Objective To explore the clinical value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid （Gd-EOB-DTPA） enhanced magnetic resonance （MR） imaging for cirrhosis-related nodules. Methods Nineteen patients who were suspected cirrhosis with lesions of liver were prospectively included for Gd-EOB-DTPA enhanced MR imaging test between Nov. 2011 and Jan. 2013. The hepatobiliary phase （HBP） images were taken in 20 minutes after agents’ injection. The images were diagnosed independently in two groups： group A， including the plain phase and dynamic phase images； group B， including plain phase， dynamic phase， and HBP phase images. The signal intensity （SI） of lesions in HBP images， background liver SI， and background noise standard deviation were measured by using a circular region of interest， then the lesion signal to noise ratio （SNR） and contrast signal to noise ratio （CNR） were calculated. Results Nineteen patients had 25 tumors in all， including 18 hepatocelluar carcinoma （HCC） and 7 regenerative nodule （RN） or dysplastic nodule （DN）， with the diameter ranged from 0.6 cm to 3.2 cm （average 1.3 cm） . Sixteen HCC manifested hypo SI relative to the normal liver， while 2 HCC manifested hyper SI at HBP. Five HCC had cystic necrosis with the necrotic area， and there were no enhancement in artery phase， while performed flocculent enhancement at HBP. Six RN or DN showed hyper SI while another 1 showed iso SI to background liver at HBP. The diagnostic accuracy rates of group A and group B were 80.0% （20/25） and 92.0% （23/25）. SNR of RN or DN at HBP was 132.90±17.21， and of HCC was 114.35±19.27， while the CNR of RN or DN was 19.47±8.20， and of HCC was 112.15±33.52. Conclusion Gd-EOB-DTPA enhanced MR imaging can improve the diagnosis capacity of cirrhosis-related nodules， so as to develop more accurate and reasonable treatment options.
Integrating visualization toolkit and the capability of interaction, bidirectional communication and graphics rendering which provided by HTML5, we explored and experimented on the feasibility of remote medical image reconstruction and interaction in pure Web. We prompted server-centric method which did not need to download the big medical data to local connections and avoided considering network transmission pressure and the three-dimensional (3D) rendering capability of client hardware. The method integrated remote medical image reconstruction and interaction into Web seamlessly, which was applicable to lower-end computers and mobile devices. Finally, we tested this method in the Internet and achieved real-time effects. This Web-based 3D reconstruction and interaction method, which crosses over internet terminals and performance limited devices, may be useful for remote medical assistant.
Currently, the medical imaging methods based on artificial intelligence are developing rapidly, and the related literature reports are increasing year by year. However, there is no special reporting standard, and the reporting of the results is not standardized. In order to improve the report quality of this kind of research and help readers and evaluators evaluate the quality of this kind of research more scientifically, a checklist for artificial intelligence in medical imaging (CLAIM) was put forward abroad. This paper introduces the content of CLAIM and explains its items.
With the change of medical diagnosis and treatment mode, the quality of medical image directly affects the diagnosis and treatment of the disease for doctors. Therefore, realization of intelligent image quality control by computer will have a greater auxiliary effect on the radiographer’s filming work. In this paper, the research methods and applications of image segmentation model and image classification model in the field of deep learning and traditional image processing algorithm applied to medical image quality evaluation are described. The results demonstrate that deep learning algorithm is more accurate and efficient than the traditional image processing algorithm in the effective training of medical image big data, which explains the broad application prospect of deep learning in the medical field. This paper developed a set of intelligent quality control system for auxiliary filming, and successfully applied it to the Radiology Department of West China Hospital and other city and county hospitals, which effectively verified the feasibility and stability of the quality control system.
