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 review the progress of artificial intelligence (AI) and radiomics in the study of abdominal aortic aneurysm (AAA). Method The literatures related to AI, radiomics and AAA research in recent years were collected and summarized in detail. Results AI and radiomics influenced AAA research and clinical decisions in terms of feature extraction, risk prediction, patient management, simulation of stent-graft deployment, and data mining. Conclusion The application of AI and radiomics provides new ideas for AAA research and clinical decisions, and is expected to suggest personalized treatment and follow-up protocols to guide clinical practice, aiming to achieve precision medicine of AAA.
As a kind of disease with high incidence rate, high mortality, high recurrence rate and high disability rate, stroke has become one of the most serious disease burdens in China. Rapid diagnosis and treatment of stroke can effectively improve the outcome of patients and reduce the psychological and economic burden of patients’ families and society. In recent years, with the rapid development of artificial intelligence technology,this technology can effectively improve daily diagnosis and treatment efficiency. This paper focuses on the application of artificial intelligence technology to the diagnosis, treatment and outcome prediction of stroke, aiming to provide ideas for further guiding precision medicine.
Continuous renal replacement therapy (CRRT) is one of the major treatments for critically ill patients. With the development of information technology, the informatization and artificial intelligent of CRRT has received wide attention, which has promoted the optimization of CRRT in terms of workflow, teaching method as well as scientific research. Benefiting from the big data generated, artificial intelligence is expected to be applied in the precision treatment, quality control, timing of intervention, as well as prognosis assessment in severe AKI, so as to ultimately improve the therapeutic effect of CRRT among critically ill patients. This paper summarizes the information construction of CRRT and the research progress of artificial intelligence, which can be used as a reference for practitioners in kidney disease, critical medicine, emergency medicine and other related fields.
Cardiovascular disease (CVD) has caused a huge burden of disease worldwide, and accurate diagnosis and assessment of CVD has a clear significance for improving the prognosis of patients. The development of artificial intelligence (AI) and its rapid application in the medical field have enabled new approaches for the analysis and fitting of various CVD data. At present, in addition to structured medical records, the CVD field also includes a large number of non-linear data brought by imaging and electrophysiological examinations. How to use AI to process such multi-source data has been explored by a large number of studies. Therefore, this review discusses the existing ways of processing various multi-source heterogeneous data with existing artificial intelligence technologies by summarizing various existing studies, and analyzes their possible advantages and disadvantages, in order to provide a basis for the future application of AI in CVD.
With the widespread adoption of lung cancer screening, an increasing number of patients are being diagnosed with early-stage lung adenocarcinoma. For stage ⅠA lung adenocarcinoma, sublobar resection is the primary treatment approach. However, in patients with concomitant spread through air space (STAS), numerous studies advocate for lobectomy as the mainstay of treatment. Due to the limitations in preoperative prediction and intraoperative frozen section evaluation for assessing STAS, current research is largely restricted to using clinical and imaging features to predict STAS occurrence, with results that are inconsistent and unsatisfactory. Furthermore, most studies focus on individual clinical or imaging characteristics, and there is a lack of large-sample investigations. The rise of artificial intelligence in recent years has provided new insights into solving this problem, and existing studies have shown that artificial intelligence demonstrates better performance in STAS prediction compared to conventional methods. This article reviews the value of artificial intelligence in predicting STAS.
