Jiancheng Dong

Telemedicine for detecting diabetic retinopathy: a systematic review and meta-analysis

Objective To determine the diagnostic accuracy of telemedicine in various clinical levels of diabetic retinopathy (DR) and diabetic macular oedema (DME). Methods PubMed, EMBASE and Cochrane databases were searched for telemedicine and DR. The methodological quality of included studies was evaluated using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS-2). Measures of sensitivity, specificity and other variables were pooled using a random effects model. Summary receiver operating characteristic curves were used to estimate overall test performance. Meta-regression and subgroup analyses were used to identify sources of heterogeneity. Publication bias was evaluated using Stata V.12.0. Results Twenty articles involving 1960 participants were included. Pooled sensitivity of telemedicine exceeded 80% in detecting the absence of DR, low- or high-risk proliferative diabetic retinopathy (PDR), it exceeded 70% in detecting mild or moderate non-proliferative diabetic retinopathy (NPDR), DME and clinically significant macular oedema (CSME) and was 53% (95% CI 45% to 62%) in detecting severe NPDR. Pooled specificity of telemedicine exceeded 90%, except in the detection of mild NPDR which reached 89% (95% CI 88% to 91%). Diagnostic accuracy was higher with digital images obtained through mydriasis than through non-mydriasis, and was highest when a wide angle (100–200°) was used compared with a narrower angle (45–60°, 30° or 35°) in detecting the absence of DR and the presence of mild NPDR. No potential publication bias was detected. Conclusions The diagnostic accuracy of telemedicine using digital imaging in DR is overall high. It can be used widely for DR screening. Telemedicine based on the digital imaging technique that combines mydriasis with a wide angle field (100–200°) is the best choice in detecting the absence of DR and the presence of mild NPDR.

Prevalence of type 2 diabetes mellitus among inland residents in China (2000–2014): A meta-analysis

Besides the aging population in China, the following have become serious public health problems: increasing urban population, lifestyle changes and diabetes. We assessed the epidemiology of type 2 diabetes mellitus in China between 2000 and 2014, and analyzed time trends to better determine the prevalence status of diabetes in China and to provide a basis for prevention and decision‐making.

Mapping Knowledge Domain Analysis of Medical Informatics Education

This research analyzes the mapping knowledge domain of the medical informatics education through the program CiteSpaceII. The data sample is downloaded from the SCI-E. The research takes the words that related to the field of the medical informatics education as the search terms, articles are searched from the Web of Science search engine. Through the visualization of the downloaded articles using CiteSpaceII, the research frontiers, countries (regions) in the area medical informatics research and education are found. At last, in order to improve the development of the medical informatics education, several suggestions are given.

Negation Detection in Chinese Electronic Medical Record Based on Rules and Word Co-occurrence

In order to extract negative terminologies in Chinese Electronic Record. Many methods have been developed. One popular and simple method is based on rules. However, the negative predictive value drops significant if the sentence contains several kinds of punctuation. In our research, a new method is used to solve the problem. The new method combines rules with word co-occurrence. In the experiments, 200 medical texts including 150,865 Chinese characters are used to test the new method. The negative predictive value is 99.85 %, which is 7.85 % higher than the rule-based method. That is to say, this method can tolerate various kinds of punctuations existing in the sentences. Therefore, the value of false-positive probability drops obviously.

The Development of a Smart Personalized Evidence Based Medicine Diabetes Risk Factor Calculator

Type 2 diabetes mellitus (T2DM) is a chronic disease affected with complex risk factors and has been regarded as one of the major social burdens due to its high occurrence. In this study, we aim to incorporate the idea of evidence based medicine (EBM) into our diabetes risk factor App development. We acquired and extracted the relative risk of different risk factors from relevant literature by searching academic databases. A total of 19 items of risk factors in our daily lives has been finally selected. To design App graphic interface, a total of three pages were designed to let user answer the questions and show the results of their level or risk to have T2DM. We validated the feasibility of our App in 100 users and the results were promising. Therefore, the personalized EBM diabetes risk factor calculator might be a feasible approach to remind those T2DM risky populations by revealing their potential risk factors, thus making implementation of personalized and prevention medicine achievable at hand.

