effect of the model through evaluation indicators such as area under the curve, F1 score and Matthews correlation coefficient. Finally, the SHAP interpretation method was used to interpret the model, and the characteristics of the model were analyzed using a patient as an example. ResultsThe final experiment showed that the random forest classifier had the best comprehensive evaluation indicators, the area under the receiver operating characteristic curve was 0.748±0.043, and the accuracy rate reached 79.2%. Interpretation and analysis of the model showed that factors such as stroke volume, peak mitral E-wave velocity and tricuspid pressure gradient were important factors influencing the prediction. ConclusionThe random forest model achieves the best predictive performance and is expected to be used by clinicians as an aided decision-making tool for screening high-embolic risk patients with valvular atrial fibrillation.