ObjectiveTo compare the consistency of artificial analysis and artificial intelligence analysis in the identification of fundus lesions in diabetic patients.MethodsA retrospective study. From May 2018 to May 2019, 1053 consecutive diabetic patients (2106 eyes) of the endocrinology department of the First Affiliated Hospital of Zhengzhou University were included in the study. Among them, 888 patients were males and 165 were females. They were 20-70 years old, with an average age of 53 years old. All patients were performed fundus imaging on diabetic Inspection by useing Japanese Kowa non-mydriatic fundus cameras. The artificial intelligence analysis of Shanggong's ophthalmology cloud network screening platform automatically detected diabetic retinopathy (DR) such as exudation, bleeding, and microaneurysms, and automatically classifies the image detection results according to the DR international staging standard. Manual analysis was performed by two attending physicians and reviewed by the chief physician to ensure the accuracy of manual analysis. When differences appeared between the analysis results of the two analysis methods, the manual analysis results shall be used as the standard. Consistency rate were calculated and compared. Consistency rate = (number of eyes with the same diagnosis result/total number of effective eyes collected) × 100%. Kappa consistency test was performed on the results of manual analysis and artificial intelligence analysis, 0.0≤κ＜0.2 was a very poor degree of consistency, 0.2≤κ＜0.4 meant poor consistency, 0.4≤κ＜0.6 meant medium consistency, and 0.6≤κ＜1.0 meant good consistency.ResultsAmong the 2106 eyes, 64 eyes were excluded that cannot be identified by artificial intelligence due to serious illness, 2042 eyes were finally included in the analysis. The results of artificial analysis and artificial intelligence analysis were completely consistent with 1835 eyes, accounting for 89.86%. There were differences in analysis of 207 eyes, accounting for 10.14%. The main differences between the two are as follows: (1) Artificial intelligence analysis points Bleeding, oozing, and manual analysis of 96 eyes (96/2042, 4.70%); (2) Artificial intelligence analysis of drusen, and manual analysis of 71 eyes (71/2042, 3.48%); (3) Artificial intelligence analyzes normal or vitreous degeneration, while manual analysis of punctate exudation or hemorrhage or microaneurysms in 40 eyes (40/2042, 1.95%). The diagnostic rates for non-DR were 23.2% and 20.2%, respectively. The diagnostic rates for non-DR were 76.8% and 79.8%, respectively. The accuracy of artificial intelligence interpretation is 87.8%. The results of the Kappa consistency test showed that the diagnostic results of manual analysis and artificial intelligence analysis were moderately consistent (κ=0.576, P＜0.01).ConclusionsManual analysis and artificial intelligence analysis showed moderate consistency in the diagnosis of fundus lesions in diabetic patients. The accuracy of artificial intelligence interpretation is 87.8%.
Acute kidney injury (AKI) is a common critical illness in clinical practice, with complex etiologies, acute onset, and rapid progression. It not only significantly increases the mortality rate of patients, but also may progress to chronic kidney disease. Currently, its incidence remains high, and improving early diagnosis rate and treatment efficacy is a major clinical challenge. Artificial intelligence (AI), with its powerful data processing and analysis capabilities, is developing rapidly in medical field, providing new ideas for disease diagnosis and treatment, and showing great potential in revolutionizing the early diagnosis, condition assessment, and treatment decision-making models in the AKI field. This article will review the application progress of AI in AKI prediction, condition assessment, and treatment decision-making, so as to provide references for clinicians and promote the further application and development of AI in the AKI field.
UK Biobank is an extensive biomedical database and research resource. It contains in-depth genetic and health information from 500 000 UK subjects, comprising a wealth of basic structured data, high-throughput genomic and genetic data, and multimodal imaging data. However, difficulties in accessing the large amount of data mean that the database has not been widely used in China. We first introduced the health-related structural data, genetic data, and imaging data in the UK Biobank. We then described methods for using different types of data downloaded from UK Biobank, and explored recent research based on these data. We also discussed classic research focusing on applying artificial intelligence technology to UK Biobank data. Finally, we predicted future research trends in the utilization of UK Biobank data in areas such as anatomy, physiology, genetic variation, and phenotypic characteristics.
