ABSTRACT
Objective:To explore the current status of the artificial intelligence (AI) developments in medical imaging in China, and to provide data for the development of AI.Methods:In May 2022, the Radiology Branch of the Chinese Medical Association and the China Medical Imaging AI Industry-University-Research Innovation Alliance jointly launched a nationwide survey on the application status and development needs of medical imaging AI in China in the form of a questionnaire. This survey was carried out for different groups of people, focusing on the clinical applications of medical imaging AI, enterprise development, and educational needs in colleges and universities, with the descriptive statistical analysis performed.Results:China′s medical imaging AI has made great progress in clinical applications, in enterprise developments, as well as in the education and teaching areas. In terms of clinical application, 90.8% (5 765/6 347) of the survey respondents had a preliminary understanding of AI. There were 62.1% (3 798/6 119) doctors confirmed the applications medical imaging AI products in their departments. AI products were applied in the whole process of medical imaging examination, especially in assistance of the diagnosis. The application of pulmonary nodules screening accounted for 89.5% (3 401/3 798) of all medical imaging AIs. The main factors restricting the rapid development of medical imaging AI included lack of experts [47.3% (3 002/6 347)], poor data quality [45.7% (2 898/6 347)] and imperfect function of the products [40.4% (2 566/6 347)]; in terms of enterprises, there were 65.4% enterprises with a scale of less than 100 employees (17/26), and 34.6% with a scale of more than 100 employees (9/26). The main group of the customers were the hospitals above the second level, accounting for about 92.3% (24/26); in terms of education, the number and quality of AI courses, practical operations and lectures currently carried out by schools vary between different levels. The AI courses for graduated students accounted for about 22.5% (86/381), which were the largest in number; while the proportion of AI courses for junior college students, undergraduates and regular trainees were less than 15%. More than 60% of the students thought it necessary for schools to establish AI courses. Among all the students, the master′s and doctoral candidates had the greatest demand for additional AI courses [84.8% (323/381)].Conclusions:The development and popularization of medical imaging AI in China continues to prosper, with opportunities and challenges coexisting. It is necessary to adhere to the orientation of clinical needs, and to realize the coordinated development of clinical application, enterprise development, as well as education and teaching.
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Objective:To explore the value of a deep learning model based on MRI in predicting the methylation status of MGMT in WHO Ⅱ, Ⅲ gliomas.Methods:The clinical and imaging data of 121 patients with WHO grade Ⅱ, Ⅲ glioma confirmed by surgical pathology and molecular pathology in the Second Hospital of Lanzhou University from June 2016 to June 2020 were retrospectively analyzed. Among them, the MGMT promoter was methylated. A total of 78 cases were metabolized and 43 cases were unmethylated. T 2WI and T 1WI enhanced sequence images of 121 cases of WHO Ⅱ, Ⅲ gliomas were collected, and all the images of each patient including the lesion level were selected manually, and were randomly divided into training set and validation set according to 7∶3. The EfficientNet-B3 convolutional neural network was used to build independent prediction models (T 2-net, T 1C-net, TS-net) based on T 2WI, T 1WI enhancement, T 2WI combined with T 1WI enhancement, and the prediction performance of each model was evaluated separately through the ROC curve. Results:The T 2-net model in the validation set presented an accuracy of 72.3%, a sensitivity of 64.7%, a specificity of 73.3%, and an area under the curve (AUC) of 0.72 for predicting the methylation status of the MGMT promoter in WHO Ⅱ, Ⅲ gliomas. The T 1C-net model showed an accuracy of 66.8%, a sensitivity of 68.3%, a specificity of 66.9%, and an AUC of 0.72. The TS-net model showed an accuracy of 81.8%, a sensitivity of 63.1%, a specificity of 85.0%, and AUC of 0.78. Conclusions:The EfficientNet-B3 convolutional neural network based on MRI can predict the methylation status of the MGMT promoter of WHO Ⅱ, Ⅲ gliomas; the TS-net model has the best prediction performance.
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In recent years, with the continuous development of artificial intelligence related technologies, deep learning (DL) technology kept improving and had become a research hotspot in medical field. Research of DL in medical imaging has brought a new development direction for precise diagnosis, individualized treatment and prognosis evaluation of brain neoplasms. The current status and future development of DL in brain neoplasms medical images were reviewed in this paper.