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.
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.
Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. ResultsTaking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.
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.
Driven by advances in intelligent technology, artificial intelligence (AI) is emerging as the cornerstone of neurosurgical education. By providing personalized learning experiences and enhancing learning outcomes, AI has enriched the avenues and depth of knowledge acquisition for medical students. The integration of AI not only helps medical students master the basic theories and practical skills of neurosurgery more thoroughly, but also lays a solid foundation for them to provide high-quality and efficient medical services in the future. At the same time, the ability of educators to use intelligent technologies further enhances the interactivity and effectiveness of teaching. In order to further ensure the application of AI in neurosurgery teaching, this article explores the strategic integration of AI in neurosurgical education, emphasizing its critical importance in ensuring that teaching methods evolve with the times.
In recent years, the computer science represented by artificial intelligence and high-throughput sequencing technology represented by omics play a significant role in the medical field. This paper reviews the research progress of the application of artificial intelligence combined with omics data analysis in the diagnosis and treatment of non-small cell lung cancer (NSCLC), aiming to provide ideas for the development of a more effective artificial intelligence algorithm, and improve the diagnosis rate and prognosis of patients with early NSCLC through a non-invasive way.
Objective To explore the application value of artificial intelligence (AI) pulmonary artery assisted diagnosis software for suspected pulmonary embolism patients. Methods The data of 199 patients who were clinically suspected of pulmonary embolism and underwent pulmonary artery CT angiography (CTA) from June 2016 to December 2021 were retrospectively analyzed. Images of pulmonary artery CTA diagnosed by radiologists with different experiences and judged by senior radiologists were compared with the analysis results of AI assisted diagnostic software for pulmonary artery CTA, to evaluate the diagnostic efficacy of this software and low, medium, and senior radiologists for pulmonary embolism. The agreement of pulmonary embolism based on pulmonary artery CTA between the AI software and radiologists with different experiences was evaluated using Kappa test. Results The agreement of the AI software and the evaluation of pulmonary embolism lesions by senior radiologists based on pulmonary artery CTA was high (Kappa=0.913, P<0.001), while the diagnostic results of pulmonary artery CTA AI software was good after judged by senior radiologists based on pulmonary artery CTA (Kappa=0.755, P<0.001). Conclusions The AI software based on pulmonary artery CTA diagnosis of pulmonary embolism has good consistency with diagnostic images of radilogists, and can save a lot of reconstruction and diagnostic time. It has the value of daily diagnosis work and worthy of clinical promotion.
Objective To evaluate medical students’ perceptions and attitudes toward artificial intelligence (AI)-assisted diagnosis of renal cell carcinoma (RCC), and to analyze their educational needs regarding AI in pathological diagnosis. Methods A questionnaire survey (including closed and open-ended questions) was conducted to assess medical students’ perceptions, attitudes, and educational needs concerning AI-assisted RCC diagnosis. Participants included medical students from different specialties and standardized training residents. The questionnaire covered demographic information, perceptions and attitudes toward AI, and AI-related educational needs. Results A total of 249 respondents completed the survey. The majority were standardized training residents, mostly aged 23-26 years, and 40.96% had practical experience in pathological diagnosis of RCC. The median scores for most closed-ended questions were 4. Respondents generally considered “efficiency” and “improved accuracy” as the most prominent advantages of AI, with timeliness, automated diagnosis, reduction of human error, and precise diagnosis being the most emphasized aspects. Analysis of AI-related educational needs revealed high-frequency keywords such as “expanding sample size” “balanced responsibility allocation” and “enhancing collaboration skills.” Conclusion Medical students hold a positive attitude toward AI and its application in RCC diagnosis, but there remains a lack of formal AI-related education.
Optical coherence tomography (OCT), as a high-resolution, non-invasive, in-vivo image method has been widely used in retinal field, especially in the examination of fundus diseases. Nowadays, the modality has been gradually popularized in most of the national basic-level hospitals. However, OCT is only employed as a diagnostic tool in most cases, ophthalmologists lack of awareness of further exploring the information behind the raw data. In the era of fast-developing artificial intelligence, on the basis of standardized information management, a more comprehensive OCT database should be established. Further original image processing, lesion analysis, and artificial intelligence development of OCT images will help improve the understanding level of vitreoretinal diseases among clinicians and assist ophthalmologists to make more appropriate clinical decisions.