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 develop a neural network architecture based on deep learning to assist knee CT images automatic segmentation, and validate its accuracy. Methods A knee CT scans database was established, and the bony structure was manually annotated. A deep learning neural network architecture was developed independently, and the labeled database was used to train and test the neural network. Metrics of Dice coefficient, average surface distance (ASD), and Hausdorff distance (HD) were calculated to evaluate the accuracy of the neural network. The time of automatic segmentation and manual segmentation was compared. Five orthopedic experts were invited to score the automatic and manual segmentation results using Likert scale and the scores of the two methods were compared. Results The automatic segmentation achieved a high accuracy. The Dice coefficient, ASD, and HD of the femur were 0.953±0.037, (0.076±0.048) mm, and (3.101±0.726) mm, respectively; and those of the tibia were 0.950±0.092, (0.083±0.101) mm, and (2.984±0.740) mm, respectively. The time of automatic segmentation was significantly shorter than that of manual segmentation [(2.46±0.45) minutes vs. (64.73±17.07) minutes; t=36.474, P<0.001). The clinical scores of the femur were 4.3±0.3 in the automatic segmentation group and 4.4±0.2 in the manual segmentation group, and the scores of the tibia were 4.5±0.2 and 4.5±0.3, respectively. There was no significant difference between the two groups (t=1.753, P=0.085; t=0.318, P=0.752). Conclusion The automatic segmentation of knee CT images based on deep learning has high accuracy and can achieve rapid segmentation and three-dimensional reconstruction. This method will promote the development of new technology-assisted techniques in total knee arthroplasty.
The technical combination of artificial intelligence (AI) and thoracic surgery is increasingly close, especially in the field of image recognition and pathology diagnosis. Additionally, robotic surgery, as a representative of high-end technology in minimally invasive surgery is flourishing. What progress has been or will be made in robotic surgery in the era of AI? This article aims to summarize the application status of AI in thoracic surgery and progress in robotic surgery, and looks ahead the future.
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.
The development of the fifth generation mobile networks (5G) technology has brought great breakthroughs and challenges to clinical medicine and medical education. In the context of “5G + medicine”, the development of telemedicine, emergency rescue, intelligent analysis and diagnosis has opened up new horizons for clinical medicine. Facing the constant impact of high technology, the focus of medical education should be on the cultivation of students’ integrated medical view, critical thinking, communication abilities and skills, and creativity. The “5G + education” model will be presented by means of virtual reality, artificial intelligence, cloud computing and other technologies, providing a new direction for the development of medical education. This article summarizes the key points and prospects of medical education under 5G technology in order to provide a reference for the field of medical education to adapt to the changes in the 5G era.
ObjectiveTo evaluate the quality differences in recommendations generated by large language models (LLM) and clinical practitioners for sarcopenia-related questions. MethodsA sarcopenia knowledge base was constructed based on the latest domestic and international research and consensus guidelines. Using the Python environment, a locally deployed and sarcopenia-focused hybrid vertical LLM (referred to as LC) was implemented via LangChain-LLM. Eight fixed questions covering etiology, diagnosis, and prevention were selected, along with eight virtual patient cases. The evaluation team assessed the quality of answers generated by LC and written by clinical practitioners. Quantitative analysis was performed on the precision, recall, and F1 scores (harmonic mean of precision and recall) of treatment recommendations. ResultsThe responses were generally perceived as "possibly written by humans or AI", with a stronger inclination toward being AI-generated, although the accuracy of such judgments was low. Regarding answer quality attributes, LC's responses were superior to those of clinical practitioners in guideline consistency (P<0.01), exhibited similar acceptability (P>0.05), showed better practicality (P<0.05), and had a lower proportion of "1–2 errors" (P<0.05). Quantitative analysis of treatment recommendations indicated that LC and GPT-4.0 outperformed clinical practitioners in recall and F1 scores (P<0.05), with minimal differences between LC and GPT-4.0. ConclusionThe locally deployed sarcopenia-focused hybrid vertical LLM demonstrates high accuracy and applicability in addressing sarcopenia-related issues, outperforming clinical practitioners and exhibiting strong clinical decision-support capabilities.
The rapid development of medical imaging methods based on artificial intelligence (AI) has led to the first release of the AI medical imaging research checklist (CLAIM) in 2020 to promote the completeness and consistency of AI medical imaging research reports. However, during the application process, it was found that some entries in CLAIM needed improvement. Therefore, the expert committee updated CLAIM and released the updated version of CLAIM 2024. This article introduces CLAIM 2024 for domestic scholars to follow up and refer to in a timely manner.