ObjectiveTo analyze the predictive value of ensemble classification algorithm of random forest for delirium risk in ICU patients with cardiothoracic surgery. MethodsA total of 360 patients hospitalized in cardiothoracic ICU of our hospital from June 2019 to December 2020 were retrospectively analyzed. There were 193 males and 167 females, aged 18-80 (56.45±9.33) years. The patients were divided into a delirium group and a control group according to whether delirium occurred during hospitalization or not. The clinical data of the two groups were compared, and the related factors affecting the occurrence of delirium in cardiothoracic ICU patients were predicted by the multivariate logistic regression analysis and the ensemble classification algorithm of random forest respectively, and the difference of the prediction efficiency between the two groups was compared.ResultsOf the included patients, 19 patients fell out, 165 patients developed ICU delirium and were enrolled into the delirium group, with an incidence of 48.39% in ICU, and the remaining 176 patients without ICU delirium were enrolled into the control group. There was no statistical significance in gender, educational level, or other general data between the two groups (P>0.05). But compared with the control group, the patients of the delirium group were older, length of hospital stay was longer, and acute physiology and chronic health evaluationⅡ(APACHEⅡ) score, proportion of mechanical assisted ventilation, physical constraints, sedative drug use in the delirium group were higher (P<0.05). Multivariate logistic regression analysis showed that age (OR=1.162), length of hospital stay (OR=1.238), APACHEⅡ score (OR=1.057), mechanical ventilation (OR=1.329), physical constraints (OR=1.345) and sedative drug use (OR=1.630) were independent risk factors for delirium of cardiothoracic ICU patients. The variables in the random forest model for sorting, on top of important predictor variable were: age, length of hospital stay, APACHEⅡ score, mechanical ventilation, physical constraints and sedative drug use. The diagnostic efficiency of ensemble classification algorithm of random forest was obviously higher than that of multivariate logistic regression analysis. The area under receiver operating characteristic curve of ensemble classification algorithm of random forest was 0.87, and the one of multivariate logistic regression analysis model was 0.79.ConclusionThe ensemble classification algorithm of random forest is more effective in predicting the occurrence of delirium in cardiothoracic ICU patients, which can be popularized and applied in clinical practice and contribute to early identification and strengthening nursing of high-risk patients.
Objective To investigate an artificial intelligence (AI) automatic segmentation and modeling method for knee joints, aiming to improve the efficiency of knee joint modeling. Methods Knee CT images of 3 volunteers were randomly selected. AI automatic segmentation and manual segmentation of images and modeling were performed in Mimics software. The AI-automated modeling time was recorded. The anatomical landmarks of the distal femur and proximal tibia were selected with reference to previous literature, and the indexes related to the surgical design were calculated. Pearson correlation coefficient (r) was used to judge the correlation of the modeling results of the two methods; the consistency of the modeling results of the two methods were analyzed by DICE coefficient. Results The three-dimensional model of the knee joint was successfully constructed by both automatic modeling and manual modeling. The time required for AI to reconstruct each knee model was 10.45, 9.50, and 10.20 minutes, respectively, which was shorter than the manual modeling [(64.73±17.07) minutes] in the previous literature. Pearson correlation analysis showed that there was a strong correlation between the models generated by manual and automatic segmentation (r=0.999, P<0.001). The DICE coefficients of the 3 knee models were 0.990, 0.996, and 0.944 for the femur and 0.943, 0.978, and 0.981 for the tibia, respectively, verifying a high degree of consistency between automatic modeling and manual modeling. Conclusion The AI segmentation method in Mimics software can be used to quickly reconstruct a valid knee model.
With the rapid development of artificial intelligence technology, researchers have applied it to the diagnosis of various tumors in the urinary system in recent years, and have obtained many valuable research results. The article sorted the research status of artificial intelligence technology in the fields of renal tumors, bladder tumors and prostate tumors from three aspects: the number of papers, image data, and clinical tasks. The purpose is to summarize and analyze the research status and find new valuable research ideas in the future. The results show that the artificial intelligence model based on medical data such as digital imaging and pathological images is effective in completing basic diagnosis of urinary system tumors, image segmentation of tumor infiltration areas or specific organs, gene mutation prediction and prognostic effect prediction, but most of the models for the requirement of clinical application still need to be improved. On the one hand, it is necessary to further improve the detection, classification, segmentation and other performance of the core algorithm. On the other hand, it is necessary to integrate more standardized medical databases to effectively improve the diagnostic accuracy of artificial intelligence models and make it play greater clinical value.
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".