Since January 2020, due to the epidemic of coronavirus disease 2019, all universities in China have postponed their studies or even suspend their studies. In response to the teaching policy of “suspending class, but keeping teaching and learning” , college teachers have rapidly changed into online teaching mode. However, how to ensure the quality and effect of online teaching still needs further exploration. Through analyzing the course characteristics of medical imaging diagnostics and students’ learning situations, this study discusses how to design detailed online teaching projects and improve the teaching quality and how to select online software suitable for the course. A questionnaire survey was conducted to evaluate the effect of online teaching during the spring course in 2020, selecting a total of 297 clinical and other undergraduate students of grade 2017 from West China School of Medicine of Sichuan University. The results showed that the detailed online teaching programs including “video learning” “distance teaching” “periodic examination” “weakness tutorial” were helpful to the learning process agreed by the majority of students. During the epidemic period, online teaching method can help students master the content of medical imaging diagnosis. In the era of Internet, the “online+offline” teaching mode is expected to be popularized in the future.
China is one of the countries in the world with the highest rate of esophageal cancer. Early detection, accurate diagnosis, and treatment of esophageal cancer are critical for improving patients’ prognosis and survival. Machine learning technology has become widely used in cancer, which is benefited from the accumulation of medical images and advancement of artificial intelligence technology. Therefore, the learning model, image type, data type and application efficiency of current machine learning technology in esophageal cancer are summarized in this review. The major challenges are identified, and solutions are proposed in medical image machine learning for esophageal cancer. Machine learning's potential future directions in esophageal cancer diagnosis and treatment are discussed, with a focus on the possibility of establishing a link between medical images and molecular mechanisms. The general rules of machine learning application in the medical field are summarized and forecasted on this foundation. By drawing on the advanced achievements of machine learning in other cancers and focusing on interdisciplinary cooperation, esophageal cancer research will be effectively promoted.
ObjectiveTo systematically review the efficacy of the application of team-based learning (TBL) pedagogy and traditional lecture-based learning (LBL) pedagogy in radiology education.MethodsPubMed, EMbase, Web of Science, WanFang Data, CNKI and VIP databases were electronically searched to collect randomized controlled trials (RCTs) of the application of TBL and LBL pedagogy in radiology education from inception to March 31st, 2020. Two reviewers independently screened literature, extracted data, and assessed the risk of bias of included studies; meta-analysis was then performed by using Stata/SE 16.0 software.ResultsA total of 11 RCTs involving 721 participants were included. The results of meta-analysis showed that TBL significantly improved students’ theoretical assessment scores (SMD=1.70, 95%CI 1.05 to 2.36, P<0.001), practical assessment scores (SMD=2.00, 95%CI 1.02 to 2.98, P<0.001), preference to the curriculum design (RR=1.53, 95%CI 1.19 to 1.97, P=0.001), agreed to more effective promotion in aspects of teamwork ability (RR=2.46, 95%CI 1.69 to 3.59, P<0.001), self-directed learning ability (RR=2.41, 95%CI 1.33 to 4.39, P=0.004), and clinical practice ability (RR=2.09, 95%CI 1.46 to 3.00, P<0.001) compared with LBL pedagogical method. However, no significant difference was found in the subjective evaluation of theoretical knowledge between two pedagogies.ConclusionsCurrent evidence shows that TBL pedagogy based on active learning and team cooperation has obvious advantages over traditional LBL mode in radiology education. Due to limited quality and quantity of the included studies, more high-quality studies are needed to verify above conclusions.
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.
In the field of artificial intelligence (AI) medical imaging, data annotation is a key factor in all AI development. In the traditional manual annotation process, there are prominent problems such as difficult data acquisition, high manual labor intensity, strong professionalism and low labeling quality. Therefore, an intelligent multimodal medical image annotation system is urgently needed to meet the requirements of labeling. Based on the image cloud, West China Hospital of Sichuan University collected the multimodal image data of hospital and allied hospitals, and designed a multi-modal image annotation system through information technology, which integrated various image processing algorithms and AI models to simplify the image data annotation. With the construction of annotation system, the efficiency of data labeling in the hospitals is improved, which provides necessary data support for the AI image research and related industry construction in the hospital, so as to promote the implementation of artificial intelligence industry related to medical images in the hospital.