Electrocardiogram (ECG) is a noninvasive, inexpensive, and convenient test for diagnosing cardiovascular diseases and assessing the risk of cardiovascular events. Although there are clear standardized operations and procedures for ECG examination, the interpretation of ECG by even trained physicians can be biased due to differences in diagnostic experience. In recent years, artificial intelligence has become a powerful tool to automatically analyze medical data by building deep neural network models, and has been widely used in the field of medical image diagnosis such as CT, MRI, ultrasound and ECG. This article mainly introduces the application progress of deep neural network models in ECG diagnosis and prediction of cardiovascular diseases, and discusses its limitations and application prospects.
Objective To explore the application effect of 3D printed heart models in the training of young cardiac surgeons, and evaluate their application value in surgical simulation and skill improvement. MethodsEight young cardiac surgeons were selected form West China Hospital as the trainees. Before training, the Hands-On Surgical Training-Congenital Heart Surgery (HOST-CHS) operation scores of the 8 cardiac surgeons were obtained after operating on 2 pig heart models of ventricular septal defect (VSD). Subsequently, simulation training was conducted on a 3D printed peri-membrane VSD heart model for 6 weeks, once a week. After the training, all trainees completed 2 pig heart VSD repair surgeries. The improvement of doctors’ skills was evaluated through survey questionnaires, HOST-CHS scores, and operation time after training. ResultsBefore the training, the average HOST-CHS score of the 8 trainees was 52.2±6.3 points, and the average time for VSD repair was 54.7±7.1 min. During the 6-week simulation training using 3D printed models, the total score of HOST-CHS for the 8 trainees gradually increased (P<0.001), and the time required to complete VSD repair was shortened (P<0.001). The trainees had the most significant improvement in scores of surgical cognition and protective awareness. The survey results showed that trainees were generally very satisfied with the effectiveness of 3D model simulation training. Conclusion The 3D printed VSD model demonstrates significant application advantages in the training of young cardiac surgeons. By providing highly realistic anatomical structures, 3D models can effectively enhance surgeons’ surgical skills. It is suggested to further promote the application of 3D printing technology in medical education, providing strong support for cultivating high-quality cardiac surgeons.
ObjectiveTo systematically review mortality risk prediction models for acute type A aortic dissection (AAAD). MethodsPubMed, EMbase, Web of Science, CNKI, WanFang Data, VIP and CBM databases were electronically searched to collect studies of mortality risk prediction models for AAAD from inception to July 31th, 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Systematic review was then performed. ResultsA total of 19 studies were included, of which 15 developed prediction models. The performance of prediction models varied substantially (AUC were 0.56 to 0.92). Only 6 studies reported calibration statistics, and all models had high risk of bias. ConclusionsCurrent prediction models for mortality and prognosis of AAAD patients are suboptimal, and the performance of the models varies significantly. It is still essential to establish novel prediction models based on more comprehensive and accurate statistical methods, and to conduct internal and a large number of external validations.
Objective To detect expression of NF-κB in the inner retina and in vestigate the inhibitoryeffect of pyrrolidine dithiocarbamate on retinal neovascularization in rats. Methods The rat models with retinopathy were set up un der the hypoxia condition, and fluorescein fundus angiography (FFA) was used to observe the retinal neovascularization. The expressions of NF-κB in the inner retina in rats with and without neovascularization were detected by immunohisto chemical method. PDTC was intraperitoneally injected in rats with neovascularization to observe the expression of NF-κB in the inner retina and the effect on retinal neovascularization. Results Hypoxia induced NF-κB activation in the retinal glial cells and endothelial cells. But immuno-staining intensity for NF-κB and adhesion molecules were reduced by PDTC intraperitoneal injection. Retin al angiogenesis in rats were suppressed effectively (P＜0.05). Conclusions NF-κB activation correlates with retinal neovascularization closely. PDTC may inhibit the NF-κB activation and prove beneficial in the treatment of ischemic neovascularization. (Chin J Ocul Fundus Dis，2003，19：201-268)
Complete three-dimensional (3D) tooth model provides essential information to assist orthodontists for diagnosis and treatment planning. Currently, 3D tooth model is mainly obtained by segmentation and reconstruction from dental computed tomography (CT) images. However, the accuracy of 3D tooth model reconstructed from dental CT images is low and not applicable for invisalign design. And another serious problem also occurs,i.e. frequentative dental CT scan during different intervals of orthodontic treatment often leads to radiation to the patients. Hence, this paper proposed a method to reconstruct tooth model based on fusion of dental CT images and laser-scanned images. A complete 3D tooth model was reconstructed with the registration and fusion between the root reconstructed from dental CT images and the crown reconstructed from laser-scanned images. The crown of the complete 3D tooth model reconstructed with the proposed method has higher accuracy. Moreover, in order to reconstruct complete 3D tooth model of each orthodontic treatment interval, only one pre-treatment CT scan is needed and in the orthodontic treatment process only the laser-scan is required. Therefore, radiation to the patients can be reduced significantly.
Objective To explore independent risk factors for 30-day mortality in critical patients with pulmonary infection and sepsis, and build a prediction model. Methods Patients diagnosed with pulmonary infection and sepsis in the MIMIC-Ⅲ database were analyzed. The CareVue database was the training cohort (n=934), and the Metavision database was the external validation cohort (n=687). A COX proportional hazards regression model was established to screen independent risk factors and draw a nomogram. We conducted internal cross-validation and external validation of the model. Using the receiver operator characteristic (ROC) curve, Calibration chart, and decision curve analysis, we detected the discrimination, calibration, and benefit of the model respectively, comparing with the SOFA scoring model. Results Age, SOFA score, white blood cell count≤4×109/L, neutrophilic granulocyte percentage (NEU%)>85%, platelet count (PLT)≤100×109/L, PLT>300×109/L, red cell distribution width >15%, blood urea nitrogen, and lactate dehydrogenase were independent risk factors. The areas under the ROC curve of the model were 0.747 (training cohort) and 0.708 (external validation cohort), respectively, which was superior to the SOFA scoring model in terms of discrimination, calibration, and benefit. Conclusion The model established in this study can accurately and effectively predict the risk of the disease mortality, and provide a visual assessment method for early identification of high-risk patients.
Objective To scoping review the risk prediction models for sarcopenia in China was conducted, and provide reference for scientific prevention and treatment of the disease and related research. Methods We systematically searched PubMed, Web of Science, Cochrane Library, Embase, China Knowledge Network, China Biomedical Literature Database, Wanfang Database, and Weipu Database for literature related to myasthenia gravis prediction models in China, with a time frame from the construction of the database to April 30, 2024 for the search. The risk of bias and applicability of the included literature were assessed, and information on the construction of myasthenia gravis risk prediction models, model predictors, model presentation form and performance were extracted. Results A total of 25 literatures were included, the prevalence of sarcopenia ranged from 12.16% to 54.17%, and the study population mainly included the elderly, the model construction methods were categorized into two types: logistic regression model and machine learning, and age, body mass index, and nutritional status were the three predictors that appeared most frequently. Conclusion Clinical caregivers should pay attention to the high-risk factors for the occurrence of sarcopenia, construct models with accurate predictive performance and high clinical utility with the help of visual model presentation, and design prospective, multicenter internal and external validation methods to continuously improve and optimize the models to achieve the best predictive effect.