The development of an ophthalmologic imaging CADe structured report for retinal image radiomics research

Retinal changes on a fundus image have been found to be related to a series of diseases. The traditional retinal image quantitative features are usually collected by various standalone and proprietary software which results in variabilities in feature extraction and data collection. Based on our previously established web-based imaging informatics platform to view DICOMized and de-identified fundus images, we developed a computer aided detection structured report (CADe SR) to capture some of the quantitative features on fundus images such as arteriole/venule diameter ratio, cup/disc diameter ratio and to record several lesions such as aneurysms, hemorrhages, neovascularization and exudates into different regions based on known research and clinically related templates such as Early Treatment Diabetic Retinopathy Study (ETDRS) 9 Region Map and four Region Map. In this way, the location patterns of the above lesions as well as morphological changes of anatomy structures could be saved in SR for further radiomics research. In addition, an on-line consultation tool was developed to facilitate further discussion among clinicians and researchers regarding any uncertainty of measurements. Compared with the present workflow of utilizing standalone software to obtain quantitative results, qualitative and quantitative data was acquired by the CADe SR directly, which will provide researchers and clinicians the ability to capture findings and will foster future image-based knowledge discovery researches.

iT2DMS: a Standard-Based Diabetic Disease Data Repository and its Pilot Experiment on Diabetic Retinopathy Phenotyping and Examination Results Integration

Type 2 diabetes mellitus (T2DM) is a common chronic disease, and the fragment data collected through separated vendors makes continuous management of DM patients difficult. The lack of standard of fragment data from those diabetic patients also makes the further potential phenotyping based on the diabetic data difficult. Traditional T2DM data repository only supports data collection from T2DM patients, lack of phenotyping ability and relied on standalone database design, limiting the secondary usage of these valuable data. To solve these issues, we proposed a novel T2DM data repository framework, which was based on standards. This repository can integrate data from various sources. It would be used as a standardized record for further data transfer as well as integration. Phenotyping was conducted based on clinical guidelines with KNIME workflow. To evaluate the phenotyping performance of the proposed system, data was collected from local community by healthcare providers and was then tested using algorithms. The results indicated that the proposed system could detect DR cases with an average accuracy of about 82.8%. Furthermore, these results had the promising potential of addressing fragmented data. The proposed system has integrating and phenotyping abilities, which could be used for diabetes research in future studies.

The design and integration of retinal CAD-SR to diabetes patient ePR system

Diabetic retinopathy (DR) is one of the serious complications of diabetes that could lead to blindness. Digital fundus camera is often used to detect retinal changes but the diagnosis relies too much on ophthalmologist’s experience. Based on our previously developed algorithms for quantifying retinal vessels and lesions, we developed a computer aided detection-structured report (CAD-SR) template and implemented it into picture archiving and communication system (PACS). Furthermore, we mapped our CAD-SR into HL7 CDA to integrate CAD findings into diabetes patient electronic patient record (ePR) system. Such integration could provide more quantitative features from fundus image into ePR system, which is valuable for further data mining researches.

Extract Examining Data Using Medical Field Association Knowledge Base

The electronical medical record incorporate a significant amount of information, which is useful for medical study. The examining data is the results of patients’ inspection. In order to extract examining data from huge amount electronic medical record. A new method is utilized in our research. The start point of the research is the whole process that human recognize the examining data in the text. The presented method takes use of medical field association knowledge. In the experiment, the value of recall and precision is 81 and 83 % respectively. The satisfied experiment values prove that the presented new method can avoid weak point exiting in the traditional methods, at the same time, can extract the examing data efficiently.

Biological fiducial point based registration for multiple brain tissues reconstructed from different imaging modalities

With the development of computer aided navigation system, more and more tissues shall be reconstructed to provide more useful information for surgical pathway planning. In this study, we aimed to propose a registration framework for different reconstructed tissues from multi-modalities based on some fiducial points on lateral ventricles. A male patient with brain lesion was admitted and his brain scans were performed by different modalities. Then, the different brain tissues were segmented in different modality with relevant suitable algorithms. Marching cubes were calculated for three dimensional reconstructions, and then the rendered tissues were imported to a common coordinate system for registration. Four pairs of fiducial markers were selected to calculate the rotation and translation matrix using least-square measure method. The registration results were satisfied in a glioblastoma surgery planning as it provides the spatial relationship between tumors and surrounding fibers as well as vessels. Hence, our framework is of potential value for clinicians to plan surgery.